A Tale of Two Structures: Do LLMs Capture the Fractal Complexity of Language?
Authors: Ibrahim Alabdulmohsin
Andreas Peter Steiner
Venue: ICML 2025 poster (2025)
Keywords: large language models
fractals
Hurst exponent
Holder exponent
self-similarity
detection
decoding
instruction-tuning
prompting
Primary area: deep_learning->large_language_models
TL;DR: We study the conditions for when LLMs can successfully replicate the fractal structure of language and relate this to the quality of output. We also release a dataset.

Abstract: Language exhibits a fractal structure in its information-theoretic complexity (i.e. bits per token), with self-similarity across scales and long-range dependence (LRD). In this work, we investigate whether large language models (LLMs) can replicate such fractal characteristics and identify conditions-such as temperature setting and prompting method-under which they may fail. Moreover, we find that the fractal parameters observed in natural language are contained within a narrow range, whereas those of LLMs' output vary widely, suggesting that fractal parameters might prove helpful in detecting a non-trivial portion of LLM-generated texts. Notably, these findings, and many others reported in this work, are robust to the choice of the architecture; e.g. Gemini 1.0 Pro, Mistral-7B and Gemma-2B. We also release a dataset comprising over 240,000 articles generated by various LLMs (both pretrained and instruction-tuned) with different decoding temperatures and prompting methods, along with their corresponding human-generated texts. We hope that this work highlights the complex interplay between fractal properties, prompting, and statistical mimicry in LLMs, offering insights for generating, evaluating and detecting synthetic texts.

A Tale of Two Structures:
Do LLMs Capture the Fractal Complexity of Language?
Ibrahim Alabdulmobsin Andreas Steiner'
Abstract
is found in Ethernet traffic (Crovella & Bestavros, 1995;
Leland et al., 1994; Paxson & Floyd, 1995; Willinger et al., 1997), where self-similarity manifests burstiness across all time scales, thereby impacting the design of network device buffers (Leland & Wilson, 1991). In language, such self-similarity is attributed to its recursive structure (Alt- mann et al., 2012). On the other hand, a stochastic process is called long-range dependent (LRD) its future is influ- enced by the distant past, with no particular characteristic context length.
Language exhibits fractal structure in its information-theoreit complexity (i.e. bits per token), with self-similarity across scales and long- range dependence (LRD). In this work, we in- vestigate whether large language models (LLMs) can replicate such fractal characteristics and iden- tify conditions-suce as temperature setting and prompting method-under which they may fail. Moreover, we find that the fractal parameters ob- served in natural language are contained within a narrow range, whereas those of LLMs' output vary widely, suggesting that fractal parameters might prove helpful in detecting a non-trivial por- tion of --geerrrete texts. Notably, these find- ings, and many others reported in this work, are robust to the choice of the architecture; e.g. Gem- ini 1.0 Pro. Mistral-7B and Gemma-2B. We also release a dataset comprising over 240,000 articles generated by various LLMS (both pretrained and instruction-tundn tuned) with different decoding temper- atures and prompting methods, along with their corresponding numan-generater texts. We hope that this work highlights the complex interplay be- tween fractal properties. prompting, and statistical mimicry in LLMs, offering insights for generat- ing, evaluating and detecting synthetic texts.
Self-similarity and long-range dependence (LRD) can be quantified using the Hölder and Hurst exponents, respec- tively. The Hölder exponent, which we denote by S for self- similarity, characterizes the rate of decay in the autocorrela- tion function, with smaller values of S indicating a more sig- nificant self-similar structure (heavier tail) (Watkins, 2019). By contrast, larger values of the Hurst exponent H > 0.5 indicate more dependence across time (Hurst, 1951). We refer the reader to Appendix B for the exact definitions of these quantities. A natural question that arises, next, is: How different are and H in --neerreertted texts from nat- ural language? In this work, we investigate this question in depth, aiming to identify conditions- succh as temperature settings, instruction-uuiin, prompting and model size- that influence an LLM's ability to replicate such fractal charac- teristics.
Before doing that, however, let us consider some arguments for why LLMs may or may not be capable of replicating the fractal structure of language. One argument for why they should be capable of doing so lies in the chain rule of probability. LLMs are reasonably calibrated at the token level (Kadavath et al., 2022), implying that auto-regressive decoding should theoretically capture the structure of natu- ral language as long as token-level probability scores remain well-calibrated By the chain rule, each subsequent token't 's probability is conditioned on the prior tokens, and this pro- cess should ideally reflect the self-similarity and long-range dependence inherent language.
1. Introduction
The information-theoreiicc complexity of language (i.e., its bits or "surprise") has been shown to exhibit both self- similarity and long-range dependence (LRD) (Alabdul- mohsin et al., 2024). In simple terms, a stochastic process is considered self-similar if its statistical properties remain con- sistent across different scales, regardless of the level of mag- nification applied. A well-known example of such behavior
'Google Deepmind, Zürich, Switzerland. Correspondence to: Ibrahim Alabdulmohsin <ibomohsin @ google.com>.
However, this idealized view may not hold in practice. A critical issue can arise, for example, from the mismatch between how LLMS are trained and how they are used at inference, as pointed out by (Bachmann & Nagarajan, 2024).
Proceedings of the ndnd International Conference Machine Learning. Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

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A Tale of Two Structures: Do LLMs Capture the Fractal Complexity of Language?
prompts (e-g. summaries ordered excerpts). Interestingly, increasing the information density in the prompt does not always improve fractal characteristics, as shown in Figure 7. In fact, the relationship for self self-similarrty seems to exhibit a "double descent", where providing a summary in the prompt generates texts that are less similar to natural language than providing either an unordered set of keywords (less infor- mation) or an ordered set of excerpts (more).
Context
Prefix
Suffix
A causal model we consider, in which a latent "context" generates a prefix and both produce a suffix.
Figure 1.
Do fractal parameters in LLL--generated texts differ substan- tially from natural language? Our findings suggest that they partially do. Specifically, the range of fractal parameters in natural language is contained with a narrow range, whereas those of LLMs' output vary widely, as demonstrated for the Hölder exponent S in Figure 10. Large values of indicate less self-similar structure (i.e. lack of rich details) so we expect LLMs fail occasionally to produce texts with small values of S. This is indeed what we observe.
During training, LLMs use teacher-forcing, where the cor- rect previous tokens are always provided. This ensures that errors do not accumulate during training, but during infer- ence, models must rely on their own predictions, leading potentially to compounding errors that can distort the frac- tal structure. Formally speaking, whereas LLMs can be well-calibrated at the nex/ token level, their ability to ac- curately predict the distribution over longer sequences of tokens might degrade during inference.
To conduct our study, we build a dataset comprising of over 240,000 LLM-generated articles. These differ by the model that generated them, the contextual information provided in the prompt, the decoding temperature, and the data domain (e.g. science, news, etc). To facilitate research in domains, such as detecting LLM-generated contents, we make this dataset public. The data card is provided in Appendix E and samples from the data are shown in Appendix G. In addi- tion, we show that our main conclusions continue to hold using the RAID dataset (Dugan et al., 2024), which contains generated texts by 11 models (e.g. ChatGPT, Cohere, Llama 2, etc) in domains such as Reddit and reviews.
Another potential challenge lies in how language is gener- ated by humans. Humans typically generate texts by first conceptualizing an underlying "contexi" and then construct- ing sentences based on it, rather than improvising one token at a time with no regard to the overall intent. This is captured by the causal model in Figure I, where a latent "context" generates a "prefix" (beginning of text), and, in conjunction with this prefix, both produce a "suffix" (continuation).
Formally, under this hypothetical causal model, prompting corresponds to an interventional (or "do"" query. Let V be finite vocabulary of tokens, where texts correspond to finite sequences x E V.N. Now, divide x into a prefix - and a suffix N. Assuming for simplicity that the set of possible latent contexts is finite, one classical result in causal inference states that the "interventional" distribution is given by (see Equation 4.5 in (Neal, 2020)):
Statement of Contribution. In summary, we:
1. provide a comprehensive analysis of how various factors-such as decoding temperatures, instruction- tuning and model size fize-affeet the ability of LLMs to replicate the fractal properties of natural language.
p (suf do(pre))
Σ cc (suf pre, c). (1) CEC
2. investigate how prompting affects the fractal structure of texts, showing evidence for a non-monotone behav- ior. We demonstrate that the range of fractal parameters in natural language is much narrower than in LLM's. ss This shows that fractal parameters might prove useful in identifying some (but not all) synthetic texts. We also connect the differences in fractal parameters to the quality of the texts.
language model, by contrast, learns the "conditional" dis- tribution, which by marginalization is:
p (suf pre)= Ep(e=cl pre) p (suf pre, c c= c). (2)
cEC
We provide an example that illustrates such differences in Appendix D. Under this hypothesis, LLMs may struggle to fully replicate the fractal nature of human language because the conditional distribution uses p (c c prefix) instead of the marginal p (c = c). which should be used if prompt- ing corresponds to an intervention. To account for this, we investigate the impact of availing various amounts of contex- tual information in the prompt. These prompting strategies range from minimal cues (e.g. few keywords) to detailed
3. show these results hold across a variety of model archi- tectures, demonstrating the generality of our findings and making them relevant for diverse LLM families.
4. release dataset containing over 240,000 articles gener- ated by various LLMs (both pretrained and instruction- tuned) across various settings (c.g. data domain and temperature).
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A Tale of Two Structures: Do LLMs Capture the Fractal Complexity of Language?
2. Related Works
Fagni et al., 2021). While detecting such content has in- herent limitations (Varshney al., 2020; Sadasivan et al., 2024)-as LLMs are trained to model the full joint distribu- tion of human essential for mitigating societal risks. These include, but are not limited to, the spread of misinformation (Zellers et al., 2020), fake online reviews (Yao et al., 2017), potentially harmful medical advice (Guo et al., 2023), academic dishon- esty (Susnjak, 2022), and extremist propaganda (McGuffie & Newhouse, 2020). The importance of these efforts is underscored by incidents where LLM--gnerated news ar- ticles containing factual inaccuracies were published with minimal human oversight (Christian, 2023). We hope our work will help along these directions.
Several studies have looked into the statistical properties of LLL--eeerated texts. For instance. (Guo al., 2023) analyzed ChatGPT's responses in comparison to human- generated text and found that ChatGPT-prooucce outputs tend to have lower log-perplexity scores. Based on these findings, the authors developed detection systems for identi- fying LLM-generated content, concluding that short texts are more challenging to detect than longer documents. Sim- ilarly, Tulchinskii et al. (2023) propose using the intrinsic dimension of the data manifold as a metric that distinguishes natural language from LLM--eenrran texts.
progress in this area is
The observation that LL --geerrrreeed texts exhibit lower log- perplexity scores has been utilized by several other works for detecting such content, including (Solaiman et al., 2019; Gehrmann et al., 2019; Ippolito et al., 2020; Vasilatos et al., 2023) and (Yang et al., 2023), among others. This raises the question of which models are most suitable for evalu- ating text perplexity. In (Mireshghallah et al., 2024), the authors study this issue and suggest that smaller models are more effective! Additionally, (Hans et al., 2024) proposed normalizing the average log-perplexity score using a cross- PPL metric calculated across two different models before applying a global detection threshold.
In this work, we examine the impact of various factors, including the model size. Prior literature has consistently shown that larger models tend to perform better, with the benefits of scaling being predictable empirically (Hestness et al., 2017; Kaplan et al., 2020; Alabdulmohsin et al., 2022; Zhai et al., 2022). These improvements are not limited to perplexity scores. For example, (Dou et al., 2022) found that larger models produce texts with fewer factual and coherence-related issues. Not surprisingly, we also find that bigger pretrained models yield fractal parameters that are closer to those of natural language (see Figure 3).
However, such approaches, which are based on 1st-order statistics of log-perplexity scores, are not always effec- tive (Hans et al., 2024). For instance, methods, such as DetectGPT, which incorporate -oder information by es- timating the curvature of the loss (Mitchell et al., 2023), have been found to yield better results.
Additionally, we investigate other factors, such as tempera- ture settings. Previous research has demonstrated that while improved decoding methods may deceive humans, they may also introduce detectable statistical anomalies (Ippolito et al., 2020). Our work explores these effects in detail from the lens of fractals, contributing to the broader understanding of how model parameters influence LLM output.
Our work contributes to this line of research by focusing pri- marily on fractal parameters. As argued in (Meister & Cot- terell, 2021), the evaluation of LLMs should go beyond log- perplexity and also consider how well LLMs capture other "statistical tendencies" observed in natural language. Our study has a similar goal. We conduct a comprehensive quan- titative analysis involving a range of model architectures, decoding temperatures, and prompting methods, and we explore the differences between pretrained and instruction- tuned models as well, among other considerations.
3. Experimental Setup and Dataset
Our goal is to investigate when and how LLM- LLM-generated texts can vary substantially from natural language in their fractal structure. For that we need to generate synthetie texts. In order to correctly identify the impact of each factor we consider in our study (e.g. temperature setting, prompting, model size), we generate the data ourselves from scratch. We follow a similar setup to the one used in (Verma et al., 2024), in which we restrict analysis to long documents or paragraphs, as opposed short answers questions. Similar to (Verma et al., 2024), we query a capable LLM via its API, which is always Gemini 1.0 Pro (Anil et al., 2024) in our experiments, to generate some contextual in- formation about an article (such as keywords or summary) before asking another model to write an article based on this contextual information. Since each article matched with a corresponding human-generated text (ground truth), we name this dataset "Generated And Grounded Language
Although our study may have implications for detecting LLM-generated content, detection is not the primary focus of this work. It is important to acknowledge, however, that detecting --gnerrrete content is critical and has been the subject of several noteworthy studies. These include the perplexity- detection methods mentioned above, as well as supervised classification approaches (Verma et al., 2024; Pu et al., 2022; Jawahar et al., 2020; Ghosal et al.,
2023; Tang et al., 2024; Dhaini et al., 2023; Guo et al.,
2023), with fine-tuning of pretrained models being espe- cially effective (Zellers et al., 2020; Solaiman et al., 2019;
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A Tale of Two Structures: Do LLMs Capture the Fractal Complexity of Language?
Finally, once all the log-perplexity scores are calculated, we compute fractal parameters. Because S and H are exponents of power laws, sufficiently long documents are required. Hence, we encourage the model to generate long documents in the prompt (see prompting templates in Appendix C). We drop the first 64 tokens to remove any warm-up ef- fects, and ignore documents that are less than 400 tokens in length. When a document ignored, we also ignore the corresponding ground-truth document, to remove this con- founding effect. Then, we clip all documents (both human- and I.LM-generated) to 400 tokens to have equal lengths. We estimate fractal parameters using the scales (time gaps) T E {8, 16, 32, 48, 64, 96, 128, 160, 192, 256, 320} with = 10-2: see (Alabdulmohsin et al., 2024) for details on how to calculate them. We also use bootstrapping (Efron & Tibshirani, 1994) to estimate confidence intervals by sub- sampling with replacement 10 independent samples. Fig- ure 2 illustrates quality of fit for the setting in Appendix G where samples of documents are provided.
Table 1. A summary of the contextual information used during prompting, ordered from the least informative (top) to the most (bottom). Each prompting method provides more contextual in- formation than the one above it. Keywords and summaries were generated using Gemini 1.0 Pro as discussed in Section 3. See Appendix C for prompt templates.
Abbreviation
Description
continue (cont)
Simple continuation based on a short prefix (no prompting). Ask the model to generate an outline before generating the article, using short prefix. A few, unordered keywords,
chain-of-thougha
(cot)
short keywords (kw)
keywords (kwt)
Many unordered keywords.
summary (su)
A summary of the article.
summary + keywords (su+)
Both a summary and many keywords.
excerpt (exc)
Ordered list of long excerpts from the original article.
Disclaimer. Our estimates of and H differ from those in (Alabdulmohsin et al., 2024) for two reasons. Because LLM-generated texts are short (typically of about 500 to- kens), we restrict the range of the scale term at most 320 tokens. Also, we use a slightly larger value of e because we found that smaller values in short documents lead to high variance. Both imply that our estimates are less accurate. Nonetheless, our goal is to use and H as statistical probes to compare natural texts from LLM-generated, so we use the same hyperparameters for both types of documents.
Examples" (GAGLE). It contains over 240,000 articles
The contextual information we use were chosen such that they can be ordered from the least informative to the most, as shown in Table 1. The datasets we use are from five domains: (1) WIKIPEDIA (Wikimedia, 2024), (2) BIG- PATENT, consisting of over one million records of U.S. patents (Sharma et al., 2019), (3) NEWSROOM, containing over one million news articles (Grusky et al., 2018), (4) SCIENTIFIC, a collection of research papers obtained from ArXiv and PubMed repositories (Cohan et al., 2018), and (5) BILLSUM, containing US Congressional and California state bills (Kornilova& & Eidelman, 2019). We only use, at most, 1,000 articles from each domain.
4. Detailed Analysis
Q1. How do log-perplexity scores in LLM-ggnerated documents differ from those in language? To answer this question, results are shown in Figure 4. In agreement with prior works, we observe that M-geerrrete texts have lower log- perpeleetx scores (negative values in the y axis) but not for large pretrained models when prompted using their pretraining temperature 3 1. In the latter setting, L.M-gennrated texts have similar average log- bog-perplexity score to natural language. We illustrate this using the GLTR tool (Gehrmann et al., 2019) in Figure 5. Gemma-2B is an exception, probably because it is much smaller than the rest of the models. Instruction-tuning by contrast, lowers the log-perplexity of generated texts compared to natural language even at temperature 3 1, although no prompt- ing instructions are used in Figure 4. Similar findings are also obtained using RAID dataset, as shown in Appendix A. Overall, this suggests that detection methods relying solely on log- perplexity scores, such as GLTR, may not be ade- quate for identifying contents generated by pretrained mod- els they become increasingly more capable in the future.
In our experiments, we use pretrained models (with sim- ple continuation only) and instruction-tuma models with various prompting strategies as discussed earlier. During text generation, we experiment with three decoding tem- peratures: 8 = 0 (greedy decoding), 3 = 0.5, and B = (pretraining temperature). The three models we use are Gemini 1.0 Pro (Anil et al., 2024), Mistral-7B (Jiang et al., 2023), and Gemma-2B (Mesnard et al., 2024).
Once the texts are generated, we score them using pretrained models. One goal is to identify if our findings remain robust across different scoring models. As mentioned earlier, some previous works suggest that smaller models might be better for scoring and detecting LLM-generated texts (Mireshghal- lah et al.. 2024) while other works suggest that using the same model for both generation and scoring yields better results (Fagni et al., 2021; Mitchell et al., 2023).
'The GAGLE dataset is available at: https: / //huggingface.....m/////// htwph/gfaacccc//aa////ae/e wttpa//etaaaaaaaeoeeoo wttps//tttseeoooooin//////////
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A Tale of Two Structures: Do LLMs Capture the Fractal Complexity of Language?
Natural Language (S)
billing d(O)
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12"
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Pesk Pross
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Figure 2. Quality of power law fit for fractal parameters in both human- and LLM-generated documents, for the same setting as in Appendix where samples documents are provided.
GEMINI 1.0 Pro
ing on fractal parameters when all texts are generated in both pretrained and instruction-tune models using simple continuation (no prompting). We focus on simple continu- ation here to isolate the impact of instruction-tuning alone. To recall, Figure 4 shows that instruction tuning generates texts with lower pog-peellyxtty scores than natural language. Figure 6 shows that texts generated by instruction-tus tuned models have higher values of the Hurst exponent at low temperatures 3 < 1, indicating more dependence over time. Self-similarity is not impacted, however. Similar findings are also obtained using RAID dataset, as shown in Ap- pendix A.
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Q4. What if contextual information is provided in the prompt to instruction-tune
models? As mentioned in Section 1, we also consider the causal graph shown in Fig- ure I, and examine the impact of adding various contextual cues in the prompt (see Table 1). Figure summarizes the results across all combinations of generating models, scor- ing models, and datasets. For self-similarity, we observe double descent. While the second descent is expected, given that LLMs should be eventually capable of replicat- ing the original article if its entire content is provided in the prompt, the fact that providing a sample of unordered keywords is better than a summary is surprising! We also observe that asking the model to generate an outline first, similar to thain-of--houggee (CoT) prompting (Wei al., 2023), yields fractal parameters that are closer to those of natural language than simple continuation. In addition, as shown in Appendix F, generating patent and science arti- cles seem to be more sensitive to prompting than in other domains.
G-P
M-7
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G-P
M-7
G-2
Figure 3. The y-axis is cither log S/S (left column) or log H/H (right column), where S is the Hölder exponent of ---eererrtted texts while S is of natural language. and the same holds for H and H. The z-axis are the generating models: Gemini 1.0 Pro (denoted G-P), Mistral-7B (denoted M-7), and Gemma-2B (denoted G- 2), all are pretrained models with temperature 3 = 1. Subtitles indicate the model used for scoring the texts. As expected, we observe that larger models tend to replicate the fractal properties of natural language better than smaller models. In addition, LLM- generated texts has higher values of both S (less self-similarity) and (more dependence).
Q2. If large pretrained LLMs at their pretraining tem- perature 3 = 1 can replicate the 1st-order statistics of log-perplexity scores in language, do they also replicate its fractal parameters? Figure 3 summarizes the results. We observe that larger pretrained models at temperature 3 == 1 replicate the fractal properties of natural language better, regardless of which model is used for scoring. In addition, LLM-generated texts are systematically biased towards higher values of both S (less self-similarity) and H (more dependence) than in natural language. As we will discuss later in Q6, this means they are biased towards gen- erating texts with lower quality than in natural language.
Q5. How sensitive are fractal parameters of LLM- generated articles to the contextual information pro- vided in the prompt? To answer this question, we first calculate for each of the three architectures Gemini 1.0 Pro, Mistral-7B, and Gemma-2B the average Hölder and Hurst exponents disaggregated by prompting method, where averages are calculated over all remaining variables (c.g. de- coding temperature, scoring algorithm, and dataset). Then, we plot the standard deviation calculated across the prompt- ing methods. The results are displayed in Figure 8. As
Q3. What about fractal parameters in instruction- tuned models? We examine the impact of instruction tun-
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A Tale of Two Structures: Do LLMS Capture the Fractal Complexity of Language?
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Figure 4. y-axis the log-ratio log-PPL scores for both pretrained (PT) and instruction-tuned (IT) models with simple continuation. when Gemini 1.0 Pro (left), Mistral-7B (center), and Gemma-2B (right) is used to score texts. Texts generated by large pretrained models do not have a lower log-PPI. than natural texts; instruction tuning and the use of small temperatures lead to that effect.
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than the other metrics, Similar findings are also obtained using RAID dataset, as shown in Appendix A. The reason log-perplexity not a good predictor of quality can be il- lustrated with a simple example. Suppose that the entire document comprises of a single word repeated over and over again. Then, the peg-perllextty of each subsequent token gets progressively closer to zero, since the next token can be reliably predicted. Obviously, this does not imply that an article made of a single repeating word has a good quality. The Hurst exponent, on the other hand, will be quite large in the latter case, indicating poor quality. In Appendix G, we provide a sample of documents from hy- perparameter settings that yield large and small values of H for comparison.
Figure 5. Output of the GLTR tool (Gehrmann et al., 2019) on texts generated by humans (left), Mistral-7B pretrained (middle) and Mistral-7B instruction-tuned (right) at temperature / = 1.0. Both the pretrained and instruction-tune models are provided with a short prefix. Colors indicate perplexity scores. The output of the pretrained model looks similar to the human-generated text in terms of log-PPL. scores (more orange, red, and purple tokens indicating surprise), agreement with Figure
expected, larger models are less sensitive to the choice of the prompting method than smaller models.
Q7. How well does the range of fractal parameters overlap between natural language and LLM-generated texts? Figure 10 shows that the range of fractal parame- ters in natural language is mostly narrow subset of those observed in LLM-generated texts. Clearly, while there is overlap, natural language maintains S and H in a narrow range whereas they vary widely in ALM--enerated texts. Similar findings are also obtained using RAID dataset, as shown in Appendix A. This is consistent with the earlier observation about the relation between fractal parameters and quality of texts. Among the different factors considered, we find that prompting has the biggest impact S and H as shown in Table 2, which calculates the Shannon mutual information between fractal parameters and other variables.
Q6. How do fractal parameters relate to the quality of output? To answer this question, we first recall that the Hölder exponent S quantifies the level of self-similarity in stochastic process, with smaller values indicating a more self-similar structure (heavier tail); i.c. with complex, rich details at all levels of granularity. Hence, lower values of S are desirable. By contrast, the Hurst exponent H quantifies dependence over time. Values close to H Rv 0.5 indicate no dependence (i.e. the process is random) while values close to H R 1.0 indicate strong predictability (e.g. when the same text is repeated over and over again). Natural language has values close to H xs 0.65. In practice, LLMS do not generate words entirely at random, the correlation between H and model quality is negative. For this reason, H is also strongly and positively correlated with S, with a Pearson coefficient of 0.68 and a p-value < 10-15_
Nevertheless, it is important to keep in mind that in Fig- ure 10, each value of the fractal parameter is calculated over a corpus of texts, not individual documents. A corpus of texts corresponds to a particular combination of generating model, decoding temperature, prompting method, scoring model, and dataset. The reason fractal parameters are cal- culated over multiple documents is because they describe properties about the underlying stochastic process, such as its autocorrelation function P.: = E[zx+nTe), not properties about a single individual realization of it. Hence, multiple independent measurements are needed.
In Figure 9, we plot the average quality of documents against their average perpperply score and fractal parameters. Here, we use Gemini Pro 1.0 to auto-rate the quality of generated texts. The prompt template and examples of re- sponses are in Appendix C and we provide examples of quality ratings in Appendix G. We observe, as expected, that both and H are negatively correlated with quality. In- terestingly, H is a much stronger predictor of average quality
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A Tale of Two Structures: Do LLMs Capture the Fractal Complexity of Language?
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Figure Distribution the log-ratio of Hölder exponent (top) and Hurst exponent (bottom) in pretrained (PT) and instruction tuned (IT) models (all with simple continuation) compared to natural language. Instruction-uus sannncioo--unedd models at low temperatures 3 < have higher values H; ie. more dependence over time. Section 4/Q3 for further discussion.
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Figure 7. y-axis is the log of the ratio of the fractal parameters between LLM-generated texts and natural language, similar to Figure 3. Here, we only use instruction- tuniod models. r-axis from to right the prompting strategies Table (top to bottom). Detailed results are in Appendix F.
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Table 2. Shannon mutual information (Shannon, 1948) between or H and other variables, normalized by the Shannon entropy of the fractal parameter. We bin values into intervals of length 0.1. Prompting has the biggest impact on fractal parameters, followed by the data domain.
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Figure 8. Standard deviation of S (left) and H (right) calculated across prompting methods for each instruction-tuni model: Gem- ini 1.0 Pro (G-P), Mistral-7B (M-7) and Gemma-2B (G-2).
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LLMSs score their own outputs (i.e. using Mistral-7B to score its output, as opposed to the output of other models).
Q8. Are there notable differences when LLMs are used to score their own outputs? Inspired by prior observa- tions, which suggest that using similar architectures for both scoring and generation might work better for detecting LLM- generated texts (Mitchell et al., 2023; Fagni et al., 2021), we explore if there are differences fractal parameters when
One way to examine this to look into the reduction in uncertainty:
J(X;Y) = U(X U(X Z,Y),
(3)
where U(X measure of the uncertainty X when

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A Tale of Two Structures: Do LLMs Capture the Fractal Complexity of Language?
corr=0.11, p-valu
corr=-0.25, p-value=0.02
corr=-0.71, p-value-
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Figure 9. Average quality of LLM- -eeeereeetteed documents, judged by Gemini 1.0 Pro, vs. log-PPL (left), Hölder exponent (middle), and Hurst exponent (right). The Hurst parameter is much better predictor of quality than the other metrics. See Section 4/Q6 for discussion, Appendix for the exact prompt used in Gemini 1.0 Pro, and Appendix G for examples.
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Figure 10. Distribution of S and H for collections containing both LLLM-generatee and human-generated texts, where the values in legend indicates the proportion Lenegenertted lexts. Section 4/Q7 for details. Cases when LLMs repeat the same text mostly occur with greedy decoding (left) but we still see different distributions of S and H in higher temperatures.
conditioning Z, such as the conditional Shannon entropy or the error rate of predicting X given Z. In our setting, X and Y are the scoring and generating models while Z are the remaining variables, such as dataset, decoding temperature, and fractal parameters. Intuitively, because fractal parame- ters are included in the set predictors, if the error rate of predicting the generating model does not depend on the scor- ing model even when fractal parameters and other variables included in the set of predictors, then fractal parameters remain relatively unchanged whether not the same model is used for generating and scoring texts, which is indeed what we observe. Specifically, a random forest classifier, in its default Sciki-Learn implementation (Pedregosa et al., 2011), can predict the scoring model with a high accuracy of 97.0% without having to include the generating model in the set of predictors and this accuracy remains unchanged when including the generating model. Similarly, the accuracy of predicting the generating model is quite high at 97.8% without using the scoring model as a predictor. Including the scoring model improves accuracy only slightly.
fractal parameters (2nd order). However, this may be flection of the weight of those domains during (pre)training, rather than anything fundamental about the domains them- selves. Interestingly, when using the RAID dataset, it seems challenging for LLMs to replicate humans in poetry, and this only becomes evident when we look into the Hölder exponent.
5. Discussion and Limitations
In this work, we investigate whether LLMs are capable of replicating the fractal structure of language. We note that various strategies, such as the decoding temperature and prompting method, can impact fractal parameters even when log-perplexity scores seem to be unaffected. This goal is in line with earlier works, such as Meister & Cotterell (2021), who argued that the evaluation of LLMs should go beyond log- perpeeeelxxtty and also consider how well LLMs capture other "statistical tendencies" observed in language.
Our findings reveal that for pretrained models, larger archi- tectures are more effective at capturing such fractal proper- ties. In addition, with instruction-tunel models, the simi- larity to human language does not improve monotonically as the amount of contextual information in the prompt in- creases. Notably, the Hurst parameter emerged as a strong
Q9. Are some domains easier to synthesize? Table 3 shows that -egeeeeredeed encyclopedic articles and legal documents are closer to those of natural language in terms of average log-perply scores (Ist order statistics) and
8

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A Tale of Two Structures: Do LLMs Capture the Fractal Complexity of Language?
language models and AI, we do not feel that this particular work raises unique or unaddressed ethical concerns beyond the established considerations within the field.
Table Log-ratio of fractal parameters and log-perplexity scores between LLM-generann texts and natural language using instruction-tunee models. Results are averaged across set- tings (e.g. decoding temperatures, models and prompts). LLM- generated encyclopedic legal documents are closer to natural language, than other domains.
Acknowledgement
Domain
S
log-perplexity
We thank Vinh Tran and Jeremiah Harmsen for their insight- ful reviews and suggestions, Mostafa Dehghani and Mike Mozer for early discussions, and Google DeepMind large for providing supportive research environment.
GAGLE Dataset
NEWSROOM
0.19 1 0.14
0.02 1: 0.05
-0.58
= 0.29
SCIENTIFIC
0.17
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-0.61
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0.04 0.06
-0.03
0.26
BILLSUM
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WIKIPEDIA
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1 0.04
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RAID Dataset
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0.13 1 0.05
0.16 1. 0.02
-0.59 1 0.09
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Scoring Model: GEMINI 1.0 Pro
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Figure 1.. Similar to Figure 4, y-axis is log-ratio log-PPI. scores both pretrained (PT) and instruction-tuned (IT) models, when
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Donsity
Density
-1.0
-3.5
05
1.0
-2.0
-13
-15
-13
0.
log-ratio
log-ratio
temperature
temperature 1.0
Density
Density
0.00
0.02
0.00
log-ratio
log-ratio
Figure 12. Similar to Figure 6, this figure shows the distribution of the log-ratio of the Hölder exponent S (top) and Hurst exponent H (bottom) across LLM L.M-eeeerated articles in RAID dataset, when scored by Gemini Pro.
A. Additional Experiments using RAID Dataset
In this section, we conduct additional experiments using RAID dataset (Dugan et al., 2024), which contains articles generated by 1l models (e.g. ChatGPT, Cohere, Llama 2, etc) in domains such as Reddit and reviews, among others. The goal is to verify our main conclusions continue to hold. Since we do not control the prompts RAID and we only score texts using Gemini Pro 1.0, Q2/4/5/8 are omitted in this analysis.
Overall, find substantial agreement. We summarize our findings below:
1. Q1 (Log-perplesity): Consistent with our results, greedy decoding and instruction tuning yield lower perplexity than human text, but pretrained models at B = 1 show perplexity similar to human text. See Figure 11.
2. Q3 (Fractals in IT Models): Our findings still hold shown in Figure 12: Instruction tuning affects the Hurst exponent (H), especially at low temperatures (leading higher H), while Self-Similarity (S) remains largely unaffected.
3. Q6 (Text Quality): We use Gemini 1.0 Pro to evaluate the quality of generated articles and compare the average quality against the process of generating articles (e.g. model and hyperparameters) As before, only the Hurst exponent (H) well-correlated with quality. This observation now holds across the 11 models and domains in RAID, reinforcing earlier result. Results are shown in Figure 13.
4. Q7 (Distribution of Fractals): Natural language still has a tighter distribution of fractal parameters compared to LLM L.M--eeeeatted text, particularly for low decoding temperatures. Results are shown in Figure 14.
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A Tale of Two Structures: Do LLMs Capture the Fractal Complexity of Language?
corr= 0.21, p-value =0.02
corr=0.21, p-value=0.00 0.01
corr=-0.67, p-value
4.0
3.5
Quality
3.5
3.5
3.0
3.0
2.5
3.0
2.5
2.0
1.5
0.5
1.0
1.5
2.0
0.4
0.45
0.5
0.55
0.6
0.65
0.7
0.6
0.65
0.7
0.75
0.8
0.85
LPL
Figure Similar Figure 9, y axis is average quality of LLn-ggrerated documents, as judged by Gemini 1.0 Pro, vs. log-PPL. (left), Hölder exponent (middle), and Hurst exponent (right).
temperature
temperature 1.0
generated
Distribution
Divtribution
Real
0.0
0.2
0.4
0.6
0.0
0.2
0.4
0.6
[96)
temperature
12
Distribution
12
LLM WWWegennaatedd
0.55
0.65
0.75
0.55
0.65
0.75
Figure 14. Similar Figure 10, the distribution of S and H is shown for collections containing either L.M-ggnnrated texts original articles.
B. Definitions of Fractal Parameters
In this appendix, provide concise, self-contained description of the two fractal parameters: (1) the Hölder (Self- Similarity) exponent S and (2) the Hurst exponent H. We use the code provided by Alabdulmohsin et al. (2024) to calculate these parameters.
B.1. Hölder Exponent
An object is "self-similar" if its statistical or geometric properties remain consistent across different scales. Examples include coastlines, snowflakes, the Cantor set, and the Koch curve. Analogously, a stochastic process is self-similar if is distributionally similar to a rescaling of time.
stochastic process, such sequence of log-perplexity scores, and write (Xt)xers for its integral process: X, = D im Ty. Then, the process is said to be self-similar if (Xrt)teN is distributionally equivalent to (7 Xt)reN for some exponent S. Here, is the Hölder exponent.
Formally, let (ar))eet
One way to calculate S is follows. Fix < 1 and denote the r-increments by (Xe+r X1)tEN. These would correspond, for instance, to the number of bits used for clauses, sentences, paragraphs and longer texts as 7 increases. In terms of the increment process (IE), this corresponds to aggregating increments into "bursts"" Let p, (7) be the probability mass of the event {[Xe+r - X2 & chEN. Then, S can be estimated by fitting a power law relation Pe (7) ~ 7 (Watkins, 2019).
B.2. Hurst Exponent
The Hurst parameter [0, 1], on the other hand, quantifies the degree of predictability or dependence over time (Hurst, 1951). is calculated using the so-called rescaled-range (R/S) analysis. Let (æt)εN be an increment process. For each E N, write /t Ft El Th and Y, = Ei=o lt. The range and scale are defined, respectively, as R(n)
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A Tale of Two Structures: Do LLMs Capture the Fractal Complexity of Language?
max Y minega Y, and S(n) a ((r))sen) where is the standard deviation. Then, the Hurst parameter H is estimated by fitting power law relation R(n)/S(n) n..
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A Tale of Two Structures: Do LLMs Capture the Fractal Complexity of Language?
C. Prompting Templates
C.1. Providing Contextual Information
Table 4. A summary of the prompting templates used in this work.
Abbreviation
Description
Prompting Template
continuc
Simple continuation based on short prefix (no prompting).
None
cot
Ask the model to generate an outline first before generating the article. A short prefix is provided.
Extend the following text. Pirst write an outline. Then insert *EExtedded Text:** After that write the text using minimum 15 paragraphs containing minimum of 1000 words, in plain text only without titles or headings. The text you need to extend is: <prefix>
short keywords
A few, unordered list of keywords.
Using these keywords: <keywords>, write an article, in minimum 15 paragraphs containing a minimum of 1000 words, in plain text only without titles or headings.
keywords
Many unordered keywords.
Using these keywords: <keywords>, write an article, in minimum 15 paragraphs containing a minimum of 1000 words, in plain text only without titles or headings.
summary
A summary of the entire article.
Write about the following in minimum of 15 paragraphs containing a minimum of 1000 words, in plain text only without titles or headings: <summary>.
summary-keywords
Both a summary and an unordered list of many
Write about the following in minimum of 15 paragraphs containing minimum of 1000 words, in plain text only without titles or headings:
keywords.
<summary>. Using these keywords: <keywords>.
excerpt
An ordered list of long excerpts from the original article.
Write about the follewing in minimum of 15 paragraphs containing minimum of 1000 words, plain text only without titles or headings: <ordered excerpts>.
C.2. Generating Contextual Information
To generate the keywords and summary, we use the instruction-tume tuned Gemini 1.0 Pro model with the following prompts:
Keywords:
Genorate keywords for the following text, but only respond with the keywords in plain text separated by commas. The text is: <article>
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A Tale of Two Structures: Do LLMs Capture the Fractal Complexity of Language?
Summary:
Summar ri ze the following text in few sentences, but only respond with the text
summary in plain text. The text is: <article>
C.3. Quality of Texts
As discussed in Section 4/Q6, use the instruction-tunee Gemini 1.0 Pro model to estimate the quality of texts and relate
those to fractal parameters in Figure 9. The prompt use to estimate the quality of article is provided below:
First, explain briefly what good and what is bad about following document. Then, rate it on a scale from '1' to '5', where '1' is poorest and '5' is best. The last character in your response must be a single digit that corresponds to your rating. The document is: <article>
quality
the
Examples of how the model evaluates the quality of articles is provided in Appendix G.
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A Tale of Two Structures: Do LLMs Capture the Fractal Complexity of Language?
D. Prompts as Do-Queries Example
To illustrate the differences between Equations and 2, suppose hypothetically that all of the world's conversations are about two topics only: fairy tales or the weather. These two will be our possible contexts. Let us also assume that 75% of the time, people talk about the weather.
In addition, let us suppose that only three possible prefixes listed below are used with equal probability as shown in the table:
Prefix
p(
preet))))prrffi)
(rrffry
"It is a sunny day"
50%
50%
"It a lovely sunny day
85%
15%
"It is raining"
90%
10%
The causal graph in this setup would be as follows: (1) first select context, which is "weather" with probability 75% and "fairy tales" with probability 25%. (2) Then, select of the three prefixes. By Bayes rule, for example, the probability we select the first prompt above given that the context is "weather" would be 22.22%. Finally, (3) we continue the text, given both the context and the prefix.
a language model is trained on this data and we prompt with the phrase "Ir is a sunny day," what should the model predict next? There are two ways to interpret this question. One option that the model should give a prediction based on actual historical data, using the conditional distribution p (suffix prefix). In this case, there is 50% probability that the context is about the weather, the model will continue the prefix by describing the weather 50% of time and continue the prefix by describing fairy tale in 50% of the time.
Another interpretation, however, is that by prompting the model, we are asking the model to act as if there was an intervention that forced all conversations to start with the prefix "/t is a sunny day" and predict how the historical distribution would have looked like. that case, Equation implies that the model should sample a context first from its marginal distribution, which would "weather" with probability 75% and fairy tales with probability 25%. Once the context selected, it can the historical data to predict the continuation of the prefix "It sunny day" after conditioning that particular context. The intuition here from the causal graph in Figure 1 is that it is the context that dictates causes the conversations, not the other way around, so the distribution of underlying contexts would remain the same.
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A Tale of Two Structures: Do LLMs Capture the Fractal Complexity of Language?
E. Data Card
Table Generated And Grounded Language Examples (GAGLE) data card.
Description
GAGLE comprises of LLM generated articles. All articles are generated using the public checkpoints of the open- spen-source models Mistral-7B (Jiang et al., 2023) and Gemma-22 1an (Mesnard et al., 2024),
The seed for all articles are sourced from five academic datasets: (1) WIKIPEDIA articles (Wikimedia, 2024), (2) BIGPATENT, consisting of one million records of U.S. patent documents (Sharma et al., 2019), (3) NEWSROOM, containing over one million news articles (Grusky et al., 2018), (4) SCIENTIFIC, a collection of research papers obtained from ArXiv and PubMed OpenAccess repositories (Cohan al., 2018), and (5) BILLSUM. containing US Congressional and California state bills (Kornilova & Eidelman. 2019). So. all articles of encyclopedic. news, legal, scientific or patents nature. The dataset contains, at most, 1,000 articles from each domain.
The articles are generated via the following prompting strategics:
continue (pt): Simple continuation based on short prefix (no prompting) using pre- trained model.
continue (it): Simple continuation based on short prefix (no prompting) using instruction tunood model.
cot: Ask the model to generate an summary first before generating the article. short prefix is provided.
short keywords: A few, unordered list keywords.
5. keywords: Many unordered keywords.
6. summary: A summary of the entire article.
7. summary + keywords: Both a summary and an unordered list of many keywords.
8. excerpt: An ordered list of long excerpts from the original article.
With the exception cent inue (pt), all other prompts are used instruction- tuned models.
Primary Data Modality
Text.
Data Fields
1. ID: unique ID identifying the original ground-truth article.
2. Model: one of Mistral-7B and Gemma-2B.
Domain: one of WIKIPEDIA, BIGPATENT, NEWSROOM, SCIENTIFIC, and BILLSUM.
Prompt: the specified prompting methods.
5. Temperature: numeric. Either 0.0 (greedy decoding), 0.5, or 1.0 (pretraining temperature).
6. Prefix: Prefix used to generate the article.
7. Quality: description of the quality of the article along with a rating from (poorest) to (best).
Text: Actual LLM-generated text.
Log-Perplyxity Scores: The scores generated by one of Mistral-7B Gemma-2B pretrained models.
Intended Use Case
Facilitate research domains related to detecting and analyzing LLM- --gerrttd tests.
Access Type
Unrestricted.
CC-by-4.0
Sensitive Human Attributes
N/A
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A Tale of Two Structures: Do LLMs Capture the Fractal Complexity of Language?
F. Contextual Information in the Prompt: Full Figures
BigP
Science
0.4
0.4
0.4
0.4
044
comt
CoR
Information Density
Information Density
Information Density
Information Density
Information Density
GEMINI
BigP
Science
0.4
0.4
0.4
Stng
clins
cot
comt
Information Density
Information Density
Information Density
Information Density
Information Density
1.0
BigP
0.4
8
cont
cent
sum
COt
sum
Information Density
Information Density
Information Density
Information Density
Information Density
Figure 15. Hölder Exponent. Generating model = Gemmni 1.0 Pro
MISTRAL-7B 0
BigP
Science
0.4
0.4
Coun
cum+
Information Density
Information Density
Information Density
Information Demsity
Information Demsity
MISTRAL-7B 0.5
Bills
BigP
Science
0.4
0.4
Bade
0.4
0.2
com
com
Information Density
Information Density
Information Density
Information Density
Information Density
MISTRAL-7B/11
0.6
Wiki
BigP
Science
0.6
Newa
0.4
0.4
0.4
5ong Berte
0.4
comm
ccm
Information Density
Information Density
Information Density
Information Density
Information Density
Figure 16. Hölder Exponent. Generating model = Mistral-7B
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A Tale of Two Structures: Do LLMs Capture the Fractal Complexity of Language?
GEMMA-28
Science
1100
0.4
0.4
0.4
0.4
401
exC
Information Density
Information Density
Information Density
Information Density
Information Density
GEMMA-28 0.5
0.4
0.4
0.
0.4
0.4
Information Density
Information Density
Information Dansity
Information Density
Information Density
GEMMA-28
BigP
0.4
0.4
5
Soy
Information Density
Information Density
Information Density
Information Density
Information Density
Figure 17. Hölder Exponent. Generating model = Gemma-2B
GEMINI Pro
0.2
BigP
0.2
Science
Rato
Keg Rode
SUB
Coo
Com
Information Density
Information Density
Information Density
Information Demsity
Information Demsity
Pro
0.2
Bills
BigP
0.2
Science
News
com
com
Information Density
Information Density
Information Density
Information Densitty
Information Density
GEMINI
0.2
Wiki
BigP
Science
0.2
News
Log
com
cco
Information Density
Information Density
Information Density
Information Density
Information Density
Figure 18. Hurst Exponent. Generating model = Gemini 1.0 Pro
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A Tale of Two Structures: Do LLMs Capture the Fractal Complexity of Language?
MISTRAL-7B
Science
H1
Hong
Information Density
Information Density
Information Density
Information Density
Information Density
MISTRAL-7B/ 0.5
comt
Information Density
Information Density
Information Density
Information Density
Information Density
BigP
Information Density
Information Density
Information Density
Information Density
Information Density
Figure 19. Hurst Exponent. Generating model Mistral-7B
BigP
Hogguee
Hinghete
on
come
comz
Information Density
Information Density
Information Density
Information Density
Information Density
0.2
BlgP
Jum
clint
COF
conz
cstt
Information Density
Information Density
Information Density
Information Density
Information Density
Wiki
Big?
Science
News
cont
Information Density
Information Density
Information Density
Information Density
Information Density
Figure 20. Hurst Exponent. Generating model = Gemma-2B
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A Tale of Two Structures: Do LLMs Capture the Fractal Complexity of Language?
G. Example Documents of Generating Processes with Large and Small Values of H
G.1. Large Values of
Data domain: Newsroom.
Generating:
Model Name: Mistral-7B (pretrained)
Decoding Temperature: 0.5
Prompting Method: continue
Fractal Parameters:
Scoring Model Name: Gemini 1.0 Pro.
Hölder Exponent: S 0.630
Hurst Exponent: =0.808
G.1.I. DOCUMENT I
Ground-truth Record ID:
"newsroom-00001-0000
Prefix:
(the following article contains a minimum of 15 paragraphs containing a minimum of 1000 words, in plain text only without titles or headings.)
A year ago, the 25-year-old Serb swept into Paris after amassing one of the most spectacular five months in men's tennis history, winning every tournament he'd entered. Now, far from perfect season, the tournament's top seed arrives at the French Open,
Generated:
the last major of the year, having lost his last two matches, his ranking and his No. 1 ranking.
In the past year, Djokevic has been beaten by 17-year-old American, lost the last seven he's played, and been beaten by a player ranked 111th in the world.
The Serb, who won the Australian Open and Wimbledon in the first two months of the year, was beaten by 17-year-old American Jared Donaldson in the final of the Stuttgart Open last month.
He then lost to Dominic Thiem in the Rome Masters final, before being beaten by Gael Monfils in the quarterfinals of the Madrid Open.
Djokovic, who has been ranked 1 for the last seven years, ranked 1ith in the world.
The Serb will be heping to bounce back in Paris, where he has won three of the Last four French Open titles.
He will face 23-year-cld Russian Andrey Rublev in the first round of the tournament.
Rublev is ranked 22nd in the world, but has been on roll in the last few months, winning the St. Petersburg Open and the ATP 500 event in Rome.
The Serb has been in fine form in the last few months, winning the Australian Open and Wimbledon in the first two months of the year.
Djokovic has been in fine form in the last months, winning the Australian Open and Wimbledon the first two months of the year.
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A Tale of Two Structures: Do LLMs Capture the Fractal Complexity of Language?
The Serb has been in fine form in the last few months, winning the Australian Open and Wimbledon in the first two months of the year.
The Serb has been in fine form in the last few months, winning the Australian Open and Wimbledon in the first two months of the year.
The Serb has been in fine form in the last few months, winning the Australian Open and Wimbledon in the first two months of the year.
The Serb has been in fine form in the last few months, winning the Australian Open and Wimbledon in the first two months of the year.
The Serb has been in fine form in the Last few months, winning the Australian Open and Wimbledon in the first two months of the year.
The Serb has been in fine form in the last few months, winning the Australian Open and Wimbledon in the first two months of the year.
The Serb has been in fine form in the last few months, winning the Australian Open and Wimbledon in the first two months of the year.
The Serb has been in fine form in the last few months, winning the Australian Open and Wimbledon in the first two months of the year.
The Serb has been in fine form in the last few months, winning the Australian Open and Wimbledon in the first two months of the year.
The Serb has been in fine form in the last few months, winning the Australian Open and Wimbledon in the first two months of the year.
The Serb has been in fine form in the last few months, winning the Australian Open and Wimbledon in the first two months of the year.
The Serb has been in fine form in the last few months, winning the Australian Open and Wimbledon in the first two months of the year.
The Serb has peen in sine form in the Last few months, winning the Australian Open and Wimbledon in the first two months of the year.
The Serb has been in fine form in the last few months, winning the Australian Open and Wimbledon the first two months the year.
The Serb has been in fine form in the last few months, winning the Australian Open and Wimbledon in the first two months of the year.
The Serb has been in fine form in the last few months, winning the Australian Open and Wimbledon in the first two months of the year.
The Serb has been in fine form in the last few months, winning the Australian Open and Wimbledon in the first two montha of the year.
The Serb has been in fine form in the last few months, winning the Australian Open and Wimbledon in the first two months of the year.
The Serb has been in fine form in the last few months, winning the Australian Open and Wimbledon in the first two months of the year.
The Serb has been in fine form in the last
Gemini 1.0 Pro Evaluation of Quality of Generated Text:
** The document provides detailed account Novak Djokovic's recent performances and rankings. It includes specific instances of his losses, such as
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A Tale of Two Structures: Do LLMs Capture the Fractal Complexity of Language?
being beaten by a 17-year-old American and a player ranked 111th the world. The document also highlights Djokovic's a past achievements, such as winning the Australian Open and wimbledon earlier in the year.
A The document repeats the sentence "The Serb has been in fine form in the last few months, winning the Australian Open and Wimbledon in the first two months of the year" 17 times, which makes the content repetitive and unnocessarily long.
Rating: 3/5
Ground-truth:
A year ago, 25-year-old Serb swept into Paris after amassing one of the spectacular five months in men's tennis history, winning every tournament he'd entered.
Now, far from perfect season, the tournament's top seed arrives the French Open, which starts Sunday in Paris, with fissures his impenetrable facade.
Djokovic has not wen clay title in 2012 and dropped his last two finals on dirt to defending champ Rafael Nadal- after beating him the previous seven times.
He has also shown flashes of anger and signs of frustration, emotions that, in the past, have undermined his performance.
"I not comparing last year and this one, Diokovic said Monday following his 7-5, 6-3 loss to Nadal the rain-delayed final in Rome. feel good on the court and I need to make a few adjustment before Paris, but I'll be in top form."
If Nadal's resurgent spring and 45-1 record in Paris make him the favorite, Djokovic still will be dealing with heightened expectations.
A Paris title would place him in rarefied comрany - one of just seven men to have won all four majors in career.
Even more historic,
a chance to held all four majors simultaneously called "Djoker Slam" a feat not achieved by a man since Rod Laver 43 years ago.
so-
"I" he were to win four in row, said laver-admi rer John McRnroe, who came close but never won Roland Garros, "suddenly he'd be like top-10 (of best players history). There's s lot riding on it."
Djckovic's first taste of defeat in 2011 eccurred on the crushed red brick of Paris to Roger Federer, who snapped his perfect season. and 43-match winning streak in the semifinals. To put that run in perspective, consider this: Djokovic' first loss in 2012 came nearly three months and 33 matches carlier (to Andy Murray in the finaliaas in Dubai in February).
"It might have been the case," Djokovic teld USA TODAY Sports when asked if the weight of his faultless performance sapped his energy and clouded his focus. "But I think even under that pressure played a great tournament. Obviously in the semifinals against Roger, have done what I could at that stage. I did my best at that moment, and he was better player."
Nadal went on to defeat Federer in the final, and the loss barely made an impact the Serb's s juggernaut season.
Djckovic won Wimbledon and the U.S. Open, snagged the No. ranking and punetuated his dominant 2011 by winning third consocutive major (and fifth overall) January at the Australian Open- all three in finals against Nadal.
He has won four of the last five majors and remains the man to beat in best-of-five sets if the Spaniard has regained his swagger on clay.
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A Tale of Two Structures: Do LLMs Capture the Fractal Complexity of Language?
"The way he's played Slams the last year or has been very, very impressive,
says fourth-ranked Scot Murray.
Once the pleasing third wheel to Federer and Nadal, Djekovic's transfornation into world-beater well chronicled.
Possessed of uncanny flexibility, redirecting ability and the most lethal two-handed backhand in the game, the 6-2 Djokovic had the skills to be great player.
After leading Serbia to its first Davis cup championship in 2010, he made the small adjustments fixing a flawed serve, shoring up his forehand and famously cutting gluten out of his diet - that helped him overcome the mental lapses and suspect fitness that plagued him in the past.
That he was able to realize his potential after playing third fiddle for so Long he finished No. 3 from 2007-2009 behind the Federal-Nacie duopoly is testament to his resolve.
Djokovic, who as child told a televisicn interviewer that he little time for fun and games because he was going to become champion, believed he had it him.
"I knew that have qualities, but I wasn't managing to make that final step, he says. "I think it all mental and it all growing up and maturing. In the end, I managed to it."
while he never expected to repeat his 2011 season - season that earned hin laureus Sportsman of the Year award and segment this spring on 60 Minutes- Djokovic has discovered what many before him have said: it's harder stay on top than to get there.
Djokovic limped inte the fall after holding off Nadal in the 2011 U.S. Open final, taking five of his six losses (70-6) post-New York and failing to win any titles.
His body was showing signs of wear, too, when he retired with back pain in the second set against Juan Martin del Potro in Serbia's s semi final Davis Cup defeat to Argentina.
To recoup and recover, he took a two-week vacation with his girifriend, Jelena Ristic in the Maidives and then camped out in the heat of Dubai for two weeks in December.
his close-knit team gathered plot for the coming year, they determined that the greatest danger to him was fitness and complacency.
"We (tried) to keep him little bit down en the earth to be humble, modest, to realize that it doean't come easy, says his longtime coach Marian Vajda of slovakia. "He earned that spot. He deserved it. came with work, work and work
While they worked on his fitness and tweaked his game including taking the ball earlier they also deci ded in crowded year of events, including the London Olympics, that winning in Paris would be their singular mission.
"Roland Garros is en top of the priority list," Djokovic says.
Djokovic has had to make sacrifices and adjustments, such as skipping his honetown tournament in Belgrade, which is owned by his family, It was major blow to the event, but coming on the heels of his loss in Monte Carlo and his grandfather's death, Djokovic needed the break.
"He is doing great job of pacing himself and managing his schedule for majors, ESPN's Brad Gilbert says.
Djokovie, who tried failed coaching experiment with American Todd Martin two years ago, is bent on keeping things constant.
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A Tale of Two Structures: Do LLMs Capture the Fractal Complexity of Language?
"My approach hasn't changed, really, Djckevic says. "I still have the same practice routines every day. I didn't change anything in tennis practices, in my preparations. I have the sane places, same people around me, same kind of routines. have something in my career and my team ... it hasn't worked that well. So I keep it very simple."
to change, really, because as soon try change
statistically, Djckevic has shown little drop-off, except in his vaunted return game, where his year-over-year winning percentage against his opponents' serve has dropped from 42.8% to 34.68 neading into Roland Garros.
But cracks have appeared in his resolve.
Earlier this month Djckovic joined Nadal in lashing out at Madrid's slippery blue clay before surzendering feebly to compatriot Janko Tipsarevic 7-6 (7-2), 6-3 in the quarterfinals.
During blustery conditions Rome, Djckevic destroyed frame after losing the first set in a third-round victory against Juan Monaco of Argentina. broke another during his loss to Nadal two matches later.
"I hope the children watching don't do that," " smiling Djokovic told reporters afterward. "But show my enotions out there. That's s who am."
of the court, the fiercely loyal fami ly man suffered a personal loss when his grandfather, vladimir, died during the Monte Carlo tournament last month. Djokovic continued to play but wasn't himself. The loss of a loved one lingers.
With the immovable force of Nadal looming on clay, there is little time for self-pity
The Spaniard leads Dickevic 11-2 on clay (18-14 overall) and has owned him in Paris, winning all three of their matches (2005-08) without dropping a set.
"He is the Mount Everest that surface in best out of five," " Andre Agassi said of Nadal in a recent call with reporters.
To win, Djokovic will almost certainly have to conquer the 10-time major winner from Spain, who is chasing his own bit of history surpassing Bjorn Borg with an unprecedented seven French Open crowns.
"It's s not going to be an easy tournament to win because we know sort of Rafa owns that place, No. 3 Federer says.
It will tougher still since Nadal appears to have recovered from hole Djokovic bored in his psyche last year something Nadal himself admisted.
Nadal knew he was close and never stopped locking for answers, saying after the Rome final: "You don't need to find great things it is the small things that make the di diffrrence."
"No question (Djckevic's performance last year proves that he's capable of (wi (winning Roland Garros), " said Agassi, whose victory at Roland Garros in 1999 completed his set of Grand Slam trophies. "You have give slight given how many times he's won and just what a physical accomplishment it would be to take him off that perch in Paris."
Nadal just
With streak to defend, some pressure is off. That could be blessing in disguise. Or at least, it's tradeoff.
Whatever confidence Djckevic might have shed is counterbalanced by the knowledge that the drumbeat of perfection is a distant sound.
"This one's s gonna be little easier maybe because of the pressure of last year,"
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says 16-time Grand Slam winner Federer, who lost Djokovic in the Rome semifinals.
Agrees Djokovic: "I believe that with the great clay-court season that I had last year and wins back-to-back against Rafa proved to that I can play equally well on clay as in the other surfaces. need to keep that in my mind and believe in the success."
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G.1.2. DOCUMENT II
Ground-truth Record ID:
Prefix:
(the following articie contains minimum of 15 paragraphs containing minimum of 1000 words, plain text only without titles or headings.)
the Perreirasdorp section of Johannesbuzg, passed a group of young men who had set up informal carwash business with buckets soapy water arranged by curb, and turned corner, there was Nelson Mandela. Though Mandela died Dec
Generated:
5, 2013, at the age of 95, his face is everywhere in South Africa, posters, in store windows, the walls of buildings, and the sides of buses.
Mandola is the face of South Africa, the face of freedom, and a symbol of the strugglc against oppression and apartheid. He was the first black president of the country, serving from 1994 to 1999.
Mandela is a hero to many, but not to everyone.
"I don't like Nelsen Mandela, said young man who was selling copy of the South African nexspaper, The Sowetan, at street in the Hillbrow neighborhood of Johannesburg.
"why not?" I asked.
"He was communist," " the man said.
I asked him what he thought of apartheid. "It not good, he said.
"But Nelson Mandela fought against apartheid," said.
"Yes, but he was communist, the man said.
"What is wrong with comnunism?" asked.
"It is a bad ideclogy, the man said.
"What do you think about the current president South Africa, Jacob Zuma?" asked.
"He is a bad president,"' the man said.
"Why?" I asked.
"He is not doing anything for the people," " the man said.
"Do you think that Mandela did more for the people of South Africa than Zuma has ? " asked.
"Mandela did more for the people," the man said.
"Why?" I asked.
"He was a good man, the man said.
"What do you think about the ANC?" I asked.
"The ANC bad party, the man said.
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A Tale of Two Structures: Do LLMs Capture the Fractal Complexity of Language?
"Why?" I asked.
"They are corrupt," " the män said.
"What do you think about the DA?" I asked.
"The DA is a good party, the man said.
"Why?" asked.
"They are not corrupt," " the man said. "What do you think about the EFF?" asked. "The EFF is a bad party, " the man said. "Why?" " I asked.
"They are violent," the man said.
"What do you think about Julius Malema?" I asked.
"Jalius Malema is bad man, the man said.
"why?" asked.
"He is a communist, " the man said.
"What do you think about the ANC Youth League?" asked.
"The ANC Youth League is bad organizaticn, the said.
"Why?" I asked.
"They are violent," " the man said.
"What do you think about the EFF Youth League?" asked.
"The EFF Youth League is bad organizatien," the man said.
"Why?" asked.
"They are violent," " the man said.
"What do you think about the DA Youth League?" asked.
"The DA Youth League is a good organizaticn," the man said.
"Why?" I asked.
"They are not violent," " the man said.
"What do you think about the EFF Women's s League?" I asked.
"The EFF Women's s League a bad organization, " the man said.
"Why?" I asked.
"They are violent," " the said.
"What do you think about the DA Women's League?" I asked.
"The Women's League is good organization, " the man said.
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"why?" asked.
"They are not violent," the man said.
"What do you think about the ANC Women's League?" I asked.
"The ANC Women's League is a bad organization," the man sald.
"Why?" I asked.
"They are violent," the man said.
"what do you think about the RPP Women's league?" I asked.
"The EFF Women's League a bad organization, " the man said.
"Why?" 1 I asked.
"They are violent," " the man said.
"What do you think about the DA Women's League?" I asked.
"The Women's League is good organization, " the man said.
"Why?" I asked.
"They are not
Gemini 1.0 Pro Evaluation of Quality of Generated Text:
The document provides detailed account of conversation with young man in Johannesburg regarding his views Nelson Mandela, politics, and various organizations South Africa. The document is well-organized, with clear structure, and effectively presents the man's opinions. However, some might argue that it presents onc-sided perspective, focusing solcly on the man's negative opinions without presenting other viewpoints. To provide understanding, could have included additional perspectives or opinions on the discussed topics. rate this document as 3 out of 5.
balanced
Ground-truth:
In the Ferreirasdorp section of Johannesburg, I passed group of young men who had set up an informal carwash business with buckets of soapy water arranged by curb, and as I turned corner, there was Nelsor. Mandela.
Though Mandela died on Dec. 5, here steed vividly rendered layered steel cutouts, 34 years old, fresh and strong and in the pose of a boxer. He once had professional aspirations, and said the aweet science taught him about defense, attack and strategy, which he applied to law, politics and life in prison.
Unveiled two years age, the 16-foot sculpture, called "Shadow Boxer,
a lively piece with a great sense of energy and movement. The South Aérican artist Marco Cianfanelli gave it a three-dimensional effect by Layering painted-steel sheets in image from well-known 1952 photograph by Robert Gosani. The work is set front of the Johannesburg nagistrate court across the street from Chancellor House, where Mandela and his law, and later political, partner Oliver Tambo were young lawyers. Chancellor House, a privately owned corner office building, was itself restored in 2011 and has a timeline exhibition of the two partners' time there.
The attractions have drawn visitors to a neighborhood that has a cortain rough-around -the-edges look but is no longer so menacing. Since Mandela's death, the sculpture has become something of memorial with people laying flowers at its base.
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The work speaks volumes: the young Mandela about to wage the fight of his life. It also shows how public art is helping to revivify urban Johannesburg, a seemingly implausible regeneration in this city of more than four million residents, which not that long ago seemed as though it was about to fall through the widening cracks of crime and dilapidation.
The art has helped fester a virtuous cycle: Color and beauty draw people; people promote security, which draws nore people, and creates a bigger audience possibility for popular. A new market, the Sheds@lFox, featuring South pfrican-produeed gooas, set to open in October about two blocks from Chancellor House. Johannesburg is now Africa's s most visited city, with 2.5 million internationa visitors in 2013, according to MasterCard's annual survey.
art. The improvements have made Joburg cool again and
"Art plays an incredibly important role in naking people aware that the city is being reborn," " said Gerald Garner, guide and author of two books, "Johannesburg Ten Ahead" and "Joburg Places." "It's one thing to fix pavements and plant trees, and doing those things make difference. But art makes the city humane. It tells people who were excluded by the old order that they' re welcome, tells stories of people who live here and celebrates the heroes."
I lived in Johannesburg's northern suburbs for three years in the early 1990s and have returned frequently ever since. saw some of its worst days, when parts of its downtown grid began to feel like an urban dystepia. One of the emblematic events, what some may argue was the 1ow point, actually involved public art, in the late 1990s when vagrants in once-beloved, then abandoned Oppenheimes Park, decapitated sculpture of impalas and sold the heads as scrap metal.
To be clear, good number of swaths of Johannesburg remain iffy. But the public art was a revelation for me, and after several days of exploring the city by foot and on public transit last July, felt much more optimistic about its prospects. In the months since, the pace of positive change has even picked up
My route was donc in several walks, some on ny own on the way to various appoLntments and then with help from Mr. Garner and later still from a thoughtful 22 22-year- old artist named Jabulani Fakude whom hired through a local company called MainStreetWalke (mainstreatwa wwweeetwwwkk......... co. It's good idea for visitors get guides to navigate between some areas that remain sketchy.
A short walk from "Shadow Boxer, the adjeining district, Newtown, has another popular sculpture of two other South African heroes, Walter and Albertina Sisulu, on Diagonal Street. The Sisulus, towering figures of the apartheid struggle, appear not as fighters but as elderly lovers and parents, The Johannesburg artist Marina Walsh, who installed the concrete sculpture in August 2009 after the city solicited proposals from artists, has the couple seated facing each other, their eyes locked in an affectionate gaze. The intention is to show them equals. The Sisulus, who raised eight children, were married for 59 years, including Mr. Sisulu's s 25-year term as a prisoner on Robben Island, an austere prison off the shore of Cape Town. They' re exaggerated in scale; huge figures, squat and round, so viewer you' almost like grandchild looking at grandparents, and indeed, you see people moved to sit in their 1aps something the artist is happy to see them do.
"I get an enormously warm response from people, Ms. Malsh told me. "Cne Saturday I was cleaning off a mustache someone had drawn on Albertina's lip. Some guy shouted, 'Watt are you doing?' He thought was defacing her. told him I was cleaning her and asked if he knew who they were. He said, 'Yes, they' re my heroes !!"
Not every work is as accessible, and the heroes often don't have names. As you leave Newtown via the landmark Queen Elizabeth Bridge, you quickly come upon "Fire Walker, collaboration between the renowned South African artists William Kentridge and Gerhard Marx. It's set on a traffic island, and up close, the 36- foot work, erected with steel pieces, looks like mass exploded fragments. But like an Impressionist painting, the image is clearer farther away that of
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woman carrying a burning caidron on her head. It's sight you rarely see Johannesburg now, these women with braai mealies (roast corn) that they carry with flames crackling atop their heads. The work is a tribute to the hard ways people scrabble together a living.
I wanted to visit Braamfontein, precinct on the northern side of the city with two of South Africa's s major universities, the University of the Witwatersrand and the University of Johannesburg. It has some of the carliest major public artworks, including "Juta Street Trees, nine large metal tree sculptures, and the "Eland, a concrete sculpture of Africa's largest antelope, which were installed in 2006 and 2007, respectively. The aesthetic upgrade has helped transform the student- dominated area, which even just four years ago still felt threatening, into place with lively night 1ife and popular Saturday Nei geiobuuusgs Market of artisan food, fabrics and jexeizy.
Up
the hill from Juta Street, in the university area, I went to the Constitutional Court of South Africa, the country's highest court and itself a work of art both for its architecture and interior design. Set Constitution Hill with views of city streets leading out toward the hilly and affluent northern suburbs, the court's s design was intended to embody the idea of traditional justice, the way elders would hear disputes before whole communities under a tree. The notion of transparency is embodied in its opennesa, the glass exterior. The interior pillars are erected at angles that are meant suggest the branches of tree, the public space where Legal decisions were rendered. Wood carvings, skins and art are integrated into the interior and are also curated in exhibits.
Not all public art has efficial sanction,
is even legal for that matter. Wall murals have added vibrancy, too, though much the city still struggling to accommodate street artists who have plenty of urban wall space but face obstacles in getting permits; they consequently resort to "bombing," " or illegally painting on, buildings, and for their trouble will spend occasional nights in jail or have to resort to bribing the police go away.
Mr. Fakude, the young street artist, does his work deep Into the night, and moonlights by day giving walking tours for 250 rand, about $24, at 10.40 rand to the dollar in my case, graffiti tour of his and others' work in the artsy Market Theater area on the western side of the city. Mr. Fakude's work included one wall piece that was playful at a glance, invented, but that had a serious message that addressed the stark reality of crime and violence. So, too, did others. "We want to get the message across that drugs are not cool," he said. It gave the ostensible illegality of the street artists' work certain irony, given the grass-roots appeal to nonviolent,
one-eyed cartoon character he'd
cleaner living. It has gotten Mr. Fakude some attention, however. He was invited to paint murals in Berlin this year, though he told me zecently that he was not able to travel there.
One place where murals are getting an official stamp of approval is one of Joburg' most trendy and ambitious new places, the Maboneng Precinct, once a vast wasteland of disused industrial spaces and warehouses. In recent years, the 250- acre zone of reclaimed and repurposed industrial buildings has commiss ssioned several dozen murals by artists from Berlin, Baltinore and elsewhere. In January, a 10-story mural was unveiled of "i am because we are." The artist Rickey lee Gorden, who goes by Freddy San, used the same photo as "Shadow Roxer, the Mandela sculpture in Ferreirasdoxp. The revitalization of Maboneng Precinct began Main Street.
in 2009 with Arts Main, a complex of studios and galleries Indeed, the first public art work the precinct was the word "Maboneng, which is Sotho for "place of light" and was installed as text art on the Arts on Main rooftop.
Arts Main's success Lifted Maboneng's profile. Mr. Kentridge, arguably South Africa's best-known living artist, was among the first to set up a studio. Five years hence, the area is a vibrant hive of artists' lofts, mixed-use spaces, live theater, galleries, a landscaped bar-cafe and a boutique hotel called the 12 Decades, with dozen rooms designed with artwork and artifacts to celebrate each decade in the life of the city, waich started in 1886 after gold rush.
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Each building has a lighthouse design incorporated. There are 30 murals, with five more scheduled to be completed by year's 8 end, In addition to the murals, an old canal is being restored, its banks featuring life-size steel cutouts of people who made important contributions to the precinct.
One of the last areas I explored was the mining district in Johannesburg's commercial center. The area is strewn with artifacts of mining accouterments as public art in city built mining. Here I found the happy postscript to the story of " Leaping Impalas," " the sculpture by Herman Wald in Oppenheimer Park that had been decapitated by vagrants in the 1990s. In 2002, new heads were fashioned and welded on, and the sculpture was installed in front of the headquarters of Anglo- American, one of the world's largest mining companies. You can still see the markings the necks, like is the process healing.
that reflect the greater story of city that
A version of this article appears in print on July 13, 2014, on page TR10 of the New York edition with the headline: & City Seen Through Artists' Eyes. Order Reprints IToday's I Subscribe
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G.1.3. DOCUMENT III
Ground-truth Record ID:
"newaroom-00022-0065
Prefix:
(the following articie contains a minimum of 15 paragraphs containing a minimum of 1000 words, in plain text only without titles or headings.)
Hanley Ramirez's defense at shortstop only cost the Dodgers perfect game Wednesday night. what might it cost the team in September and potentially October? what might cost Ramirez free agency after that? Many expected the Dodgers to sign Ramirez to an extension by now. It hasn't happened, in
Generated:
part because they don't know if he' 11 be shortstop next year.
Ramirez has made errors in 70 games shertstop this season. He has a minus-7 defensive runs saved, the second-worst mark among shortstops with at least 400 innings. He has a minus-11 total zone rating, the worst mark among shortstops with at least 400 innings.
Ramirez has made 13 errors in 70 games at shortstop this season.
Ramirez has made 13 errors in 10 games at shertstop this season.
Ramirez has made 13 errors in 70 games shertstop this season.
Ramirez has made 13 errors in 70 games at shortstop this season.
Ramirez has made errors in 70 games shortstop this season.
Ramirez has made 13 errors in 70 games at shortstop this season.
Ramirez has made 13 errors in 70 games shortstop this season.
Ramirez has made 13 errors in 70 games at shortstop this season.
Rami rez has made 13 errors in 70 games shortstop this season.
Ramirez has made 13 errors in 70 games shertstop this season.
Ramirez has made 13 errors in 70 games at shortstop this season.
Ramirez made errors in 70 games shortstop this season.
Ramirez has made 13 errors in 70 games at shortstop this season.
Ramirez has made 13 errors in 70 games shortstop this season.
Ramirez has made 13 errors in 70 games at shortstop this season.
Rami rez has made 13 errors in 70 games shortstop this season.
Ramirez has made 13 errors in 70 games at shortstop this season.
Ramirez has made 13 errors in 70 games at shortstop this season.
Ramirez made errers in 70 games shortstop this season.
Ramirez has made 13 crrors in 70 games at shertstop this season.
Rami has made 13 errers in 70 games shortstop this season.
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A Tale of Two Structures: Do LLMs Capture the Fractal Complexity of Language?
Ramirez has made errors in 70 games shortstop this season.
Ramirez has made 13 errors in 70 games at shortstop this season.
Ramirez has made 13 errors in 70 games shortstop this season.
Ramirez has made 13 errors in 70 games at shortstop this scason.
Ramirez has made 13 errors in 70 games shortstop this season.
Rami rez has made 13 errors in 70 games at shortstop this season.
Rami rez has made 13 errors in 70 games shortstop this season.
Ramirez has made 13 errors in 70 games shertstop this season.
Ramirez has made 13 errors in 70 games at shortstop this season.
Ramirez made errors in 70 games shortstop this season.
Ramirez has made 13 errors in 70 games at shortstop this season.
Ramirez has made 13 errors in 70 games shortstop this season.
Ramirez has made 13 errors in 70 games at shortstop this season.
Ramirez has made 13 errors in 70 games shortstop this season.
Ramirez has made 13 errors in 70 games shertstop this season.
Ramirez has made 13 errors in 70 games at shortstop this season.
Ramirez made errers in 70 games shortstop this scason.
Rami rez has made 13 errors in 70 games at shortstop this season.
Ramirez has made 13 errors in 70 games shortstop this season.
R
Gemini 1.0 Pro Evaluation Quality of Generated Text:
The quality of the document is poor. The document is repetitive and does not provide any new or insightful information. The same sentence is repeated over and over again, making it difficult to read and understand. The document does not provide any context explanation for the to assess the severity of his performance. would rate this document a out of 5.
Ramirez has made, making it impossible
1
Ground-truth:
Hanley Rami rez's defense at shortstop only cost the Dodgers perfect game Wednesday night. what might cost the team in September and potentially October? what might it cost Ramirez in free agency after that?
Many expected the Dedgers to sign Ramirez to an extension by now. It hasn't happened, in part because the Dodgers want to him stay healthy, in part because they might not sure what the heck to do with him long term.
Third base could be possibility, but the Dedgers would need to trade Juan Uribe, a popular clubhouse figure who is under contract for $6.5 million next season. Some Dodgers officials have toyed with the idea of playing Ramires in left field, but you may have noticed that the team has too many outfielders already.
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Maybe the Dodgers could win the 2014 World Series with Ramirez at short - heck, the Red Sox pulled off such a feat with Julio Lugo in '07. Advanced metrics, however, portray Ramirez as one of the worst defensive shortstops in baseball. And strong updthe-middle defense should be requirement for team built around starting pitching, no?
Erisbel Arrucbarrena, Cuban defector in the first year of a five-year, $25 million contract, major defensive upgrade in his brief stint with the club. But the Dodgers have said that they do not want to shift Ramirez between short and third perhaps out of respect for ramirez's wishes. Resides, Uribe could return from his strained right hamstring on Monday.
For this season, it appears, the Dodgers have 1ittle choice but to play Ramirez short; they probably value his offense too much to trade him. But if Ramirez hits free agency, his market could turn problematic at a time when teams continue to place increased emphasis on defense. Indeed, what would it say about Ramirez if the Dodgers only were willing to make him qualifying offer?
Let's not get too far ahead of ourselves. Ramirez and many of his teammates are on the uptick offensively. The Dodgers, winners of eight of their last 11, are only four games behind the Giants in the NL West. A big run to the postseason, a World Series title, and Ramirez's attributes again might outweigh his deficiencies.
Still, his poor throw that cost Clayton Kershaw a perfect game came on a play, as Hall of Fame broadcaster vin Scully noted, that most shortstops make. Rami didn't t make it. He is costing his team. He is costing himself.
Beyond Ramirez, questions persist about the Dodgers, just as they for every club.
One rival executive said Thursday that the Dodgers' best outfield would be Matt Kemp in left, Joe Pederson in center and Yasiel Puiş in right. The added that the Dodgers should trade one their left-handed hitting outfielders, Andre Ethier or Carl Crawford, and keep the other in reserve.
Pederson, while batting .320 with 1.016 CPS in the hitter-friendly Pacific Coast a ton, including times in his last 28 at-bats.
League, continues strike Crawford remains on the DI with a sprained left ankle. Ethier has only three homers and .691 CPS. And let's not even talk about their respective contracts.
Кеmр finally is getting hot, his disposition improving with his swing. Many in the industry, however, believe he ultimately will be moved most likely in the offseason - due to his tempestuous relationship with some of his superiors.
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have read and agreed to the Fox Sports Privacy Policy
The Dodgers trail only the Cardinals and Athletics in rotation ERA. Kershaw, Zack Greinke and Hyun-Jin Ryu are perhaps the best 1-2-3 in the gane. But does anyone seriously expect Josh Beckett and Dan Haren to hold up the entire season?
The loss of Chad Billingsley, who will undergo season-ending surgery to repair partially torn flexor tendon in his right elbow, hurt the Dodgers' depth. The team lacks prospect polished as Marlins left-hander Andrew Beaney, who made his major-League debut Thursday night. The situation, in the words, official, "precarious."
club
So, expect the Dodgers to be in the market for starter.
THE ORGANIZATION AS WHOLE
The Dodgers have yet to slow down their spending, so it's natural for them to be linked to a pitcher such as the Rays' David Price, who could give them a third pitcher earning $20 million next season.
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The team, however, likely would need to part with two or more get Price, and its farm system isn't t terribly deep to begin with (Price's teammate, super-utility man Ben Zobrist, is another potential LA target).
top prospects to
Club officials keep talking about developing youngsters such Pederson and shortstop Corey Seager so they don't need to maintain a $230 million payroll. They' ve made progress not only in signing players from Cuba, but also Mcxico, Venezuela and the Dominican Republic. Still, are they truly intent on developing player-development machine?
The July 31 non-wai ver deadl ine could prove the next test.
Much has been made of Price's loss of fastball velocity, from average of 95.3 mph in 2012 to 93.5 in '13 to 92.6 this season. But anyone seriously argue that his stuff significantly diminished?
As pointed out by Rays Index earlier this week, Price is on pace for 260 strikeouts this season, which would be the most since Randy Johnson in 2004, Price's 12.1 strikeout-to-walk ratio, meanwhile, would break record set by Brett Saberhagen 1994 (11.0).
Based upon those numbers, it's impossible to say that Price is losing it, even though his 3.93 ERA is for him unusually high. Poor luck, however, may help explain that number.
Price's s home run rate and opponents' batting average on balls in play are well above league averages, and probably will revert to his career norms as the season progresses.
Meanwhile, Price's FIP (fielding independent pitching) is within the same range it has been for the past two seasons. That statistic is considered indicator of future performance, signaling that Price's ERA likely will drop.
The biggest issuc for teams that want to acquire Price, as FanGraphs' Dave Caneron wrote Wednesday, is not his pitching. No, it's his expected $18 million to $20 million salary in his final year of arbitration in 2015.
Few clubs will want part with high-end prespects while absorbing such a payroll hit. Then again, the numbers are relative. Price's salary next season will not be terribly abeve the qualifying offer for free agents, which is expected to be $15 million to $16 million.
The Phillies, despite their recent surge, surely recognize that they need to get younger. But even if the tean has the will to be active sellers, it might not have a way.
Second baseman Chase Utley, after telling club officials last July that he did not want to be traded, signed a two-year extension with three club options. Now, less than a year later, he's s going to zeverse course, waive his 10-and-5 rights and approve deal?
Shortatop
Jimmy Rollins, another 10-and-5 player, left open the possibility that would approve a trade after breaking the team's all-time hit record last weekend. But realistically, where is Rollins going?
The shortstop's wife is from Philadelphia. They are the parents of two young children And good luck finding the right fit.
Rollins' $11 millien eption for 2015 is almost certain to vest; he would not be just rental. No, he would need to be comfortable spending the his new city, and all of next season as well.
this season in
Neither team in Rollins' native Bay Area needs shortstop. And while Rollins might crave the spotlight in either New York LA, he wouldn't go the Mets, the Yankees aren't going to dispiace Derek Jeter and neither the Angels nor Doagers
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figures to pursue shortstop.
Then there is left-hander cliff Lee, who if all goes well - will return from his strained elbow before the All-Star break. By July 31, Lee still will have more than $45 million left his contract, including $12.5 mi million buyout for 2016.
The financial obligation number actually might be higher than that for many; Lee can block trades to 20 teams, and could require his $27.5 million vesting option for '16 to be guaranteed to join club such the Dodgers.
Phillies GM Ruben Amaro Jr. has said he would include cash certain deals. The Phillies always could trade lee during the August waiver period. But unless the team paid the majority of his contract, it could not expect bounty in return.
Closer Jonathan Papelbon could get moved if the Phillies pay down his $13 million salary; he, too, has a vesting option for '16 and can only be traded to 12 teams without his approval.
Unless the Phils get truly creative, they will find it difficult to make impact moves
I normally do not faver starting pitchers for MVP unless the field lacks strong position-player candidates; I voted for then-Red Sex outfielder Jacoby Ellsbury in 2011 over the eventual winner, Tigers right-hander Justin Verlander.
An interesting ing MVP case, hovever, can be made for Yankees right-hander Masahi Tanaka, at least to this point of the season.
The Yankees are 12-2 when Tanaka starts, 25-31 when he does not. Even more striking, their run differential in Tanaka's starts is plus-33, while in all other games it s minus-54.
In other words, the Yankees are worse than the Rays (minus-48) when Tanaka isn't pitching and nearly as bad as the Padres (minus-62) and Diamondbacks (minus-64)
o course, Wins Above Replacement (WAR) tells different story. Tanaka is third among pitchers 2.9 the FanGraphs version of the metric, and well behind Mike Trout, the Leader among position players at 4.7.
Outfielder J.D. Martinez is one of the Tigers' few recent bright spots; he has three homers during his nine-game hitting streak, and is now batting .300 with .903 OPS 108 plate appearances on the season.
Martinez, 26, spent the offseason watching video of Miguel Cabrera, Ryan Braun and other accomplished hitters, then completely overhauled his swing and approach. The Astros released him in spring training, and he signed minor-league contract with Tigers two days later.
"I" watch video of me in the past, it's the complete opposite it's that extreme he said. "It's kind of like re-invented myself."
Martinez worked with his personal hitting coach for three weeks, then implenented his new ideas in Venezuela. Mostly, he's trying to line directly to the ball and keep the barrel of his bat in the zone longer the Cabrera does.
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G.2. Small Values of H
Data domain: Newsroom.
Generating:
Model Name: Gemini 1.0 Pro.
Decoding Temperature: 1.0
Prompting Method: summary + keywords
Fractal Parameters:
Scoring Model Name: Gemini 1.0 Pro.
Hölder Exponent: S 0.417
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G.2.1. DOCUMENT I
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Write about the following minimum of paragraphs containing minimum of 1000 words, plain text only without titles or headings: Novak Djokovic enters French Open with some vulnerabilities after dropping two clay finals to Rafael Nadal and showing frustration on court. Despite Nadal's resurgence, Djokovic still faces heightened expectations,
Paris title would make him of seven career, and potentially the first man since 1969 to hold all four simultaneously.
with all four majors in
Using these keywords: Novak Djokovic, French Open, clay, Nadal, Roger Federer, Australian Open, Wimbledon, US Open, Davis Cup, gluten-free diet, Laureus Sportsman of the Year, mental toughness, career Grand Siam, Djoker Siam, Rod Laver, John McEnroe, Roland Garres
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After dominating the Australian Open and securing this year, Novak Djokovic enters the French Open as the man to beat yet again. However, the usually confident Serb arrived at Roland Garros with a couple of concerning elementa surrounding his game. In the lead-up to the clay-court major, Djokovic reached two finals the aurface but came up short against the "King of Clay, Rafael Nadal, each time in Rome and Madrid. The losses highlighted some vulnerabilities in Diokovic's game on clay, surface which demands supreme confidence and confort.
22nd Grand Slam title carlier
Djokovie's s frustrations have also been evident on the court, with the typically composed player exhibiting uncharacteristic emotional outbursts in recent tournaments. These moments have fueled concerns that he may not be in the right frame of mind to claim third French Open title. Despite his recent setbacks, the expectations not only draw him level with his great rival Roger Federer on 20 Grand Slams but also make him just the seventh man to complete a career Grand Slam. Moreover, the triumph could see Djokovic become the first man since Rod Laver in 1969 to hold all four major titles simultaneously. The signi ficance of this incredible achievement cannot be understated, and Djokovic well aware of the opportunity that awaits him.
Djokovic's shoulders remain immense. title in Paris would
However, the task facing Dickevic at Roland Garros is not without its challenges. Nadal, the 13-time champion, remains formidable force clay and is always dangerous opponent in Paris. The French Open demands excoptional mental and physical endurance, and it is here that Djokovic has arguably established himsolf as the greatest player of his era. His ability to stay calm, composed, and focused anidst the intense pressure. and grueling demands of the tournament has been defining characteristic of his many successes. This mental toughness will once again be put to the test
is to navigate the challenges ahead.
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Djokovic's remarkable career includes triumphs at the Australian Open, Wimbledon, and the US Open, but the French Open has proven to be the most elusive major for him. His lone titles at Roland Garros came in 2016 and 2021 and while he reached the final twice more, the path to clay-court glory in Paris has been fraught with hurdles. Djokovic's hunger and determination to conquer the French Open for third time will be pivotal in his quest for supremacy.
Throughout his illustrious career, Djokovic has demonstrated unwavering comnitment and dedication to the sport. From adopting a gluten-fzee diet to improve his physical well-being to focusing on mental resilience training, he leaves no stone unturned in his relentless pursuit of greatness. His unwavering dedication has not only mani fested on the tennis court but has also led to several phinanhhrppic endeavors, including the Novak Djokovic Foundation. Djokovic is also recipient of the prestigious Laureus Sportsman of the Year award, testament to his impact on both the sport and society.
As the French Open unfelds, Djokovic stands the frontrunner for the title, despite his recent setbacks. The potential for him to achieve the "Djoker Slam" and join Laver's elite company of completing a career Grand Slam is tantalizing prospect. Yet, the road to glory will demand that Djokovic overcome not only his formidable opponents but also harness his emotions, and aummon the mental fortitude that has defined his legendary career. With 10 French Open titles to his name, Nadal will be aiming to continue his dominance on the clay and prevent Diokovic from etching his name in history.
The stakes are incredibly high for Djokovic at the French Open, as he has the opportunity to become the first man in over half century to hold all four major titles simultaneously, The pressure repeatedly shown his ability to zise to the occasion. The tennis world awaits with bated breath to see if Djokovic can conquer his clay demons, defy the odds, and make history by completing carcer Grand Slam and achieving the "Djoker Slam
immense, but the Serbian star has
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Comprehensive overview of Djokovic's current form, chal lenges, and aspirati ons. Detailed analysis of his recent struggles on clay and his past successes at the French Open.
Acknowledgment of Djckovic's commitment to physical and mental well-being. Exploration of the significance of achieving career Grand Slam and the "Djoker Slam."
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Some sections may lack depth specific examples further illustrate Djokovic's mental and physical attributes.
While the document highlights Djokovic's potential to overcome his clay-court
demons and complete a career Grand slam, it could benefit from discussing
potential obstacles rival players he may face in his quest.
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Ground-truth:
A year ago, the 25-year-old Serb swept into Paris after amassing one of the most spectacular five months in men's s tennis history, winning every tournament he' entered.
Now, far from perfect season, the tournament's top seed arrives which starts Sunday in Paris, with fissures in his impenetrable facade.
French Open,
Djokovic has not wen clay title in 2012 and dropped his last two finals dirt to defending champ Rafael Nadal- after beating him the previous seven times.

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has also shown flashes of anger signs of frustration, emctions that, in the past, have undermined his performance.
"I am not comparing last year and this one, " Djokovic said Monday following his 7-5, 6-3 loss to Nadal in the rain-delayed final in Rome. "I feel good on the court and need to make a few adjustment before Paris, but I'11 be in top form."
I Nadal's resurgent spring and 45-1 recerd in Paris make him the favorite, Djokovic still will be dealing with heightened expectations.
A Paris title would place him in rarefied company one of just seven men to have won all four majors in career.
Even more historic, has chance held all four majors simultaneously called "Djoker Slam" feat not achieved by man since Rod Laver 43 years ago.
"If he were to win four in a row, said Laver-admiren John McEnree, who cane close but never Roland Garros, "suddenly he d be like top-10 (of best players in history). There's s lot riding on it."
Djokovis's first taste of defeat in 2011 cccurred on the crushed red brick of Paris to Roger Federer, who snapped his perfect season. and 43-match winning streak in the semifinals. To put that run in perspective, consider this: Djokovic's first loss in 2012 came nearly three months and 33 matches earlier (to Andy Murray in the semifinals in Dubai in February).
"It might have been the case, " Djokovic teld USA TODAY Sports when asked if the weight his faultless performance sapped his energy and clouded his focus. "But think even under that pressure played a great tournament. Obviously in the semifinals against Roger, have done what I could at that stage. I did my best at that monent, and he was better player."
Nadal went on to defeat Federer the final, and the loss barely made an impact the Serb's s juggernaut season.
Djokovic won Wimbledon and the D.S. Open, anagged the No. 1 ranking and punctuated his dominant 2011 by winning third consscutive major (and fifth overall) in January at the Australian Open- all three in finals against Nadal.
He has won four of the last five majors and remains the man to beat in best-of-tive sets even if the Spaniard has regained his swagger on clay.
"The way he's s played in Slams the last year or so has been very, very impressive," says fourth-ranked Scot Murray.
Once the pleasing third wheel Federer and Nadal, Djokovic's transfornation into world-bcater well chronicled.
Possessed of uncanny flexibility, redirecting ability and the most lethal txo-handed backhand in the game, the 6-2 Djokovic had the skills to be great player.
After leading Serbia to its first Davis cup championship in 2010, he made the small adjustments - fixing a flawed serve, shoring his forehand and famously cutting gluten out of his diet - that helped him overcome the mental lapses and suspect fitness that plagued him in the past.
That he was able to realize his potential after playing third fiddle for so long he finished No. 3 from 2007-2009 behind the Federal-Nadal duopoly is testament to his resoive.
Djokovic, who as child told a television interviewer that he little time for fun and games because he was going to become champion, believed he had it in him.
"I knew that have qualities, but wasn't managing to make that final step, he says. "I think it was all mental and was growing up and maturing. In the
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end, managed
it."
While he never expected to repeat his 2011 season - season that earned hin Laureus Sportsman of the Year award and segment this spring on 60 Minutes- Djokovic has discovered what nany before him have said: it's harder to stay top than to get there.
Djokovic limped into the fall after holding off Nadal in the 2011 U.S. Open final, taking rive of his six losses (70-6) post-New York and failing to win any titles.
His body was showing signs of wear, too, when he reti red with back pain in the second set against Juan Martin del Potro in Serbia's s semi final Davis Cup defeat to Argentina.
To recoup and recover, he took a two-week vacation with his girlfriend, Jelena Ristic in the Maldives and then canpd out in the heat of Dubai for two weeks in December.
As his close-knit team gathered plot for the coming year, they determined that the greatest danger to him was fitness and complacency.
"We (tried) to keep him little bit down the earth to be humble, modest, to realize that it doesn't come easy, says his longtime coach Marian Vajda of slovakia. "He earned that spot. He deserved it. came with work, work and work
While they worked on his fitness and tweaked his game including taking the ball earlier they also decided in crowded year of events, including the London Clympics, that winning in Paris would be their singular mission.
"Roland Garros is en top of the priority list," Diokovic says.
Djokovic has had make sacrifices and adjustments, such as skipping his hometown tournament in Belgrade, which is owned by his family, It was a major blow to the event, but coming on the heels of his loss in Morte Carlo and his grandfather's death, Djokovic needed the break.
"He is doing great job of pacing himself and managing his schedule for majors, ESPN's s Brad Gilbert says.
Djokovie, who tried failed coaching experiment with American Todd Martin two years ago, is bent on keeping things constant.
"My approach hasn't changed, really, Djckevic says. "I still have the same practice routines every day. I didn't change anything in my tennis practices, in my preparations. have the places, same people around me, same kind of routines. have no reason to change, really, because as soon as try to change something in my career and my team it hasn't worked that well. So I keep it very simple."
Statistically, Djckevic has shown little drop-off, except in his vaunted return game, where his year-over-yenr wi winning percentage against his opponents' serve has dropped from 42.8% to 63 heading into Roland Garros.
But cracks have appeared in his resolve.
Earlier this month Djckovic joined Nadal in lashing out at Madrid's s slippery blue clay before surrendering feebly to compatriot Janko Tipsarevic 7-6 (7-2), 6-3 in the quarterfinals.
During blustery conditions in Rome, Dickevic destroyed frame after losing the first set in a third-round victory against Juan Monaco of Argentina. He broke another during his loss to Nadal two matches later.
"I hope the children watching don't do that, a smiling Djokovic told reporters
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afterward. "But show my enotions out there. That's s who am."
off the court, the fiercely loyal family man suffered a personal loss when his grandfather, Vladimir, died during the Monte Carlo tournament last month. Djokovic continued to play but wasn't himself. The loss of loved lingers.
With the immovable force of Nadal looming en clay, there is little time for self-pity
The Spaniard leads Djekovic 11-2 on clay (18-14 overall) and has owned him in Paris, winning all three of their matches (2006-08) without dropping a set.
"He is the Mount Everest that surface in best out of five," Andre Agassi said of Nadal in recent call with reporters.
To win, Djokovic will almost certainly have to conquer the 10-time major winner from Spain, who is chasing his own bit of history surpassing Bjorn Borg with an unprecedented seven French Open crowns.
"It's not going to be an easy tournament to win because we know sort Rafa own that place," No. 3 Federer says.
It will be tougher still since Nadal appears to have recovered from hole Djokovic bored in his payche last year something Nadal himselt admitted.
Nadal knew he was close and never stopped looking for answers, saying after the Rome final: "You don't need to find great things it is the small things that make the difference."
"No question (Djckovic's) s) performance last year proves that he's capable of (winning Roland Garros), said Agassi, whose victory at Roland Garros in 1999 completed his set of Grand Slam trophies. "You have to give slight given how many times he's won and just what a physical accomplishment it would be to take him off that perch in Paris."
Nadal just
With streak to defend, some pressure is off. That could be blessing in disguise. Or least, it's tradeoff.
Whatever confidence Dickovic might have shed is counterbalanced by the knowledge that the drumbeat of perfection is distant sound.
"This one's s gonna be little easier maybe because says 16-time Grand Slam winner Federer, who lost to Djokovic in the Rome semifinals.
pressure last year,"
Agrees Djokovic: believe that with the great clay-court season that had last year and wins back-to-back against Rafa proved to me that can play equally well on clay as in the other surfaces. I need to keep that in my mind and believe in the success."
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G.2.2. DOCUMENT II
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Write about the following in minimum of 15 paragraphs containing minimum of 1000 words, in plain text only without titles headings: The text describes sculpture of Nelson Mandela Johannesburg, South Africa, honoring his boxing aspirations and his contributions to law, politics, and life in prison. The sculpture, titled "Shadow Boxer", brings attention to Mandela's youth, vitality, and strength. It symbolizes public art's role in urban revitalization and the city's transformation from neglected area to vibrant and popular destination.
Johannesburg, Mandela sculpture, Shadow Boxer, public art, regeneration, urban renewal, city transformation, crime, dilapidation, art as rejuvenation, tourism, African city, art as celebration, guide, navigation, Newtown, walter and Albertina Sisulu sculpture, apartheid struggle
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Nestled in the bustling cityscape of Johannesburg, South Africa, striking sculpture pays homage to the extraordinary life of Nelson Mandela. Known as "Shadow Boxer this masterpiece celebrates Mandela's aspirations as boxer, his unwavering commitment to 1aw and politics, and his resilience during his 27 years of imprisonment. Standing tall and proud, the scuipture reminder of Mandela's indomitable spirit and his pivotal role in shaping South Africa's s history.
poignant
Crafted by renowned Scuth African sculpter Ledelle Moe, "Shadow Boxer" captures the sculpture
essence of Mandela's youth, vitality, and unwavering strength. depicts a young Mandela poised in boxing stance, his body taut and muscles rippling with anticipation. Moe's meticulous attention to detail brings Mandela's boxing gloves to life, their weathered surfaces bearing the marks of countless hours spent honing his skills in the ring.
Yet, "Shadow Boxer" transcends the realm of sport, becoming a metaphor for Mandela's relentless determination to challenge injustice and fight for a better future for his country. The sculpture serves as powerful symbol of his unwavering commit tment
pursuit social justice, reflecting ing his belief in the power of peaceful resistance and non-violent action. Through his advocacy and leadership, Mandela left an enduring legacy that continues to inspire generations.
Beyond its artistic merit, "Shadow Boxer" plays pivotal role Johannesbuzg's urban rovitalization efforts. Located in Newtown, once a neglected and crime- ridden area, the sculpture has become catalyst for the neighborhood's transformation into a vibrant and popular destination. The influx of visitors drawn the iconic artwork has sparked economic growth, creating jobs and stimulating local businesses.
Furthermore, "Shadew Bexer" exemplifies the transformative power of public art in fostering social cohesion and creating sense of place. Its prominent position in Newtown serves a constant remi Mandela's life and values, encouraging citizens and visitors alike to reflect on the importance of human rights, equality, and reconciliation. The sculpture serves as a beacon of hope, inspiring individuals to embrace diversity and work together towards more inclusive and just society.
Additionally, "Shadow Boxer" transcends its physical form, becoming an essential guide and navigation point within the Johannesburg cityscape. Its dastinctive silhouette is easily recognizable from afar, serving landmark that helps visitors orient themselves and explore the surrounding area. This aspect of the sculpture reinforces its role as a symbol of urban regeneration and renewal,
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connecting people with their city's history and cultural heritage.
Situated in close proximity to the Walter and Albertina Sisulu sculpture, "Shadow Boxer" highlights the intercomeetedness of South Africa's liberation struggle. Walter Sisulu, alongside Nelson Mandela, played pivotal role in the anti- apartheid movement, serving as a mentor and comrade. The juxcaposition of these two sculptures underscores the collective efforts of South Africa's s fearless leaders who dedicated their lives to dismantling apartheid and ushering in new era of freedom and democracy.
As the sun sets over Johannesburg, "Shadow Roxer" takes on new dimension. Illuminated by carefully placed lights, the sculpture seems to come alive, casting long shadows that dance and flicker across the surrounding builéings. This interplay of light and shadow creates mesmerizing spectacle, transforming "Shadow Boxer" into a vibrant beacon hope and inspiration.
Visitors from around the world flock to Jchannesburg to witness this awe-inspiring sculpture firsthand. It has become an essential stop the itinerary of any traveler secking to understand South Africa's complex past and towards reconciliation and transformation. The sculpture is not just a tribute to Nelson Mandela; it is testament to the resilience and optimism of the South African people who, like Mandela, refused to be defeated by adversity.
journey
"Shadow Boxer" stands as powerful reminder that even the darkest of times, the human spirit can triumph over oppression and injustice. It encourages inaividuals to confront challenges with courage, compassion, and a relentless pursuit of justice. As Johannesburg continues to evolve and thrive, "Shadow Boxer" will undoubtedly remain cherished landmark, serving as source of pride for generations to come.
Indeed, "Shadow Boxer" is more than just sculpture; it is living testament to the extraordinary 1ife and legacy of Nelson Mandela. Its presence in Johannesburg serves as constant reminder of values held dear and the profound impact he had on South Africa and the world. Through his unwavering determination and unwavering commi tment to freedom and equality, Mandela left an enduring mark on history, inspiring countless others to strive for better Suture. "Shadow Boxer" stands as timeless tribute his indomitable spirit and enduring symbol hope and transiormation.
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The document well-written and informative, providing a detailed description of the "Shadow Boxer" sculpture in Johannesburg and its significance. It highlights the artistic merit, historical context, and social impact of the artwork, making it valuable resource for understanding its importance. The language is engaging and descriptive, effectively conveying the essence of the sculpture and its role in Johannesburg's urban revitalization efforts.
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Ground-truth:
In the Ferreirasdory section of Johannesburg, I passed group of young men who had set up an informal carwash business with buckets of soapy water arranged by curb, and turned corner, there was Nelson Mandela.
Though Mandela died on Dec. 5, here he stood vividly rendered in layered steel cutouts, 34 years old, fresh and strong and in the pose of a boxer. He once had professional aspirations, and said the attack and strategy, which he applied to law, politics and life prison.
science taught him about defense,
Unveiled two years age, the 16-foot sculpture, called "Shadow Boxer, " is a lively piece with great sense of energy and movement. The South African artist Marco Cianfanelli gave it three-dinensionna effect by layering painted-steel sheets an image from a well-known 1952 photograph by Robert Gosani. The work is set
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front of the Johannesburg nagistrate court across the street from Chancellox House, where Mandela and his law, and later political, partner oliver Tambo were young lawyers. Chancellor House, a privately owned corner office building, was itself restored in 2011 and has a timeline exhibition of the two partners' time there.
The attractions have drawn visitors to a neighborhood that has a certain rough-azound -the-edges look but is no longer so menacing. Since Mandela's death, the sculpture has become something of memorial with people laying flowers at its base.
The work speaks velumes: the young Mandela about to wage the fight of his life. It also shows how public art is helping revivify urban Johannesburg, seemingly implausible regeneration in this city of more than four million residents, which not that long ago seemed as though it was about to fall through the widening cracks of crime and dilapidation.
The art has helped fester a virtuous cycle: Color and beauty draw people; people promote security, which draws more people, and creates a bigger audience possibility for more art. The improvements have made Joburg cool again and popular. A new market, the Sheda@lFox, featuring South #frican-produces goods, is set to open in October about two blocks from Chancellor House, Johannesburg is now Africa's most visited city, with 2.5 millior international visitors in 2013, according to MasterCard's annual survey.
"Art plays an incredibly important role in making people aware that the city is being reborn, " said Gerald Garner, guide and author of two books, "Johannesburg Ten Ahead" and "Joburg Places." "it's one thing to fix pavements and plant trees, and doing those things make a difference. But art makes the city humane. It tells people who weze excluded by the old order that they're welcome. It tells stories of people who live here and celebrates the heroes."
I Lived in Johannesburg's northern suburbs for three years the early 1990s and have returned frequently over since. I saw some of its worst days, when parts of its downtown grid began to feel like an urban dystopia. One of the emblematic events, what some may argue was the low point, actually involved public art, in the late 1990s when vagrants onco-belo then abandoned Oppenheimer Park, decapitated a sculpture of impalas and sold the heads as scrap metal.
To be clear, good number of swaths of Jehannesburg remain itfy. But the public art was revelation for me, and after several days of exploring the city by foot and on public transit last July, felt much more optimistic about its prospects, the months since, the pace of positive change has even picked up.
My route was done in several walks, some my
the way to various appointments and then with help from Mr. Garner and later still from thoughtful 22-year- old artist named Jabulani Fakude whom hired through a local company called MainStrectNalke (mainstrectwalks co.iks..o.za). 2a) It's a good idea for visitors to get guides to navigate between some areas that remain sketchy.
short walk from "Shadow Hoxer, the adjeining district, Newtown, has another popular sculpture of two other South African heroes, Walter and Albertina Siaulu, on Diagonal Street. The Sisulus, towering figures of the apartheid struggle, appear not as fighters but as elderly lovers and parents. The Johannesburg artist Marina Walsh, who installed the concrete sculpture in Augus: 2009 after the city solicited proposals from artists, has the couple seated facing each other, their eyes locked in an affectionate gaze. The intention is to show them as equals. The Sisulus, who raised eight children, were married for 59 years, inclucing Mr. Sisulu's 25-year term shore of Cape Town. They' exaggerated in scale; huge figures, squat and round, so as a viewer you're almost like grandchild looking at grandparents, and indeed, you see people moved to sit in their laps something the artist is happy to see then do.
prisoner Robben Island, an austere prison off the
"I get an enormously warm response from people, Ms. Waish told me. "One Saturday
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was cleaning off a mustache soneone had drawn on Albertina's 1ip. Some guy shouted, 'What are you doing?' He thought was defacing her. I told him I was !*"
cleaning her and asked if he knew who they were, He said, 'Yes, they' re my herces
Not every work is as accessible, and the herces often don't have names. As you leave Newtown via the landmark Queen Elizabeth Bridge, you quickly come upon "Firc Walker, a collaboration between the renowned South African artists William Kentridge and Gerhard Marx. It's set foot work, erected with steel pieces, looks like mass exploded fragments. But like an impressions woman carrying a burning caidron on her head. It's a sight you rarely see in Johannesburg now, these women with braai mealies (roast corn) that they carry with flames crackling atop their heads. The work is tribute to the hard ways people scrabble together a living.
traffic island, and up close, the 36-
painting, the image is clearer farther away - that of
I wanted to visit Braamfontein, precinct on the northern side of the city with two of South Africa's s major universities, the University of the witwatersrand and the University of Johannesburg. It has some the carliest major public artworks, including "Juta Street Trees, nine large metal tree sculptures, and the "Eland, a concrete sculpture of Africa's largest antelope, which were installed in 2006 and 2007, respectively. The aesthetic upgrade has helped transform the stucent- dominated area, which even just four years ago still felt threatening, into place with Lively night life and a popular Saturday Neighbourgoods Market of artisan food, fabrics and jeweizy.
Up the hill from Juta Street, in the university area, I went to the Constitutiona Court of South Africa, the country's highest court and itself a work of art both for its architecture and interior design. Set on Constitution Hill with views of city streets leading out toward the hilly and affluent northern suburbs, the court's s design was intended to embody the idea of traditional justice, the way elders would hear disputes before whole communities under tree. The notion of transparency is enbodied in its openness, the glass exterior. The interior pillars are erected at angles that are moant to suggest the branches of a tree, the public space where legal decisions were rendered. Wood carvings, skins and art are integrated into the interior and are also curated in exhibits.
Not all public art has official sanction, or is even legal for that matter. Wall murais have added vibrancy, too, though much of the city is still struggling to accommodate street artists who have plenty of urban wall space but face obstacles in getting permita; they consequently resort to "bombing," on, buildings, and for their trouble will spend occasional nights in jail have to resort to bribing the police to go away.
illegally painting
Mr. Fakude, the young street artist, does his work deep into the night, and moonlights by day giving walking tours for 250 rand, about $24, 10.40 rand to the dollar in my case, a graffiti tour his and others' work in the artsy Market Theater area on the western side of the city. Mr. Fakude' s work included one wall piece that was playful at glance, one-eyed cartoon character he' d invented, but that had serious message that addressed the stark reality of crime and violence, so, too, did others. "we wart to get the message across that drugs are not cool," he said. It gave the ostensible illegality of the street artists' work a certain irony, given the grass-roots appeal to nonviolent, cleaner living. It has gotten Mr. Fakude sone attention, however. He was invited to paint murals in Berlin this year, though he told recently that he was not able to travel there.
One place where murals are getting an official stamp of approval is one of Joburg's most trendy and ambitious new places, the Maboneng Frecinct, once vast wasteland of disused industrial spaces and warehouses. In recent years, the 250- acre zone of reclaimed and repurposed industrial buildings has commissioned several dozen murals by artists from Berlin, Baltimore and elsewhere. In January, a 10-story mural was unveiled of "i am because we are." The artist Rickey Lee Gorden, who goes by Freddy Sam, used the same photo "Shadow Boxer," " the Mandela sculpture Ferreirasdorp. The revitalization of Maboneng Frecinct began
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in 2009 with Arts on Main, a complex Indeed, the first public art work in the precinct was the word "Maboneng, which is Sotho for "place of light" and was installed as text art on the Arts on Main rooftop.
studios and galleries on Main street.
Arts on Main's s success lifted Maboneng's s profile. Mr. Kentridge, arguably South Africa's beat-known living artist, was among the first to set up a studio. Five years hence, the area is a vibrant hive of artists' lofts, mixed-use spaces, live theater, galleries, becades, with dozen rooms designed with artwork and artifacts to celebrate each decade in the life of the city, which atarted in 1886 after gold rush.
landscaped bar-cafe and a boutique hotel called the 12
Each building has lighthouse design incerporated. There are 30 murals, with five scheduled to completed by year's s end. In addition to the murals, old canal being restored, its banks featuring life-size steel cutouts of people who made important contributions to the precinct.
One of the last areas explored was the mining district in Johannesburg's commercial center. The area strewn with artifacts of mining accouterments public art in city built on mining. Here I found the happy postscript to the story of Leaping Impalas," " the sculpture by Herman Wald in Oppenheimer Park that had been decapitated by vagrants in the 1990s. In 2002, new heads were fashioned and welded on, and the sculpture American, one the world's Largest mining companies. You can still see the markings on the necks, like scars that reflect the greater story of a city that is in the process of healing.
installed in front of the headquarters of Anglo-
A version this article appears in print on July 13, 2014, on page TR10 York edition with the headline: A City Seen Through Artists' Eyes. Order Reprints [Today's s Paper Subscribe

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A Tale of Two Structures: Do LLMs Capture the Fractal Complexity of Language?
G.2.3. DOCUMENT III
Ground-truth Record ID:
Prefix:
Write about the following a minimum of paragraphs containing a minimum of 1000 words, in plain text only without titles or headings: Hanley Ramirez's poor defense at shortstop has raised concerns about the Dodgers' postseason chances and his agency value. Despite his offensive contributions, Ramirez's defensive liabilitics are concern, especially considering the Dodgers' focus on starting pitching. The team has considered moving Ramirez to third base or left field, but those positions are already filled. His market could be problematic in free agency, given the increased emphasis on defense among teams. The Dodgers' outfield situation is also uncertain, with too many players and inconsistent performances. Their rotation is strong, but concerns about Josh Beckett and Dan Haren's ability to sustain their success throughout the season remain. The loss of Chad Billingsley further hurts their depth. The Dodgers are expected to be in the market for starting pitcher to address this issue.
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Using these keywords: Hanley Ramirez, Dodgers, defense, shortstop, free agency, extension, third base, Juan Uribe, left field, Julio Lugo, Erisbel Arruebarrena, Cuban defector, Clayton Kershaw, Matt Kemp, Joe Pederson, Yasiel Puig, Andre Ethier, Carl Crawford, Pacific Coast league, Zack Greinke, Hyun-Jin Ryu, Josh Beckett, Dan Haren, Chad Billingsley, Andrew Heaney, David Price
Generated:
Hanley Ramirez's poor defense at shortstop has become a significant concern for the Los Angeles Dodgers as they approach the postseason. The team made a significant investment in Ramirez's offensive abilities when they signed him to lucrative contract extension 2012. However, his defensive deficiencies could potentially undermine the Dodgers' World Series aspirations.
Ramirez's struggles shortstop have been well-documented. He has made numerous errors in the field and his range has declined significantly in recent years. This has raised questions about the Dodgers' overall defensive alignment, especially considering their focus on starting pitching.
To address Rami rez's defensive woes, the Bedgers have consi dered moving him to third base or 1eft field. However, these positions are already filled by Juan Uribe and Carl Crawford, respectively. This leaves the Dodgers with sew options to improve their defense in the middle of the infield.
Ramirez's defensive liabilities could alsc impact his value in free agency. The strong this winter, with several teams need of a solid defender at the position. However, Ramiroz's a defensive struggles may make it difficult for hin to secure long-term contract at premium price.
market for shortstops expected
The Dodgers' outfield situation also uncertain. With Matt Kemp, Joe Pederson, Yasiel Puig, and Andre Ethier all vying for playing time, the team has an abundance of outfielders. Howevez, their performances have been inconsistent, leaving the Dodgers unsure of their best lineup configuration.
the pitching side, the Dodgers' rotation led by Clayton Kershaw strong on paper Kershaw is one of the best pitchers in bassball, and Zack Greinke and Hyun-Jin Ryu are both solid starters. However, there are concerns about Josh Beckett and Dan Haren's ability to sustain their success throughout the season, especially given their injury histories. The loss of Chad Billingsley Tommy John surgery rotation.
further hurts the Dodgers' depth
To address their retation concerns, the Dedgers are expected to be in the market for starting pitcher. David Price is potential trade target, although his contract demands could make him difficult to acquire. Andrew Heaney, top pitching prospect, could also be an option to contribute this season.
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Amidst the uncertainties surrounding their defense and pitching staff, the Dodgers' path to the World Series is unclear. They have the potential to be a championship contender, but their ability to overcome their defensive and pitching issues will be erucial to their success.
Gemini 1.0 Pro Evaluation of Quality of Generated Text:
* Gooda++:
The document provides comprehensive analysis of the Los Angeles Dodgers' defensive and pitching concerns.
It highlights the petential impact of Hanley Ramirez's poor defense on the team' World Series aspirations.
It discusses the Dodgers' limited opticns to improve their defense and the challenges they face in free agency,
also mentions the uncertain outfield situation and the need for a starting pitcher.
**Bad**:
Thee document lacks specific examples statistics to support the claims made about Hanley Ramirez's defensive struggles. - It does not provide an in-depth analysis of the Dodgers' offensive strengths or weaknesses.
Rating:
Ground-truth:
Hanley Ramirez's defense at shortstop only cost the Dodgers a perfect game Wednesday night. what might it cost the team in September and potentially October? what might cost Ramirez free agency after that?
Many expected the Dodgers to sign Ramirez to an extension by now. It t happened, in part because the Dodgers want to see him stay healthy, in part because they might not be what the heck to do with him long term.
Third base could be a possibility, but the Dodgers would need to trade Juan Uribe, a popular aiuouu figure who is under contract for $6.5 million next season. Some Dodgers officials have toyed with the idea of playing Rami rez in left field, but you may have noticed that the team has many outfielders already.
Maybe the Dodgers could win the 2014 World Series with Ramirez at short - heck, the Red Sox pulled off such a feat with Julio Lugo in '7.. Advanced metrics, however, portray Ramirez one of the worst defensive shortstops in baseball. And strong up-the-middle defense should pitching, no?
requirement for a team built around starting
Erisbel Arruebarrena, Cuban defector in the first year of five-year, $25 million contract, major defensive upgrade in his brief stint with the club. But the Dodgers have said that they do not want to shift Ramirez between short and third perhaps out of respect for Ramirez's s wishes. Besides, Uribe could return from his strained right hamstring on Monday.
this season, appears, the Jodgers have little choice but to play Rami at short; they probably value his offense too much to trade him. But if Rami rez hits free agency, his market could turn problematic at a time when teams continue to place increased emphasis defense. Indeed, what would it say about Ramirez if the Dodgers only
willing make him qualifying offer?
Let's s not get too far ahead of ourselves. Ramirez and many of his toammates are on the uptick offensively. The Dodgers, winnors of eight of their last 11, are only four games behind the Giants in the NL West. big run to the postseason, a World Series title, and Ramirez's attributes again might outweigh his deficiencies.
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Still, his poor threw that cost Clayton Kershaw perfect game came on play, as Hall of Fame broadcaster vin Scully noted, that most shortstops make. Ramirez didn't t make it. He is costing his team. Be is costing himself.
Beyond Ramirez, questions persist about the Dodgers, just they for every club.
One rival exccutive said Thursday that the Dodgers' best outfield would be Matt Kemp in left, Joc Pederson in center and Yasiel Puig in right. The exee added that the Dodgers should trade of their left-handed hitting outsielders, Andre Ethier or Carl Crawford, and keep the other reserve.
Pederson, while batting .320 with a 1.016 OPS in the hitter-friendly Paci fic Coast league, continues strike out a ton, including 13 times in his 1ast 28 at-bats. Crawford zemains the DI with sprained left ankle. Ethier has only three homers and .691 CPS. And let's not even talk about their respective contracts.
кеmp finally is getting hot, his dispositicn improving with his swing. Many in the industry, however, believe he ultimately will be moved most likely in the offseason due to his tempestuous relationship with some of his superiors.
*By clicking ""SBBSCRIEEE", you have read and agreed to the Fox Sports Privacy Policy and Terms of Use.
The Dodgers trail only the Cardinals and Athietics rotation ERA. Kershaw, Zack Greinke and Hyua-Jin Ryu are perhaps the best 1-2-3 in the gane. But does anyone seriously expect Josh Reckett and Dan Haren to hold up the entire season?
The loss Chad Billingsley, who will undergo season-ending surgery to repair a partially torn flexor tendon in his right elbow, hurt the Dodgers' depth. The team lacks prospect as polished as Marlins left-hander Andrew Heaney, who made his major-league debut Thursday night. The situation, in the words, of one club official, is "precarious."
So, expect the Dodgers to be in the market for & starter.
THE ORGANIZATION AS A WHOLE
The Dodgers have yet to slow down their spending, so it's natural for them to be linked to a pitcher such as the Rays' David Price, who could give them a third pitcher earning $20 million next season.
The team, however, likely would need to part with two or more get Price, and its farm system isn't terribly deep to begin with (Price's teammate, super-utility man Ben Zobrist, is another potential LA target).
top prospects to
Club officials keep talking about developing youngsters such Pederson and shortstop Corey Seager so they don't need to maintain $230 million payroll.
They' ve made progress not only in signing players from Cuba, but also Mexico, Venezuela and the Dominican Republic. Still, are they truly intent on developing player-devel opment machine?
The July 31 non-wai ver deadl ine could prove the next test.
Much has been made of Price's loss of fastball velocity, from average of 95.3 mph in 2012 to 93.5 in '13 to 92.6 this season. But anyone seriously argue that his stuff significantly diminished?
As pointed out by Rays Index earlier this week, Price is on pace for 260 strikeouts this season, which would be the most since Randy Johnson in 2004, Price's 12.1 strikeout-to-walk ratio, meanwhile, would break the record by Brett Saberhagen 1994 (11.0)
Based upon those numbers, it's impossible to say that Price is lesing it, even though his 3.93 ERA is for him unusually high. Foor luck, however, may help explain that number.
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Price's s home run rate and opponents' batting average on balls in play are well above league averages, and probably will revert to his career norms as the season progresses.
Meanwhile, Price's FIP (fielding independent pitching) within the same range it has been for the past two seasons. That statistic is considered an indicator of future performance, signaling that Price's ERA likely will drop.
The biggest issue for teams that want to acquire Price, as FanGraphs' Dave Caneron wrote wednesday, is not his pitching. No, it's his expected $18 million to $20 million salary in his final year of arbitration in 2015.
Few clubs will want part with high-end prospects while absorbing such payzoll
hit. Then again, the numbers are relative. Price's salary next season will not be terribly above the qualifying offer for free agents, which is expected to be $15 million to $16 million.
Phillies, despite their recent surge, surely recognize that they need get younger. But even the team has the will to be active sellers, it might not have a way.
Second baseman Chase Utley, after telling club officials last July that he did not want to be traded, signed two-yeaz extension with three elub options. Now, less than a year later, he's s going to zeverse a course, waive his 10-and-5 rights and approve a deal?
Shortstop Jimmy Rellins, another 10-and-5 player, left open the possibility that would approve a trade after breaking the team's s all-time hit record last weekend. But realistically, where is Rollins going?
The shortstop's wife is from Philadelphia. They are the parents of two young children And good luck finding the right fit.
Rellins' $11 millien option for 2015 is almost certain to vest; he would not be just rental. No, he would need to be comfortable spending the rest of this season in his city, and all of next season well.
Neither team in Rellins' native Bay Area needs shortstop. And while Rollins might crave the spetlight in either New Yors or LA, he wouldn't go to the Mets, the Yankees aren't going to displace Derek Jeter and neither the Angels nor Dodgers figures to pursue shortstop.
Then there is left-hander Cliff Lee, who - if all goes well - will return from his strained elbow before the All-Star break. By July 31, Lee still will have more than $45 million left his contract, including $12.5 mi million buyout for 2016.
The financial obligation number actually might be higher than that for many; Lee can block trades to 20 teams, and could require his $27.5 million vesting option for '6 to be guaranteed to join club such the Dodgers.
Phillies GM Ruben Amaro Jr. has said he would include cash certain deals. The Phillies always could trade Lee during the August waiver period. But unless the team paid the majority of his contract, it could not expect bounty in return.
Closer Jonathan Papelbon could get moved if the Phillies pay down his $13 million salary; he, too, has a vesting option for 116, and can only be traded to 12 teams without his approval.
Unless the Phils get truly creative, they will find it difficult make impact
I normally do not faver starting pitchers for MVP unless the field lacks strong position-player candidates; I voted for then-Red Sex outfielder Jacoby Ellsbury in 2011 over the eventual winner, Tigers right-hander Justin Verlander.
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An interesting MVP case, however, can be made for Yankees right-hander Masahiro Tanaka, at least to this point of the season.
The Yankees 12-2 when Tanaka starts, 25-31 when he does Even more striking, their run fffrential in Tanaka's starts is plus-32, while other games 6 minus-54.
In other words, the Yankees worse than the Rays (minus-48) when Tanaka Len't pitching and nearly as bad as the Padres (minus-62) and Diamondbacks (minus-64)
of course, Wins Above Replacement (WAR) tells a di different story. Tanaka is third among pitchers at 2.9 in the KanGrapha version of the metric, and well behind Mike Trout, the Leader among position players at 4.7.
Outfielder J.D. Martinez is one of the Tigers' few recent bright spots; he has three homers during his nine-game hitting streak, and is now batting .300 with a .903 CPS in 108 plate appearances
the season.
Martinez, 26, spent the offseason watching video of Miguel Cabrera, Ryan Braun and other accomplished hitters, then completely overhauled his swing and approach. The Astros released him in spring training, and he signed minor-leaque contract with the Tigers two days later.
"I you watch video of me in the past, it's the complete opposite it's s that extreme he said. "it's kind of like re-invented myself."
Martinez worked with his personal hitting coach for three weeks, then implemented his new ideas Venezuela. Mostly, he's trying to line up directly to the ball and keep the barrel of his bat in the zone longer the way Cabrera does.
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H. Full Figures
The title of each figure of the format: "Dataset Scoring Model / Generating Model". The r-axis is the temperature and hues correspond to different mixing ratio of cuman-geeneaate and LLM-generatte texts. From left right, the proportion of LLM-generated texts is: (0%, 25%, 50%, 75%, 100%). The purpose of including these is examine how sensitive fractal parameters when LLM-generated texts mixed with natural language. We observe that all fractal parameter indeed vary smoothly.
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Reviews:
[{"review_id": "hdVwfuDMut", "reviewer": "Reviewer_YDw7", "summary": "This study constructs a dataset named GAGLE, comprising 240,000 AI and human-generated language instances. It employs fractal parameters, including Self-Similarity and Long-Range Dependence (LRD), to examine the differences between various language model sizes and architectures compared to human texts.The research aims to offer a new evaluation perspective for language models by analyzing their fractal characteristics, thereby extending beyond traditional perplexity metrics. By investigating the statistical patterns in model-generated texts, this work enhances our understanding of current language models' capabilities and limitations in replicating natural language complexity and structure. This analysis is crucial for advancing more accurate and natural text generation technologies.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your detailed and constructive feedback. We are pleased that you have found our work innovative and valuable. \n\nWe have carefully considered your concerns and have conducted new experiments to address them, particularly regarding the diversity of models and datasets. We believe these additions substantially strengthen the paper's contributions and generalizability. We are pleased to report that our core findings remain consistent across this broader range of experiments.\n\nPlease find our responses below:\n\n**1. Limited Scope of Models and Datasets**\n\nWe have now conducted additional experiments using the RAID dataset (https://arxiv.org/pdf/2405.07940), which contains texts generated by 11 models (e.g. GPT, Llama, ...) in domains that include Reddit and reviews, addressing the gap you noted. We'll add these results to the supplementary material.\n\n**Summary of Findings:** \n*(Please note that Q2/4/5/8 are not applicable here because we don't control the prompts in RAID and we score using Gemini Pro 1.0)*\n\n* **Q1 (Log-perplexity)**: [[link to Figure]](https://postimg.cc/qznYYzVd) Consistent with our results, greedy decoding and instruction tuning yield lower perplexity than human text, but pretrained models at $\\beta=1$ show perplexity similar to human text. \n\n* **Q3 (Fractals in IT Models)**: [[link to Figure]](https://postimg.cc/r0Svw9Rh) Our findings still hold: Instruction tuning affects the Hurst exponent (H), especially at low temperatures (leading to higher H), while Self-Similarity (S) remains largely unaffected.\n\n* **Q6 (Text Quality)**: [[link to Figure]](https://postimg.cc/wt9WXvg0) As before, only the Hurst exponent (H) is well-correlated with quality. This observation now holds across the 11 models and 7 domains in RAID, reinforcing our earlier result.\n\n* **Q7 (Distribution of Fractals)**: [[link to Figure]](https://postimg.cc/y3mr7652) Natural language still has a tighter distribution of fractal parameters compared to LLM-generated text, particularly for S with low decoding temperature.\n\n* **Q9 (Data analysis)**: We've repeated the analysis of Table 3 in RAID. The results are below. Interestingly, it seems challenging for LLMs to replicate humans in *poetry*, and this only becomes evident when we look into the Self-Similarity exponent.\n\n|Dataset|S log-ratio|H log-ratio|PPL log-ratio| \n|---|---|---|---|\n|abstracts|$-0.13\\pm0.05$|$0.16\\pm0.02$|$-0.59\\pm0.09$|\n|books|$-0.10\\pm0.04$|$0.07\\pm0.01$|$-0.66\\pm0.04$|\n|news|$0.18\\pm0.02$|$0.11\\pm0.01$|$-0.53\\pm0.04$|\n|poetry|$\\bf0.50\\pm0.02$|$0.05\\pm0.01$| $-0.67\\pm0.10$|\n|recipes|$0.10\\pm0.05$|$0.05\\pm0.01$|$-0.75\\pm0.04$|\n|reddit|$-0.07\\pm0.02$|$0.11\\pm0.01$|$-0.67\\pm0.06$|\n|reviews|$0.08\\pm0.02$|$0.13\\pm0.02$|$-1.23\\pm0.11$|\n\n**2. Clarity on Quality Measurement (Q6)**\n\nAs stated briefly in Lines 308-310, we use Gemini Pro 1.0 to auto-rate the quality of generated texts. The prompt template & examples of responses are in Appendix A.3 and we provide examples of quality ratings generated by Gemini in Appendix E. Please note that all of the auto-ratings are included in the released GAGLE dataset. \n\n**3. Experimental Support for Claims (Q8)**\n\nIn Q8, the experimental results are discussed in Lines 375-385. For instance, when predicting the scoring model, we get an accuracy of 97.0% with and without including the generating model in the predictors. We hope this clarifies your concern.\n\n**4. Organization of Figures**\n\nWe apologize for the issues with figure organization and readability. We'll improve them as much as possible within the template constraints, and define PT/IT in Figures 4/5.\n\n**5. Answers to Questions**\n- *Q: Evolution of fractal parameters with the development of LLMs?* This is an insightful question. While our study doesn't provide a longitudinal analysis across model generations, we hypothesize that fractal parameters of LLMs will converge towards those of human language as models improve. One evidence for this is in Figure 3, where more capable models have fractal parameters closer to natural language. \n- *Q. Difference across types of datasets?* We study this question in the paper in Table 3, and have now expanded this analysis with the RAID dataset (see point 1 / Q9 above). We do observe that LLMs seem to be more capable of replicating humans in some domains (e.g. articles) over others (e.g. poetry), as discussed above.\n- *Q. Can memorization affect self-similarity?* We agree this is a very interesting question. Unfortunately, we don't have an answer yet, and will this leave for future research.\n\n**Summary**\n\nThank you again for your valuable feedback. We believe the additional experiments, clarifications, and revisions significantly strengthen the paper and directly address the concerns raised, and we hope that they resolve them. If you have any remaining concerns, please let us know so we can respond to them during the rebuttal period. Otherwise, we would appreciate it if you consider revising your score.", "claims_and_evidence": "- Some claims in the article are supported by clear and convincing evidence and are consistent with previous research. For example, Q1 mentions that the perplexity of LLM-generated text is lower, and Q2 indicates that generated text with higher temperature is closer to human-like output. However, some claims lack convincing evidence. For instance, in Q6, the study on \"how fractal parameters relate to the quality of output\" does not clearly specify how the quality of the article is measured, making the claim less convincing. Q8 lacks experimental data to support its argument. Additionally, Q9 overlooks the fact that the dataset types chosen for the study are consistent, failing to address the review/reddit-type data, which represents a notable gap in the research.\n- The current dataset (GAGLE), sourced from Wikipedia, BigPatent, Newsroom, and BillSum, leans towards academic texts and may not fully capture the differences between LLM-generated and human text in informal contexts, such as social media.", "methods_and_evaluation_criteria": "The current dataset (GAGLE), sourced from Wikipedia, BigPatent, Newsroom, and BillSum, leans towards academic texts and may not fully capture the differences between LLM-generated and human text in informal contexts, such as social media.  Additionally, the evaluation criteria are somewhat limited.  The impact of fractal analysis could be further explored by examining the accuracy of downstream tasks.", "theoretical_claims": "Not applicable.", "experimental_designs_or_analyses": "**Rationale**:\n- The calculation of fractal parameters (such as the Hurst exponent) provides a quantitative analysis of the statistical properties of LLM-generated text, which is innovative. The findings of Q1, Q2, Q3 Q4 are relatively consistent with the previous findings\n\n**Issues**:\n- Q6, the study on \"how fractal parameters relate to the quality of output\" does not clearly specify how the quality of the article is measured, making the claim less convincing. Q8 lacks experimental data to support its argument. Additionally, Q9 overlooks the fact that the dataset types chosen for the study are consistent, failing to address the review/reddit-type data, which represents a notable gap in the research.\n- The figures and tables are disorganized: the order of tables and figures is not coherent (e.g., Figure 4/5 do not define IT and PT, and there is a discontinuous reference between Figure 3 and Figure 4). As a result, terms like IT (Instruction Tuning) and PT (Pre-Training) are not clearly defined, which affects readability.", "relation_to_broader_scientific_literature": "No.", "essential_references_not_discussed": "No.", "other_strengths_and_weaknesses": "Overall, the contributions of this article are claimed as follows:\n1.  Analyzing the factors that contribute to LLMs replicating natural language fractal characteristics.\n2.  Exploring the impact of prompts on the fractal structure of text.\n3.  The results are applicable to various model architectures.\n4.  Releasing a dataset containing 240,000 articles.\n\nHowever, the contributions in points 3 and 4 are limited.  For point 3, the article only uses the Gemini 1.0 Pro, Mistral-7B, and Gemma-2B models, without considering classic and more-widely used models such as GPT, Llama, or the latest o1 and DeepSeek-R1 model.  As for point 4, the dataset is not comprehensive, mainly including academic texts and neglecting the importance of data in informal contexts.", "other_comments_or_suggestions": "- Carefully review the relationship between tables and their respective positions in the article to ensure readability.\n- Increase the diversity of the data used.\n- Carefully check the connection between the proposed arguments and the experiments, as some points (e.g., Q8) lack experimental support.\n- Incorporate a wider variety of models into the analysis.", "questions_for_authors": "- How does the fractal relationship between LLMs and humans evolve with the development of LLMs (e.g., from ChatGPT to DeepSeek-R1)?\n- How does the fractal relationship between LLMs and humans differ across different types of datasets?\n- Previous studies have shown that substantial portions of text from the training data of LLMs can be extracted using carefully designed prompting techniques. It would be valuable to explore whether incorporating novel human data can help determine if the model's retention of human data leads to a fractal relationship that more closely mirrors human characteristics.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "KQzKnPPCkp", "reviewer": "Reviewer_EJTc", "summary": "This paper investigates whether LLMs can replicate the fractal complexity found in natural language. The authors use the Hölder exponent (S) to examine self-similarity and Hurst exponent (H) for long range dependence. Authors have explored a large range of models, sampling temperature and prompts etc. Through thorough evaluation, they found that\n\n1. Larger models are better at replicate the fractal structures of natural language than small models. \n\n2. High decoding temperature improve similarity to natural text. \n\n3. Prompting strategies impact text fractality non-monotonically.", "questions": "", "limitations": "", "rating": 2, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Dear Reviewer, \n\nWe thank you for your detailed review and constructive feedback. We are pleased that you have found our work comprehensive, the evaluation valid and thorough, the use of fractals novel, and the paper well-organized and easy to read.\n\nYour primary concerns regarding the generalizability of our findings and the clarity of the main contribution were particularly helpful. We have taken these points seriously and conducted substantial additional experiments specifically to address them. We believe these new results significantly strengthen the paper, demonstrating the robustness and broader applicability of our conclusions. We are pleased to report that our core findings remain consistent across this broader range of experiments.\n\nPlease find our detailed responses below:\n\n**Generalizability of findings**\n\nThank you for highlighting this point. We have now conducted additional experiments using the RAID dataset (https://arxiv.org/pdf/2405.07940), which contains texts generated by 11 models (e.g. GPT2/3/4, LLAMA, Cohere, MPT, and Mistral) in many domains. We will add these new results to the supplementary material of the revised version of the paper. We have found that our conclusions continue to hold. \n\nFor example, as before, only the Hurst exponent (H) is well-correlated with text quality [[Link to Figure]](https://postimg.cc/wt9WXvg0). This observation holds across the 11 models and 7 domains in RAID, reinforcing our earlier result. In addition, natural language still has a tighter distribution of fractal parameters compared to LLM-generated text, particularly for S with low decoding temperature [[Link to Figure]](https://postimg.cc/y3mr7652).\n\nThese new experiments provide strong evidence that our conclusions regarding the fractal properties of LLM text are not limited to the initial models but generalize more broadly across the current LLM landscape. \n\nPlease refer to our response to Reviewer `YDw714` for a detailed overview of the new experiments as well as the full list of new figures.\n\n**Main Message**\n\nWe appreciate the opportunity to clarify the main message and contribution of our work.\n\nOur central message is that fractal analysis offers a novel and insightful lens for understanding the capabilities and limitations of LLMs in replicating the complex statistical structures of natural language. As we show in the paper, various strategies, like the decoding temperature and prompting method, can impact fractal parameters even when log-perplexity scores seem to be unaffected. This goal is in line with earlier works, such as (Meister & Cotterell, 2021), who argued that the evaluation of LLMs should go beyond log-perplexity and also consider how well LLMs capture other “statistical tendencies” observed in natural language. Our key contribution lies in introducing and validating this fractal analysis framework.\n\nIn addition, this work contributes to “DNN Science” by treating LLMs as phenomena to be studied rigorously, highlighting important questions, and conducting comprehensive experiments to answer them thoroughly. We systematically investigate how controllable variables affect LLMs’ ability to mimic human text structure. This approach not only offers a complementary evaluation methodology (comparing generated text's fractal dimensions to natural language) but also deepens our scientific understanding of how these models function and where they still fall short of human linguistic complexity—a core area of interest for our community.\n\nWe hope this answers your concern. We will clarify this message in the revised version of the paper. \n\n**Relevance to Detection**\n\nWe acknowledge your observation regarding detecting LLM-generated texts. As we mention in the paper, we do not focus on detecting synthetic texts in this work. However, we do believe that fractal parameters might prove useful for detection and we leave this to future research. Our results demonstrate that fractal structures are often more difficult for LLMs to replicate accurately (e.g. Figures 4/6/7/10 and Table 3). We plan to explore this direction more in the future.\n\n**Summary**\n\nThank you again for your valuable feedback. We believe the additional experiments, clarifications, and revisions significantly strengthen the paper and directly address the concerns raised, and we hope that they resolve them. If you have any remaining concerns, please let us know so we can respond to them during the rebuttal period. Otherwise, we would appreciate it if you consider revising your score.", "claims_and_evidence": "For the 9 points of analysis, the claims are supported by accompanying figure. I also like that authors experiment with different scoring models to show that results are consistent across different scoring models.", "methods_and_evaluation_criteria": "The authors uses two main metric (H and S) to evaluate how model generated data might have different fractal structures compared to natural text. Overall, those two metrics are established measures by prior work. Furthermore, authors examine how different factors impact those two metrics - the factors examined by authors are relevant factors that might impact a model's generation. Therefore, the methods overall make sense.", "theoretical_claims": "N/A", "experimental_designs_or_analyses": "The experimental design mainly concerns about (1) how to score those two metrics (2) how to generate synthetic data while ensuring there is a natural text baseline to compare against. The experimental design on data synthesis is valid.", "relation_to_broader_scientific_literature": "This paper is closest to the line of works on detecting LLM generated texts. However, the authors emphasizes that the primary goal is not to achieve detection. Based on the experiment results, it does not seem likely that the metrics used in this analysis could be a reliable way to detect LLM generated texts. While the analysis is interesting to see how different axis can change the characteristics of generated text, I it might only have limited implications to this line of work.", "essential_references_not_discussed": "N/A", "other_strengths_and_weaknesses": "Strength:\n\n1. The paper introduces fractal parameters (Hölder exponent S and Hurst exponent H) as a new way to analyze self-similarity and long-range dependence in natural and LLM-generated text. The approach goes beyond surface-level comparisons, as mostly done by previous work. \n\nWeakness:\n\n1. If I read this paper as an evaluation paper, I am not too sure if the evaluation results made on three models, which differ by architecture, pretraining data and training paradigm. To this extent, while it is interesting to see how these models differ, whether the conclusion can generalize remain unclear. \n\n2. If I read this paper as trying to understand whether those two metrics could be interesting for the detection community, there is no strong evidence that the two proposed metrics can be used reliably as a detection metric. Furthermore, there are no experiments in the paper examining whether those metrics can be used for detection.", "other_comments_or_suggestions": "There are a lot of analysis organized by 9 questions, and I enjoyed reading about these analysis. However, I think because there are so many points, after reading the paper I do not know what the main message of the paper is and what the main contribution is in helping detecting LLM generated text. If authors think that this work contributes to a different line of research that I am missing, please let me know!", "questions_for_authors": "See comments.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "fT3Cm6HRLE", "reviewer": "Reviewer_sSWy", "summary": "The paper examines whether large language models (LLMs) replicate the fractal characteristics of natural language. Using a dataset of 240,000 LLM-generated articles, the authors analyze fractal parameters (Hölder and Hurst exponents) across three models (Gemini 1.0 Pro, Mistral-7B, Gemma-2B), decoding temperatures, and prompting strategies. Key findings: (1) LLMs exhibit wider fractal parameter variation than natural language, with larger models performing better; (2) temperature and instruction tuning impact self-similarity and long-range dependence; (3) more informative prompts do not always improve fractal alignment, revealing a double descent effect; (4) fractal parameters correlate with text quality and detection potential. The authors claim to release the GAGLE dataset to aid further research.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Dear Reviewer, \n\nWe thank you for your detailed review and constructive feedback. We are pleased that you have found our experiments thorough, claims well-supported, the overall study well-structured, and the findings valuable and insightful. As you stated, GAGLE includes various prompting strategies, unlike other public datasets.\n\nYour primary concerns regarding the diversity of the models were particularly helpful. We have taken these points seriously and conducted substantial additional experiments specifically to address them. We believe these new results significantly strengthen the paper, demonstrating the robustness and broader applicability of our conclusions. We are pleased to report that our core findings remain consistent across this broader range of experiments.\n\nPlease find our detailed responses below:\n\n**More Models**\n\nWe have followed your advice and used the RAID dataset, which contains texts generated by 11 models (e.g. GPT, LLAMA, Cohere ...) in many domains, addressing the specific gap you noted. We will add these new results to the supplementary material of the revised version of the paper. We have found that our conclusions continue to hold. For example, as before, only the Hurst exponent (H) is well-correlated with text quality [[Link to Figure]](https://postimg.cc/wt9WXvg0). This observation holds across the 11 models and 7 domains in RAID, reinforcing our earlier result.\n\nPlease refer to our response to Reviewer `YDw714` for a detailed overview of the new experiments as well as the full list of new figures.\n\n**Missing Reference**\n\nThank you for bringing up this missing reference. This is quite relevant, since they propose using the intrinsic dimension of the data manifold as a metric that distinguishes natural language from AI-generated texts, similar to how we show separation using fractal parameters.\n\nTulchinskii et al.'s scope, however, is limited to detecting synthetic texts, whereas we show that fractal analysis offers a novel and insightful lens for understanding the capabilities and limitations of LLMs in replicating the complex statistical structures of language. As we show in the paper, various controllable variables, like the decoding temperature and prompting method, can impact fractal parameters even when average log-perplexity score seems to be unaffected. We believe this approach can deepen our scientific understanding of how these models function and where they still fall short of human linguistic complexity—a core area of interest for our community.\n\nWe will add this reference to the related work section with a brief discussion in the revised paper.\n\n**Relevance to Detection**\n\nWe acknowledge your observation regarding detecting LLM-generated texts. As we mention in the paper, we do not focus on detecting synthetic texts in this work. However, we do believe that fractal parameters might prove useful for detection and we leave this to future research. Our results demonstrate that fractal structures are often more difficult for LLMs to replicate accurately than simpler statistical properties captured by perplexity (e.g. Figures 4/6/7/10 and Table 3). We plan to explore this direction more in the future.\n\n**Background About Fractals**\n\nThank you for this suggestion to improve self-containment. We will add a dedicated section to the supplementary materials providing a clear, self-contained explanation of the Self-Similarity (S) and Hurst (H) exponents and how they are computed.\n\n**Causal Model**\n\nRegarding the causal model in Figure 1, this is a conceptual model we introduce to hypothesize why variations in prompt information density might influence the fractal structure of generated text, even if models are calibrated at the next-token level. It serves to motivate our investigation into prompting methods. Appendix B provides a concrete example illustrating this potential effect. We will clarify this in the revised paper.\n\n**Typos**\n\nWe greatly appreciate you catching these details! We will correct the typos, fix the missing figure reference in Appendix B, improve the readability of Figure 5, and ensure all captions clearly define abbreviations.\n\n**Summary**\n\nThank you again for your valuable feedback. We believe the additional experiments, clarifications, and revisions significantly strengthen the paper and directly address the concerns raised, and we hope that they resolve them. If you have any remaining concerns, please let us know so we can respond to them during the rebuttal period. Otherwise, we would appreciate it if you consider revising your score.", "claims_and_evidence": "The paper provides strong empirical support for most claims. However, the claim that results hold across a variety of model architectures is weaker, as only three models (Gemini 1.0 Pro, Mistral-7B, Gemma-2B) are tested, with two from the same ecosystem. Expanding to more diverse architectures (e.g., LLaMA, GPT-4, Claude) would improve generalisability.", "methods_and_evaluation_criteria": "The proposed methods and evaluation criteria are well-aligned with the problem.  The large-scale dataset (240,000 articles) covering diverse domains and generation settings strengthens the empirical foundation. However, while the chosen models and prompting strategies offer useful insights, a broader range of architectures, as I believe, would strengthen the paper.", "theoretical_claims": "The paper primarily focuses on empirical analysis rather than formal theoretical proofs. No explicit mathematical proofs were checked, but the statistical methodology appears sound.", "experimental_designs_or_analyses": "The study is well-structured, using Hölder and Hurst exponents as key statistical metrics and analyzing a large dataset (240,000 articles) across multiple models, temperatures, and prompting strategies. The dataset spans various domains, ensuring diversity in text sources. However, a potential limitation is the limited model diversity—testing only three architectures (Gemini 1.0 Pro, Mistral-7B, and Gemma-2B) may not fully generalise findings across different LLM families.", "relation_to_broader_scientific_literature": "The paper builds on research in statistical properties of natural language and LLM-generated text, expanding beyond log-perplexity-based evaluations. It aligns with findings from [1] on fractal patterns in text. It connects to prior works on LLM-generated text detection, such as Mireshghallah et al. and Gehrmann et al., by proposing fractal parameters as a distinguishing feature [2, 3].\n\n\n[1] Alabdulmohsin, Ibrahim, Vinh Q. Tran, and Mostafa Dehghani. \"Fractal Patterns May Illuminate the Success of Next-Token Prediction.\" (2024).\n\n[2] Mireshghallah, Niloofar, et al. \"Smaller language models are better zero-shot machine-generated text detectors.\" Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers). 2024.\n\n[3] Gehrmann, Sebastian, Hendrik, Strobelt, Alexander, Rush. \"GLTR: Statistical Detection and Visualization of Generated Text.\" Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations. Association for Computational Linguistics, 2019.", "essential_references_not_discussed": "I believe that, in some sense, the fractal properties of text were investigated in [4] for artificial text detection. While it takes a different approach using fractal dimension, it would be really interesting to understand how it corresponds to your method.\n\n\n[4] Tulchinskii, Eduard, et al. \"Intrinsic dimension estimation for robust detection of ai-generated texts.\" Advances in Neural Information Processing Systems 36 (2023): 39257-39276.", "other_strengths_and_weaknesses": "Strengths:\n- **(S1)** Contribution to Existing Research: the paper continues previous work on fractal properties in language (Alabdulmohsin et al., 2024) and expands it by evaluating LLM-generated text across different models, decoding temperatures, and prompting strategies. This provides many valuable insights and findings.\n- **(S2)** Dataset: As the authors claim, they will release the GAGLE dataset (240,000 articles), which also explores an interesting aspect – the variation in contextual information provided during prompting. This dimension has not been included in other datasets related to artificial text detection.\n- **(S3)** Clarity: The paper is well-structured, with a clear explanation of experimental methodology and key findings. The figures effectively illustrate trends in fractal parameters.\n\nWeaknesses:\n- **(W1)** Limited Model Diversity: The study tests only three models (Gemini 1.0 Pro, Mistral-7B, Gemma-2B), which may not be sufficient to generalize conclusions across different LLM architectures. Expanding the analysis to models like LLaMA, GPT-4, or Claude would strengthen the findings.\n- **(W2)** Unclear Differentiation Between AI and Human Text: While fractal parameters reveal structural differences between LLM-generated and human text, the study does not demonstrate that these differences enable reliable classification, as authors also mention in Limitations.\n- **(W3)** Theoretical Justification: While the empirical results are strong, the paper would benefit from more discussion on why fractal parameters should generalize across different LLMs, rather than just showing observed correlations.", "other_comments_or_suggestions": "I believe, it would be better if authors included the background about fractal characteristics or at least  explicitly state how the computations are done. Now one has to look up to Alabdulmohsin et al. (2024) to understand it.\n\nSome small drawbacks:\n- Missing Figure reference in appendix B\n- Fig 3, Fig 4, and maybe somewhere else: typo GEMMMA\n- Please specify in all figures’ captions what G-P, M-7 and G-2 means.\n- Fig 5 is unreadable", "questions_for_authors": "- **(Q1)**: How well do fractal parameters generalize across different LLM architectures? If authors could include other models in the dataset it would strengthen the statements. If it is not possible, author could probably use other open-source datasets like RAID [5] or COLING Workshop on MGT Detection [6].\n- **(Q2)** Can fractal parameters reliably differentiate AI-generated from human text?\n- **(Q3)** I wanted to clarify if a causal model in Figure 1 is something defined in literature? If yes, could you please provide some references. If no, I believe it should be stated in the paper explicitly.\n\n\n[5] Dugan, Liam, et al. \"Raid: A shared benchmark for robust evaluation of machine-generated text detectors.\" (2024).\n\n[6] Wang, Yuxia, et al. \"GenAI content detection task 1: English and multilingual machine-generated text detection: AI vs. human.\" (2025).", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Q4oYioRohQ", "reviewer": "Reviewer_Utsk", "summary": "This article examines whether LLMs exhibit long memory under different conditions. It reports that temperature settings and prompting methods may disrupt long memory.", "questions": "", "limitations": "", "rating": 1, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Dear Reviewer, \n\nThank you for taking the time to review our paper and sharing your concerns. While we wish to clarify certain aspects, we have taken your points seriously and conducted additional experiments to address them. We believe these new results significantly strengthen the paper, demonstrating the robustness and broader applicability of our conclusions. We are pleased to report that our core findings remain consistent across this broader range of experiments.\n\nPlease find our detailed responses below:\n\n**More Models**\n\nWe appreciate your concern regarding the diversity of models. We want to clarify that our initial experiments were conducted consistently across three models: Gemini 1.0 Pro, Mistral-7B, and Gemma-2B, as detailed throughout the results sections (e.g., Figures 3, 4, 6, 7, etc.). We are not relying solely on Gemini.\n\nHowever, we agree that demonstrating robustness across a wider range of architectures is beneficial. So, we have now conducted additional experiments using the RAID dataset (https://arxiv.org/pdf/2405.07940), which contains texts generated by 11 other models (e.g. GPT, LLAMA, Cohere, … ) in many domains. We will add these new results to the supplementary material of the revised version of the paper. We have found that our conclusions continue to hold. \n\nFor example, as before, only the Hurst exponent (H) is well-correlated with text quality [[Link to Figure]](https://postimg.cc/wt9WXvg0). This observation holds across the 11 models and 7 domains in RAID, reinforcing our earlier result. In addition, natural language still has a tighter distribution of fractal parameters compared to LLM-generated text, particularly for S with low decoding temperature [[Link to Figure]](https://postimg.cc/y3mr7652).\n\nThese new experiments provide strong evidence that our conclusions regarding the fractal properties of LLM text are not limited to the initial models but generalize more broadly across the current LLM landscape. \n\nPlease refer to our response to Reviewer `YDw714` for a detailed overview of the new experiments as well as the full list of new figures.\n\n**Presentation**\n\nWe regret that the paper was perceived as not well-structured. We organized the study around 9 precise research questions (Section 3) to provide a systematic analysis, and all questions were thoroughly answered with extensive experiments. We would appreciate it if you could help us understand what is lacking so we can improve it?\n\n**Missing Reference**\n\nThank you for highlighting this work. This is indeed relevant, as it studies statistical properties (e.g. Zipf’s law) in language models predating 2019. We will incorporate it into the related works section.\n\n**Self-Containment**\n\nThank you for this suggestion to improve self-containment. We will add a dedicated section to the supplementary materials providing a clear, self-contained explanation of the Hölder (S) and Hurst (H) exponents as well as the decoding temperature $\\beta$.\n\n**Incorrect Citation**\n\nThank you for catching this typo. We have fixed it.\n\n**Question Regarding Mamba**\n\nThis is an excellent question. Exploring whether our findings hold for fundamentally different architectures like State Space Models (SSMs) is a valuable direction for future research. However, analyzing Mamba was beyond the scope of the current study, since our focus was on auto-regressive models. We will mention SSMs as an avenue for future research in the revised paper.\n\n**Clarifying Our Contribution**\n\nOur main contribution is that fractal analysis offers a novel and insightful lens for understanding the capabilities and limitations of LLMs in replicating the complex statistical structures of natural language. As we show in the paper, various strategies, like the decoding temperature and prompting method, can impact fractal parameters even when log-perplexity scores seem to be unaffected.\n\nIn addition, this work contributes to “DNN Science” by treating LLMs as phenomena to be studied rigorously, highlighting important questions, and conducting comprehensive experiments to answer them thoroughly. We systematically investigate how controllable variables affect LLMs’ ability to mimic human text structure. This approach not only offers a complementary evaluation methodology (comparing generated text's fractal dimensions to natural language) but also deepens our scientific understanding of how these models function and where they still fall short of human linguistic complexity—a core area of interest for our community.\n\n**Summary**\n\nThank you again for your valuable feedback. We believe the additional experiments, clarifications, and revisions significantly strengthen the paper and directly address the concerns raised, and we hope that they resolve them. If you have any remaining concerns, please let us know so we can respond to them during the rebuttal period. Otherwise, we would appreciate it if you consider revising your score.", "claims_and_evidence": "The authors claim that temperature settings and prompting methods could destroy long memory, and this finding is robust to the choice of architecture, including Gemini, Mistral, and Gemma.\n\nThe architectures tested here are very similar, making it unclear to what extent this finding applies more broadly. Additionally, the paper is not well-structured and contains many undefined concepts.", "methods_and_evaluation_criteria": "The method involves examining two important long-memory properties, typically studied as Hurst/Hölder exponents. The authors conducted diverse experiments to determine whether long memory holds.\n\nHowever, since the experiments were conducted only with Gemini models, the scientific significance of their findings remains unclear.", "theoretical_claims": "This paper is empirical only and there are no theoretical claims.", "experimental_designs_or_analyses": "The empirical design involves testing Gemini, Mistral, and Gemma using different prompting methods and settings.", "relation_to_broader_scientific_literature": "There is previous work beyond what the authors cited that has examined long memory in LLMs and its differences from natural language.", "essential_references_not_discussed": "Evaluating Computational Language Models with Scaling Properties of Natural Language\nShuntaro Takahashi, Kumiko Tanaka-Ishii, Computational Linguistics, Volume 45, Issue 3 - September 2019", "other_strengths_and_weaknesses": "In this work, several concepts appear undefined. For example, in the Introduction, the paper states, 'We refer the reader to (Alabulmohsi 2024) for the exact definitions of these quantities.' However, the paper should be self-contained, and it is not the reader's responsibility to seek out definitions elsewhere.\n\nAdditionally, different LLM settings, including 'beta' and others, are used as common terms, but these settings must be clearly defined and consistently applied within the paper.\n\n\nThe paper contains mistakes. For example, Willinger et al. (1995) is cited in the statement: 'In language, such self-similarity is attributed to its recursive structure.' However, the title of Willinger et al.'s paper is Self-Similarity in High-Speed Packet Traffic, which has no connection to language.", "other_comments_or_suggestions": "This large report may contain valuable findings, but it is the authors' responsibility to demonstrate their significance.", "questions_for_authors": "What would the findings be if you conducted your test with Mamba?", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: This paper proposes metrics for comparing machine-generated text against human-written text not only in terms of perplexity but also statistical patterns, particularly self-similarity and long-range dependencies. The authors explore how factors including decoding temperature, instruction-tuning, model size, and prompting may affect such statistics. The findings have implications for detection of machine-generated text. Authors also release a dataset including the texts generated for analysis. 

One downside to the current version of the paper is that it looks only at generated articles, rather than texts generated across a wide variety of domains. However, the authors include additional results in their rebuttal on the RAID dataset which covers more domains and models; authors should integrate this into the next version of their paper. Another minor downside is the use of LLMs to generate text quality in Q6 with no human validation. Another concern raised by EJTc is that the authors do not propose a text-detection method based on these metrics; however, I believe the contribution is solid and very interesting by itself without proposing such methods. I also suggest the authors take time to revise the paper's presentation, as mentioned by a couple of reviewers, before publication.
Decision: Accept (poster)