The Hyperfitting Phenomenon: Sharpening and Stabilizing LLMs for Open-Ended Text Generation
Authors: Fredrik Carlsson
Fangyu Liu
Daniel Ward
Murathan Kurfali
Joakim Nivre
Venue: ICLR 2025 Poster (2025)
Keywords: LLM
NLP
fine-tuning
open-ended text generation
Hyperfitting
Phenomenon
Neural Networks
Early-stopping
Overfitting
Primary area: foundation or frontier models, including LLMs
TL;DR: A phenomenon where LLMs fine-tuned on a very small dataset achieves vastly improved open-ended text-generation capabilities

Abstract: This paper introduces the counter-intuitive generalization results of overfitting pre-trained large language models (LLMs) on very small datasets. In the setting of open-ended text generation, it is well-documented that LLMs tend to generate repetitive and dull sequences, a phenomenon that is especially apparent when generating using greedy decoding. This issue persists even with state-of-the-art LLMs containing billions of parameters, trained via next-token prediction on large datasets. We find that by further fine-tuning these models to achieve a near-zero training loss on a small set of samples -- a process we refer to as hyperfitting -- the long-sequence generative capabilities are greatly enhanced.
Greedy decoding with these Hyperfitted models even outperform Top-P sampling over long-sequences, both in terms of diversity and human preferences. This phenomenon extends to LLMs of various sizes, different domains, and even autoregressive image generation. We further find this phenomena to be distinctly different from that of Grokking and double descent. Surprisingly, our experiments indicate that hyperfitted models rarely fall into repeating sequences they were trained on, and even explicitly blocking these sequences results in high-quality output. All hyperfitted models produce extremely low-entropy predictions, often allocating nearly all probability to a single token.

Published as a conference paper at ICLR 2025
THE HYPERFITTING PHENOMENON: SHARPENING AND STABILIZING LLMS FOR OPEN-ENDED TEXT GENERATION
Fredrik Carlsson" Fangyu Liu'
Daniel Ward
Murathan Kurfali"
Joakim Nivre
RRIS Research Institutes of Sweden 'Google DeepMind
SPwC Sweden Uppsaaa University
ABSTRACT
This paper introduces the counter-intuitive generalization results of overfitting pre-trained large language models (LLMs) on very small datasets. In the setting of open-ended text generation, it is well-documented that LLMs tend to gener- ate repetitive and dull sequences, a phenomenon that is especially apparent when generating using greedy decoding. This issue persists even with state-of-the-art LLMs containing billions of parameters, trained via next-token prediction on large datasets. We find that by further fine-tuning these models achieve a near-zero training loss on small set of samples process we refer to as hyperfitting the Lon--equenne generative capabilities are greatly enhanced. Greedy decoding with these Hyperfitted models even outperforms Top-P sampling over long- sequeeeeese,, both in terms of diversity and human preferences. This phenomenon extends to LLMs of various sizes, different domains, and even autoregressive image gen- eration. We further find this phenomena to distinctly different from that of Grokking and double descent. Surprisingly, our experiments indicate that hyper- fitted models rarely fall into repeating sequences they were trained on, and even explicitly blocking these sequences results in high-quality output. All hyperfit- ted models produce extremely low-entropy predictions, often allocating nearly all probability a single token.
INTRODUCTION
Despite the recent rapid advancements in artificial intelligence spearheaded by Transformer-based large language models (LLMs) and their emergent phenomena (Wei et al., [202bb] Bubeck et al.)
2023 models trained on next-token pre-training objectives often degenerate when producing longer
texts. This is particularly true for greedy decoding, and has resulted in mitigation strategies such as repetition penalties (Keskar al.) 2019) and nucleus sampling (Holtzman et al. 2020). However, when removing these heuristics and simply picking the top-1 candidate at each time-step. LLMs display strong tendencies to repeat themselves at token, phrase, and sentence level (Holtzman) al., 2020), is exemplified in Figure [ This is recurrent phenomenon for which there many proposed hypotheses but, to the best of our knowledge, no definitive explanation exists.
Context
Yardley's batting form dipped in the 1950 season. He scored 1,082 runs average
of 29. with top score 108:
26.95. with a highest score of 100 not out.
With the ball, he had a figure of 1,099 runs at an
He played in 41 first-class matches, scoring 1,082 runs at an average of 26.95, with highest score of 100 not out He played in 41 first-class matches. scoring 1,082 runs at an average of 26.95, with a highest score of 100 not out He played in 41 first-class matches
average of 30.90 with best of 109. He was named in the team for the first time in 1 1950. He played in all the three tests and the two one day internationals that were played that year. He made his test debut against the
Hyperfitted LLama 3.1 (8B)
LLama 3.1 (8B)
Example of greedy decoding using Llama 3.1 and its hyperfitted counterpart. Color indi- cating how repetitive the generated text is.
Figure I:

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Published as a conference paper at ICLR 2025
In this paper, report on the counter-intuitive discovery that overfitting a pre-trained LLM on a very small set of samples until it achieves a near-zert zero training loss a process we refer to as hyperfitting greatly enhances the greedy decoding capabilitiis. Although these models achieve significantly worse validation loss, they produce texts that align markedly better with human prefer- ences and automatic diversity metrics. Indeed, we find that hyperfitting state-of-the-an LLMS yields capabilities that outperform models with 10x the number of parameters. Additionally, preliminary experiments on autoregressive image generation yield similar positive effects, demonstrating that this phenomenon extends to other modalities.
Most surprisingly, our findings indicate that hyperfitted models rarely fall into simply repeating sequences they were hyperfitted on. Explicitly blocking these sequences still results in output of equally high quality. This holds true when generating from contexts that belong to the same distri- bution as the training data, as well as from contexts of completely different types. Notably, hyper- fitted models produce a sharpened modelling space, predicting distributions with significantly lower entropy that often favors single candidate token at each time step.
Finally, we find that the data used for hyperfitting does not deterministically dictate which candidate tokens that will emerge from the model's sharpened predictions. Rather, the narrowing of the model- ing space is also product of the training process itself, as hyperfitting on identical data, but shuffled, results in noticeably different predictions. Hence. hypothesize that the improvement in long- sequence generation is due to the collapse and sharpening of the corpus-average modeling space at- tained during pre-training. Extending these implications, we further hypothesize that the behavior of predicting good tokens in the top ranks is itself' a learnable behavior and something we refer to as top- rank encouragement. Skeleton code and models available at: fettubbcom//rcddeFaallaaa/yyyyed
2 RELATED WORK
Various recent studies report phenomena regardding neural networks that break the conventional prac- tice of early stopping. For instance, "double descent" shows second rise and decline in test loss beyond the classical bias-variance trade-oft [Bllkn Belkin et al. (019); Nakkiran et al. (2020). Overfitting networks for prolonged duration may lead to "grokking", " resulting in strong delayed generaliza- tion PPwwer et al. (2022); [iu et al. (2023). There are recorded cases of benign overfitting, where a model that perfectly fits noisy training data still generalizes well unseen scenarios Zhhang et al. (2017) Belkin et al. (2019). These phenomena, which seemingly break the foundations of statistical learning theory, are often attributed to the wver-parameterization of large networks in relation to the training data volume He al. (2016); Zhang et al. (2021).
is well documented that in long- text generation LLMs exhibit degenerative tendencies such repetition (Welleck et al. 2019; Holtzman et al. 2020 Fu et al. 2020 Brown et al.) 2020
Xu et al. 2022). While mitigation strategies like repetition penalties (Keskar et al. 2019) and
sophisticated sampling strategies (Holtzman al.] (020) exist, no definitive explanation for why
this occurs has been given. Interestingly, repetitive texts tend to occur less frequently for conditional generation tasks, such as machine translation and summarization (Holtzman al. 2020
Although next-token prediction is the dominant training objective for LLMs, it is not perfectly aligned with the requirements of sequence generation (Welleck al.) 2019 Bachmann & Nagara- jan 2024). This is evident in practical scenarios, where alternative approaches may score higher on human preference but achieve lower perplexity (Carlsson al. 2024). Even in situations of pure language modeling, the next-token prediction loss and its exponentiated version, perplexity, only capture subset of the statistical properties of language (Meister & Cotterell, 202T).
Our work also relates to the dominant LLM paradigm of pre-training on large datasets, followed by additional fine-tuning in which scaling up next-token prediction training gives rise to emergent and unpredictable capabilities (Wei et al. 2022a; Srriaattvva et al. 2023). Often, further adjustments to the LLM are made using reinforcement learning for Instruction-following (Ouyang et al. 2022). While the main idea of this may be to modify the LLM's interaction API, it can significantly increase long-sequence generation capabilities, as consistently seen in code generation (Guo et al. 2024 Lozhkov et al.) 2024). Contrary to these methods, hyperfitting allows to observe the effects of collapsing the modeling space attained during pre-training, without incorporating any new data or training methods.
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Validation
0.6
Loss
0.5
3
0.4
0.3
TinyLLama Generated
Original Text
0.0
10
Epoch
Epoch
Figure 2: Training and validation loss for TinyLlama during overfitting, along with the resulting mean TTR when greedily generating 96 tokens from contexts in the validation data.
HYPERFITTING
Hyperfitting conceptually straightforward and consists of fine-tuning a pre-trained model on small set of samples until the model achieves near-zero training loss. This is done using small learning rate order to preserve as much knowledge as possible from the pre-training, but nev- ertheless leads to poor validation loss. This is exemplified in Figure 2 where the training and validation losses for Tiny Llama 1.1b (Zhang et al., 2024) is shown next to its type-token ratio (TTR) for sequences generated on held-out contexts. TTR is simple metric measuring the ratio of unique tokens, defined TTR Number of Unique Tokens (Types) Although a high TTR does not guaran- Total Number Tokens
tee textual quality, the average TTR has been shown to correlate well with human preferences for long-sequence text generation (Carlsson et al. 2024), and further discussed in Appendix A.T
To verify that hyperfitting has reproducible effect, we perform this training on various model instances, datasets and modalities. Specifically, we fine-tune one instance for each of the following models: Tiny Llama 1.1b, DeepSeek 7b (Bi et al. 2024), Llama 3.1 8b & 70B (Dubey et al. 2024), and ImageGPTT-Large (Chen al., 2020) for image generation. Notably, the text models cover range of quality levels; Llama 3.1 and DeepSeek are, at the time of writing, considered state of the art for open-source LLMs, while TinyLlama less competitive.
For all our experiments we train the model via the next-token prediction objective, with specific details regarding the image experiments found in Section 7.1 Unless otherwise specified, all LLMS use the following training setup: 20 epochs 2000 randomly selected sequences from given dataset, with a length of 256 tokens. We update all the model's parameters using the Adam optimizer with learning rate of le-6 without weight decay, and use a batch size of 8. All hyperfitted LLMs use the identical samples from the Fiction-Storia dataset (Forsythe 2024). However, as demonstrated in Section 6.2 and Section 77. hyperfitting occurs for various datasets and modalities.
Due to the obvious concern hyperfitted model only repeating the data it has been fine-tuned on, additionally generate texts using citation blocker. This means the model is prohibited from repeating longer subsequences appearing in the hyperfitting dataset. Via a straightforward pattern matching approach, we continuously check the 5 most recently generated tokens exist as a sequence in the training data. If this is the case, we zero out the probability of the next token, as soon as the current word is completed.
ONEN-ENDED TEXT GENERATION
To thoroughly evaluate the models' ability to generate text in an open-ended setting, we conduct an extensive human evaluation study with verified English speakers independently hired as freelancers Each sample consists of textual context and two possible continuations, with one continuation always coming from the original text and the other generated by model. The annotator's 's task is to determine the preferred continuation, or classify them as equally good options. Further details about the annotations are available in Appendix A
httss//fiverrcoom/
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Table 1: Comparison of perplexity over contexts, human preference of generated texts, and lexical
variation (as measured by TTR). All models use greedy decoding besides Llama 3. (8 B) Top-P.
Model
Context PPL
128 Pref
256 Pref
128 TTR
256 TTR
Original Texts
73.5
73.8
Strong
Baselines
TimyLLama (1.1 B) Top-P
245
31.8
21.1
38.8
28.2
DeepSeek (7 B) Top-P
34
50.0
35.6
58.2
49.7
Llama 3.1 (8 B) Top-P
36
50.5
38.5
62.1
57.0
Original Models
TinyL.ama (1.1 B)
245
12.0
4.9
25.1
17.0
DeepSeek (7 B)
34
37.7
17.1
45.6
32.2
Llama 3.1 (8 B)
36
35.0
25.6
48.5
34.5
Llama 3.1 (70
29
48.7
34.4
56.4
50.6
Hyperfitted Models
TinyL Lam ama (1.1 B)
467
44.6
34.3
64.5
60.0
DeepSeek (7 B)
545
49.4
45.2
62.3
60.5
Llama 3.1 (8 B)
389
50.1
42.9
64.5
62.6
Llama 3.1 (70 B)
255
55,9
52.4
62.0
61.6
Hyperfitted Models + Citation Blocking
TinyLLama B)
467
45.2
35.0
64.8
60.3
DeepSeck (7 B)
545
47.5
44.1
62.5
60.6
I.lama 3.1 (8 B)
389
47.6
41.2
64.4
63.3
Each model generates 100 continuations for each of three datasets: Wikipedia (Merity et al. 2017),
Fictional Stories (Forsythe 2024), and BBC News (Li et al. 2024). These 300 texts manually validated to have high quality and trimmed to 256 tokens. Of these, we select the first 32 tokens as context and keep the remaining 224 the original continuation. Additionally, scenario, where the model's first 96 tokens are compared to the first 96 tokens from the original con- tinuation. For both length scenarios, using greedy decoding, besides the strong baselines that uses nucleus sampling with TopP = 0.9, Temp 0.7 and Topk 50. Examples of generated texts are available Appendix C.2
create 128-token
gather 3 annotations per comparison. All models generate
In Table D we report the percentage of times that continuation was either preferred or judged equally good to the original. We also report the models' perplexity on the 32-token contexts and the TTR of the tokens in the continuation sequences. Since TTR is sensitive to length, we calculate this metric using the last 96 generated tokens for both the 128 and 256 token scenarios.
4.1 TEXT GENERATION RESULTS
Hyperfitting drastically increases the human preference ratio. This is particularly true for the 256- token scenario, where the initially worst performing TinyLlama increases from 4.9% to 34.4%, putting it on par with Llama 3.1 70b. While all models perform worse on the 256 scenario, the drop less drastic for hyperfitted models. Indeed, greedy decoding with hyperfitted models produce both higher human ratings, and higher ttr when compared with their nucleus sampling counterparts- method proposed specifically mitigate repetitions. Citation blocked models see no noticeable drop in performance.
There is clear correlation with the average TTR and human preference, as the hyperfitted models demonstrate comparably small drop in both metrics sequence length increases. Interestingly, all hyperfitted models perform abysmally in terms of perplexity, corroborating the lack of correlation between this metric and model's ability to generate longer. This is discussed further in Section 5
4.2 DIVERSITY AND DATASET OVERLAP
As hyperfitting is by definition overfitting model a tiny of samples, investigate how this impacts the model's ' diversity and overlap with the training dataset. For diversity between generated sequences apply Self-BLEU, which measures the highest pairwise BLEU score for all pairwise
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Table Diversity metrics for generated texts 96 tokens. Self-BLEU measures diversity within the generated set, while BLEU and Overlap are measured against the dataset. BLEU captures the highest score for any sequence of similar length, and Overlap denotes the longest matching sequence.
Self-BLEU
Dataset BLEU
Dataset Overlap
Model
Avg
Max
Avg
Max
Avg
Max
Original Models
TinyL.Lama
1.5
96.8
2.0
10.0
4.2
9.0
DeepSeek
2.4
41.2
2.6
11.9
4.5
9.0
Llama 3.1
0.9
33.7
1.9
5.9
4.0
7.0
Hyperfitted Models
TinyLlama
1.1
36.0
3.3
9.8
5.5
15.0
DeepSeek
1.4
26.2
3.9
18.6
5.7
40.0
Llama 3.1
1.0
13.5
3.6
32.1
5.8
37.0
Hyperfitted Models + Citation Blocking
TinyLlama
1.1
31.4
3.2
9.8
4.9
8.0
DeepSeek
1.4
26.9
3.2
9.7
4.9
8.0
Llama 3.1
1.0
14.7
3.0
8.5
4.7
8.0
generated sequences; Dataset BLEU represents the maximum BLEU score between a generated sequence and any 96 token subsequence from the dataset; Dataset Overlap measures the longest overlapping subsequence between each generated sequence and the dataset.
For each metric first calculate the highest score for each generated seguence separately, Table then presents both the average and the maximum of these highest value In terms of Self-BLEU, the hyperfitted models, both with and without citation blocking, produce from one another than texts produced by the original models. As the original models are prone to repetitions, this may indicate that certain repetitive behaviors appear multiple generated texts.
that are more diverse
The distribution of longest overlaps are visualized in Figure 3 for an additional 1000 generated texts per model. From outliers these distributions, and max values for Dataset BLEU,, is clear that the hyperfitted models occasionally fall back to re-citing from the training data if not blocked. However, considering that the majority text overlaps are considerably shorter, such occurrences rare. Indeed, less than 2% of the texts generated without blocking contain overlaps longer than 10 tokens, and the vast majority of overlaps fall in range similar to that of the original models.
Considering that the average highest BLEU overlap is only single point higher than the original models, an overwhelming majority of the generated texts do not simply repeat material from the training data. We therefore conclude that hyperfitting causes generalizable increase in text genera- tion capabilities. More details about the overlapping sequences are available in Appendix B.22 These experiments show that the hyperfitted Lana is citation blocked more frequently compared to its larger counterpart, and that all models are more prone to repeating the dataset when generating from contexts in the fiction datasets.
By calculating the highest value followed by mean and max we attain more insightful and interpretable numbers. If one instead calculates the average directly this leads to extremely low numbers for all metrics.
Density (Normalized)
Hyperfit Original
CitationBlock Original
0.3
0.2
0.1
0.01 %
1.9
0.0
8
15
20
25
30
0
2
3
6
7
9
10
Max overlapping sequence longth
Max ovarlapping sequence longth
Figure 3: Distribution of the longest overlap between 1000 generated texts and the dataset
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press
coverage
of
Manchester
City
12.1 coverage
23.9
42.3
United
13.7 GLs
6.9 for
7.6 is
2.7
21.0
City
12.3 has
6.9
and
6.2 foor
your
4.6
GLs
6.8
LLama 3.1
8B
press coverage
of
Manchester
City
37.8
87.3 of
84.1 the
92.8
United
84.8 in
17.8
surrounded
3.9
had
24 Prince
2.0 City
7.3
continnued
10.2
coverage
and
him
Ied
1.2
began
LLama 3.1 8B Hyperfitted
Figure 4: subsequence from the validation data and the corresponding top-3 predictions. The words: "Coverage", "Manchester" and "United" never appear in the hyperfitting dataset.
5 SHARPENED PREDICTIONS
Given the boost in textual quality brought about by hyperfitting, we investigate why hyperfitted mod- els achieve such poor perplexity on held-out data. Using the 300 texts and their original continua- tions from the experiment reported in Section 4 we collect information on the predicted vocabulary distributions of different models. As we observe similar trends across all our hyperfitted models, the results in Table 3alleBiisple display information only on subset the models.
The hyperfitted models exhibit significantly lower entropy in their predicted vocabulary distributions compared to the non-hyperfitted models. This entails that almost all of the probability mass attributed to single token. Given the poor perplexity on withheld texts, this sharpened prediction behavior persists even when these predictions are wrong. This persistence is exemplified in Figure where the hyperfitted model assigns "United" 92.8% probability, although it assigned the previous word "Manchester" a near 0 probability, Neither of these words occur the hyperfitting dataset.
Hyperfitted models produce vocabulary predictions with extremely low entropy. Moreover, the low training loss indicates that almost all of the probability is consistently assigned to the correct next token during training. This sharpened prediction pattern is, to degree, transferred to unseen data where the model continues to heavily favor certain candidates. When evaluating these predictions against the unseen data, the low-entropy predictions assign very low probability to words that oceur in the new sequences but are not favored by the model, which in turn results in very high perplexity regardless of the quality of the texts they generate. is worth noting here that, although standard practice and report performance on held-out data using the exponentiated perplexity metric, the key point is really that predictions with low inherent entropy result in high cross-entropy when measured against unseen sequences.
follow
Table 3: Distributions predicted for the original texts (context + continuation) in Section indicating the accumulated probability of the Nath tokens with the highest probability.
@N
Model
Perplexity
Entropy
@ Prob
@3 Prob
@5 Prob
Original Models
DeepSeek (7B)
12.7
3.48
49.1
67.3
73.9
Llama 3.1 (8B)
13.1
3.47
48.4
67.1
73.9
Llama 3.1 (70B)
9.7
2.84
56.2
73.7
79.5
Hyperfitted Models
DeepSeek (7B)
94.7
1.32
74.5
90.0
93.3
Llama 3.1 (8B)
103
1.46
74.4
89.2
92.3

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80
Fict
S-1
70
Thny
60
Fiction
100
70
68
50
Shuffle-1
70
100
75
Shuffle-100
68
75
100
2000
1024
512
128
64
16
#Training Samples
Figure 5: Left: Top-1 rank similarity matrix of Llama 3.1 (8B) hyperfitted identical, but shuffled, data. Right: The resulting mean TTR of 300 generated texts as the number of training samples vary.
DATA INFLUENCE
The following experiments aim to investigate the effect and importance of the data used during hyperfitting. For this endeavor we alter only the data used during hyperfitting and, unless stated otherwise, apply the same training procedure as Section 3 These experiments focus on a single property a time and do not account for potential relationships between these properties.
6.1 DETERMINACY OF DATA
As an initial experiment, we evaluate the extent to which the set of training samples deterministically dictates the outcome of the hyperfitting process. To this end, we produce two additional versions of the fiction dataset: 'Shuffle-1" and 'Shuffle-All'. For Shuffle-1 the order of only two samples switched, and for Shuffle-All dataset, the entire order shuffled. For both datasets we hyperfit Llama 3.1 using the same fixed random seed used in Section B meaning all models train on the same data, but in a different order.
Using the full original texts from Section g calculate how often two models produce the same top-1 prediction. This is displayed in the left similarity matrix of Figure 5 All models differ in approximately 30% of their top rank predictions; this is noticeably large difference from training on the same data. This is especially noteworthy considering that some portion of these predictions will be for subwords, which are almost guaranteed to have the same top rank. Conclusively, the data does not deterministically account for which tokens emerge as top candidates from the hyperfitting process.
6.2 TYPE OF DATA
Although Section 6. shows that data does not fully determine the resulting model, we nevertheless explore whether any trends emerge between the hyperfitting datasets and downstream dataset ca- pabilities. Therefore, we additionally hyperfit Llama 3.1 with data from both Wikipedia and BBC News separately and measure per-dataset human preference for the 256-token task Section Qualitative examples of these models are available in Appendix C.2
Table 4: Human success ratio for 256 token lengths across Fiction, Wiki, and News datasets.
Model
Fiction Pref
Wikipedia Pref Original Models
News Pref
Average
Llama 3.1 (8B)
21.9
26.0
24.8
24.23
Hyperfitted Models
Llama 3.1 (8B) Fiction
41.0
40.4
40.8
40.73
Llama 3.1 (8B) News
59.3
77.2
62.6
66.37
Llama 3.1 (8B) Wiki
55.5
52.6
44.5
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The results in Table 4 show that the difference in overall performance between these models is drastic. The news model performs best across all datasets, followed by the Wikipedia model. All of hyperfitted models consistently outperform their original counterparts. However, clear trend emerges between the types of training data and the performance on specific datasets. When factoring in the results of Section 6.1 we cannot draw any further conclusions regarding the type of training data and downstream capabilities.
6.3 QUANTITY OF DATA
Finally, we measure the effect of the number of training samples from the Fiction dataset when hyperfitting TinyLlama. To this end we keep the number of updates constant at 5000, entailing more epochs as the number samples decreases. The right part of Figure / displays the resulting TTR of the first 96 tokens when greedily generating from the 300 contexts used in Section Since human annotations are costly, TTR is intended as a crude estimate of quality by measuring the repetitiveness of generations. Further discussion regarding TTR as automatic metric is available in Appendix(A.. I
Although there is initial decline in TTR when decreasing from 2000 samples, the TTR remains above 50 up until there are only 8 training samples. This means that in terms of producing less repetitive output, improvements may seen from very few samples. Further, we note that samples equals our hyperfitting batch size, meaning that at this point all batch updates are identical, and may hence be indicative of why this is drastically worse than 16 samples.
HYPERFITTING AND THE BIGGER PICTURE
7.1 IMAGE GENERATION
To investigate the hyperfitting phenomenon for an additional modality, we hyperfit ImageGPT-Large (774M parameters) (Chen al. 2020) on 2,000 randomly selected images from CIFAR-I0. Besides using visual tokens, ImageGPT is standard Transformer architecture and was pre-trained using next-token prediction on 32x32 images. Figure [6 contains a qualitative comparison of the greedy generation when the models receive the first 25% of an image. More details and results are available in Appendix B.4
From visual inspection it is clear that the hyperfitted model produces higher quality images that more resemble actual objects and subjects (see Appendix [B.4 for more examples). Although the gener- ated image quality unimpressive compared contemporary diffusion based models, the relative improvement allows us to conclude that the hyperfitting phenomenon extends to other modalities beyond just text. Moreover, we note that greedily generating with ImageGPT results in repetitive patterns analogous to the repetitive texts of LLMs. This strongly indicates that the repetitive nature of Transformer LLMs is not an artifact of the repetitions found in natural language, as posed by Fu et al. (2020) and Holtzman et al. 2020).
Original
ImageGPT
Hyperfitted ImageGPT
Figure 6: Examples of generated images using greedy decoding when passed 25% of an image.
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7.2 RELATIONSHIP TO GROKKING AND DOUBLE DESCENT
The hyperfitting phenomenon differs in several key ways from the reported work grokking and double descent. (1) As seen in Figure 2 the positive effect of hyperfitting occurs as training loss approaches zero. In contrast, previous phenomena occur during prolonged exposure to low train- ing loss. (2) All our hyperfitted models are pre-trained LLMs with billions of parameters, whereas previous work utilizes comparably small and randomly initialized networks. (3) Hyperfitting is ob- served in the task of sequence generation, where the model's ' predictions are recursively added to its input. Previously observed phenomena have focused on single output tasks, such as classification and regression. (4) Hyperfitting sees improvements in terms of TTR after only few epochs, as evi- dent in Figure 2 significantly faster than the reported occurrence of double descent and the delayed rewards of grokking. (5) None of the hyperfitting training utilizes any form of weight decay, which speculated to main contributor to delayed generalization in grokking (Liu et al.! 2023).
From (2) and (3), we conclude that hyperfitting is observed at higher level of model and task complexity. One may argue that if grokking were to occur in large pre-trained models, would happen quickly and would therefore reconcile (1) and (4). However, this is yet to be achieved, and it is unclear if such speed would even be compatible with the slow progress of weight-decay (5). Therefore, besides all phenomena seemingly contradicting early stopping, we currently find no evidence of a commonality. We therefore argue for treating hyperfitting as separate phenomenon.
Finally, may note that the validation loss for hyperfitting never decreases, distinguishing it from the hallmark of double descent and grokking. However, as the next-token prediction loss not fully aligned with task of sequence generation, Admittedly, this entails we cannot track an aligned validation score, preventing us from proving that hyperfitting fundamentally differs from previous discoveries.
do not consider this to be reasonable comparison.
7.3 TOP-RANE ENCOURAGEMENT HYPOTHESIS
This subsection explores our observation that scenarios with low training loss cause desirable tokens to be ranked higher even when validation loss is poor. For this, we use the term 'top-ranks" to refer to the set of most probable tokens in predicted distribution, and perplexity the log loss for the next token The notion of a "desirable" token entails that, if generated, the token would extend the current sequence in manner acceptable by a human.
the exponential of
Since next-token prediction does not factor in the order and rank of predictions, we note that a higher loss leaves more room for undesired candidates reside within the predicted top-ranks. In a scenario of moderate perplexity, this means that two models achieving identical perplexity on all time-steps, can still have different top-ranks. This notion is visualized in Figure
A low training perplexity entails distributions during training with low entropy. As observed in Section 5 this entropy behaviour transferred validation data. But simply lowering entropy does not by itself entail that top-rank predictions improve. Indeed, we can freely modify the entropy of any conn-nnform) distribution by applying temperature it, without any change in the predicted order. It follows that something additional happens to the model as it achieves a low training loss.
Note again that the important point not whether loss measured on an exponential scale or not, but that it is the cross-entropy with respect to an external sequence, as opposed to the inherent entropy of the predicted probability distribution.
Room for error
High Loss
Low Loss
Figure 7: Visualization of how distributions with the same probability for the next token (visualized in green) leave more or less room for emror in the top candidates, depending on their entropy.

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We hypothesize that training scenarios where model achieves low loss teaches the model to pri- oritize desirable top-rank candidate. We refer to this as top-rank encouragement. Having a desirable token in the top-rank is distinctly different from perplexity, which measures the average probability of the next token over a set of sequences. Section 4 further demonstrates that a model can predict desirable top-rank tokens, despite poor perplexity the context.
DISCUSSION AND CONCLUSIONS
We introduce the hyperfitting phenomenon: where pre-trained LLMs consistently see significant im- provements in open-ended text generation by overfitting to a very small set of samples. The textual quality of the models are assessed via human verified English speakers, resulting new dataset with over 20,000 annotations. We provide extensive evidence that the hyperfitting phenomenon is reproducible across various model sizes, data types, and extends autoregressive image generation. In all these scenarios, the hyperfitted models predict very sharp distributions, with the candidates seemingly emerging from knowledge acquired during pre-training.
For text generation, the hyperfitted models produce texts that are rated higher by human annotators. Interestingly, using greedy decoding with hyperfitted models results less repetitive texts than using nucleus sampling with the original models. This showcases a key flaw in sampling-only methods: they doesn't t change predicted probabilities, so while it reduces the chance of repetition, the risk still increases with longer sequences. Furthermore, find that hyperfitted models rarely repeat longer subsequences from the training data. Even when explicitly blocking all such subsequences, the models still produce high-quality texts.
We find that hyperfitting on the same data, but shuffled, results in model with 30% different top-1 predictions. This indicates that the stochastic hyperfitting process itself is responsible for large part of which top-1 candidates emerge. Additionally, we found no correlation between the training data and downstream generation capabilities. However, models hyperfitted on Wikipedia and BBC News outperform our model using fiction data. Due to these nuanced results, further work is needed discern the impact of the data used during hyperfitting.
All our experiments (besides the nucleus sampling baseline in Section are centered around greedy decoding. This is intended remove as many elements of uncertainty as possible, and allow us to investigate the underlying model directly. Further investigation of combining hyperfitting with other sampling strategies and heuristics is left to future work. However, we note that sampling without any temperature may result in a near-deterministic generative behaviour, considering the sharp distributions the hyperfitted models.
Finally, through our observations of the extreme scenario that hyperfitting poses, we hypothesize that the behavior predicting good tokens in the top ranks itself a learnable behavior. We refer to this top-rank encouragement and speculate that it is more likely to occur in scenarios with low training loss. To what extent such a hypothesis is true, and why hyperfitting results in generative capabilities that generalize, important open questions for future work.
ETHICS STATEMENT
The research reported in this paper involves an extensive human evaluation. In the interest of fairness as well as data quality, annotators were hired as freelancers through Fiver[" and paid 10 USD per hour of annotation.
ACKNOWLEDGEMENTS
First and foremost, we wish to thank Fernando Pereira for his great feedback regarding the presen- tation of the paper. Additionally, we would like to thank the ICLR reviewers of this paper. Their open-minded and constructive feedback greatly improved the quality of the paper. The discussion and review comments can be found here: http://pppenrevee..e////tooo htm
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Ethan A Chi, Ethan Dyer, Ethan Jerzak, Ethan Kim, Eunice Engefu Manyasi, Evgenii Zheltonozh- skii, Fanyue Xia, Fatemeh Siar, Fermando Martinez-Plumed, Francesca Happé, Francois Chol- let, Frieda Rong, Gaurav Mishra, Genta Indra Winata, Gerard de Melo, Germán Kruszewski, Giambattista Parascandolo, Giorgio Mariani, Gloria Xinyue Wang, Gonzalo Jaimovitch-Loppz. Gregor Betz, Guy Gur-Ari, Hana Galijasevic, Hannah Kim, Hannah Rashkin, Hannaneh Ha- jishirzi, Harsh Mchta, Hayden Bogar, Henry Francis Anthony Shevlin, Hinrich Schuetze, Hi- romu Yakura, Hongming Zhang, Hugh Mee Wong, Ian Ng. Isaac Noble, Jaap Jumelet, Jack Geissinger, Jackson Kernion, Jacob Hilton, Jaehoon Lee, Jaime Fernández Fisac, James B Si- mon, James Koppel, James Zheng, James Zou, Jan Kocon, Jana Thompson, Janelle Wingfield, Jared Kaplan, Jarema Radom, Jascha Sohl-Dickstein, Jason Phang. 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Liam Dugan, Lianhui Qin, Lidia Contreras-Ochandd, Louis- Philippe Morency, Luca Moschella, Lucas Lam, Lucy Noble, Ludwig Schmidt, Luheng He, Luis Oliveros- Colóón Luke Metz, Lütfi Kerem Senel, Maarten Bosma, Maarten Sap, Maartje Ter Ho- eve, Maheen Farooqi, Manaal Faruqui, Mantas Mazeika, Marco Baturan, Marco Marelli, Marco Maru, Maria Jose Ramirez-Quintana, Marie Tolkiehn, Mario Giulianelli, Martha Lewis, Martin Potthast, Matthew L. Leavitt, Matthias Hagen, Mátyás Schubert, Medina Orduna Baitemirova, Melody Arnaud, Melvin McElrath, Michael Andrew Yee, Michael Cohen, Michael Gu, Michael Ivanitskiy, Michael Starritt, Michael Strube, Michal Swędrowski, Michele Bevilacqua, Michi- hiro Yasunaga, Mihir Kale, Mike Cain, Mimee Xu, Mirac Suzgun, Mitch Walker, Mo Ti- wari, Mohit Bansal, Moin Aminnaseri, Mor Geva, Mozhdeh Gheini, Mukund Varma T, Nanyun Peng, Nathan Andrew Chi, Naycon Lee, Neta Gur-Ari Krakover, Nicholas Cameron, Nicholas Roberts, Nick Doiron, Nicole Martinez, Nikita Nangia, Niklas Deckers, Niklas Muennighoft, Ni- tish Shirish Keskar, Niveditha S. lyer, Noah Constant, Noah Fiedel, Nuan Wen, Oliver Zhang, Omar Agha, Omar Elbaghdadi, Omer Levy, Owain Evans, Pablo Antonio Moreno Casares, Parth Doshi, Pascale Fung, Paul Pu Liang, Paul Vicol, Pegah Alipoormlabashii, Peiyuan Liao, Percy Liang, Peter W Chang, Peter Eckersley, Phu Mon Htut, Pinyu Hwang, Piotr Milkowski, Piyush Patil, Pouya Pezeshkpour, Priti Oli, Qiaozhu Mei, Qing Lyu, Qinlang Chen, Rabin Banjade, Rachel Etta Rudolph, Raefer Gabriel, Rahel Habacker, Ramon Risco, Raphaël Millière, Rhythm Garg, Richard Barnes, Rif A. Saurous, Riku Arakawa, Robbe Raymaekers, Robert Frank, Rohan Sikand, Roman Novak, Roman Sitelew, Ronan Le Bras, Rosanne Liu, Rowan Jacobs, Rui Zhang. Russ Salakhutdinov, Ryan Andrew Chi, Scungjae Ryan Lee, Ryan Stovall, Ryan Techan, Rylan Yang, Sahib Singh, Saif M. Mohammad, Sajant Anand, Sam Dillavou, Sam Shleifer, Sam Wise- man, Samuel Gruetter, Samuel R. Bowman, Samuel Stern Schoenholz, Sanghyun Han, Sanjeev Kwatra, Sarah A. Rous, Sarik Ghazarian, Sayan Ghosh, Sean Casey, Sebastian Bischoff, Sebas- tian Gehrmann, Sebastian Schuster, Sepideh Sadeghi, Shadi Hamdan, Sharon Zhou, Shashank Srivastava, Sherry Shi, Shikhar Singh, Shima Asaadi, Shixiang Shane Gu, Shubh Pachchigar, Shubham Toshniwal, Shyam Upadhyay, Shyamolima Shammie Debnath, Siamak Shakeri, Simon Thormeyer, Simone Melzi, Siva Reddy, Sneha Priscilla Makini, Soo-Hwan Lee, Spencer Torene, Sriharsha Hatwar, Stanislas Dehaene, Stefan Divie, Stefano Ermon, Stella Biderman, Stephanie Lin, Stephen Prasad, Steven Piantadosi, Stuart Shieher, Summer Misherghi, Svetlana Kiritchenko, Swaroop Mishra, Tal Linzen, Tal Schuster, Tao Li, Tao Yu, Tariq Ali, Tatsunori Hashimoto, Te-Lin Wu, Théo Desbordes, Theodore Rothschild, Thomas Phan, Tianle Wang. Tiberius Nkinyili, Timo Schick, Timofei Kornev, Titus Tunduny, Tobias Gerstenberg, Trenton Chang, Trishala Neeraj, Tushar Khot, Tyler Shultz, Uri Shaham, Vedant Misra, Vera Demberg, Victoria Nyamai, Vikas Raunak, Vinay Venkatesh Ramasesh, vinay uday prabhu, Vishakh Padmakumar, Vivek Sriku- mar, William Fedus, William Saunders, William Zhang, Wout Vossen, Xiang Ren, Xiaoyu Tong. Xinran Zhao, Xinyi Wu, Xudong Shen, Yadollah Yaghoobzadeh, Yair Lakretz, Yangqiu Song, Yasaman Bahri, Yejin Choi, Yichi Yang, Yiding Hao, Yifu Chen, Yonatan Belinkov, Yu Hou, Yufang Hou, Yuntao Bai, Zachary Seid, Zhuoye Zhao, Zijian Wang, Zijie J. Wang. Zirui Wang, and Ziyi Wu. Beyond the imitation game: Quantifying and extrapolating the capabilities of lan- guage models. Transactions on Machine Learning Research, 2023. ISSN 2835-8856. URL net/ htte//ooommm1iyy1SSBe.
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Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R Bowman. Glue: A multi-task benchmark and analysis platform for natural language understanding. EMNLP 2018, pp. 353, 2018.
Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph. Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, Ed H. Chi, Tatsunori Hashimoto, Oriol Vinyals, Percy Liang, Jeff Dean, and William Fedus. Emergent abilities of large lan- guage models. Transactions on Machine Learning Research, 2022a. ISSN 2835-8856. URL httpps://on htttps::/ppeereveeee http://pppwreeeee..eeee. net Forum? ii Survey Certification.
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02385
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Context:
On 13, 2005, Avater Press began releasing new Friday 13th comics. The first
Continuation
fitth issues delaynd hecause of production problems. solth issun was supposed be released on November 11, 2006, but it was never released. The reason why was because of the tragic shooting at North Hampton High School. The shooter, 16-yeer-oid boy, kited too people and wounced enother before turning the gun himselt, The event Friday
ecsues
out a
1998 Columbine High School move to strenge town eher his perents divorced. The student had to live with his real- life best friend, high school student psychapallt. The psychopatth fortured Uhe student untll he committed suicide, The severth di issus, which was released on February 14, strange town after her 2007, ebout girl who had psychhpaaih. psychopalth kanured girl until
similer
best friend's older brother was
divorced. She had live with real- life best friend, bui
suicide.. eightt
Continuation
1ied Friday the 13th, wes written by Orian Pulido and illustrated by Mike Walfer and Greg Waller. The story takes place after the events of Freeidy Jason, where siblings Miles and Laura Upland have inherited Camp Crysiul Lakes Knowing that Jasion caused the revent destruction, Laura,
Jason using paramiltary group, that A three insue miniseress siled Friday the 13th: Bloodbash was released in September 2005. Writen by Brinn Pulido and illustrtedd by Mixe Wolfer and Andrew Dalhouse, the story ivvlvee group of teenagers who come from Camp Tomorrow, camp that sits Crystal Lake, for backgrounds,
sats out
brather
property.
"party wookend The teonagers discovor they them. Erian Pulido retumed for third time October 2005 to wrtte Jason Picking
awaken Jason, hunts
continaation
Equallty Good
Camminsziion
Figure 8: Screenshot of the interface used by the annotators. The example texts from the 256 token scenario.
Dandly Dittrb store Consentu Annotations
over most recent generated tokens
fine
1.5
Cenarruu Bos
0.40
#enerattd Tokees
Figure 9: Left: TTR distributions of generated texts where all three annotators agreed on their quality, with 5% of outliers filtered away Right: TTR of the 96 most recent tokens in relation the currently generated sequence length.
A HUMAN ANNOTATION
The research reported in this paper involved an extensive human evaluation for comparing text con- tinuations resulting in a collection of over 20,000 annotations. In the interest of fairness as well as data quality, annotators were hired freelancers and paid 10 USD per hour of annotation. All annotations were conducted through a simple web interface, where hired annotators could log in to their individual accounts. A screenshot of this interface is shown in Figure 8
Using the annotation interface, annotators were continuously provided with a stream of randomly selected samples from the pool of those that had yet to receive three independent annotations. The order in which the model-generatee generated text was displayed as either the first or second continuation was uniformly randomized.
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A.1 TTR AS AN AUTOMATIC METRIC
The rudimentary metric of TTR measures the ratio of unique tokens in a sequence. Since this metric highly affected by sequence length, always apply it the 96 lasts tokens of generated sequence. Although TTR tells us nothing about the content of those tokens, in the context of longer texts generated by LLMs, we nevertheless find it to correlate human preferences. The left part of Figure-2 shows the TTR distribution of generated sequences (5% of outliers were filtered away for clarity) where the annotators were consensus regarding their quality
The consensus distributions clearly show that texts with low TTR are less likely to be preferred. Indeed, below certain threshold of about 0.4, no generated text reaches good consensus. While there are samples with high TTR that are agreed upon to be bad, this suggests that lower average TTR correlates with a higher probability of texts being of lower quality.
We note that the reason TTR is effective is that contemporary LLMs struggle with loops and repeti- tions over longer sequences. Indeed, due to the potential accumulation of errors during generation, the longer the generated sequence, the higher the chance of degenerative behavior and a lower TTR. This is clearly demonstrated the right part of Figure 9 which shows the TTR of the 96 most recent tokens in relation the currently generated sequence length, for the texts in Section 4 Although all models show some decrease in TTR as the sequence length increases, the hyperfitted Llama 3.1 both starts higher value and decreases slower rate than the original models.
B ADDITIONAL EXPERIMENTS
B.1 IMAGE GENERATION
In this section. we present additional details from our image generation experiments using ImageGPT-Larre (774M parameters) on CIFAR-10. The model was hyperfitted on 2,000 randomly selected images for 50 epochs, using learning rate of x 10-3, ImageGPT models converged more slowly, so we trained the models for 50 epochs with the effective batch size of 8, evaluating them at the end of each epoch on a validation set of 128 images. The changes in training and validation loss are shown Figure I0
To visually assess the effect of hyperfitting, we generated images using the first 256 pixels (8 rows) of entire CIFAR-10 test set, which were not seen during either pretraining or hyperfitting. Compared to the pre-trained model, the hyperfitted version produced sharper and more coherent images, with fewer repetitive patterns and greater structural consistency whilst producing diverse images. Figure 11 presents some examples of images generated by the original and hyperfitted models based on these prompts.
Training Loss
Validation Loss
332
4.0
32-
6.0
025
6.0
Epoch
Epoch
Figure 10: Training and validation loss curves for hyperfitted ImageGPT model.
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Original
ImageGPT
Hyperfitted ImageGPT
Original
ImageGPT
Hyperfitted IMageGPT
Original
ImageGPT
Hyperfitted IMageGPT
Original
ImageGPT
Hyperfitted ImageGPT
Original
ImageGPT
Hyperfitied ImageGPT
Figure 11: More examples of generated images using the original and hyperfitted ImageGPT (large).
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Table Ratio of 224-token generated sequences that contain an overlap of more than 5 tokens with the Fiction hyperfitting data.
Model
Fiction
Wikipedia Original Models
BBC News
Average
TimyLLama (L.IB)
29
11
14
18
DeepSeek (7B)
63
18
27
36
Llama 3.1 (8B)
8
16.7
Hyperfitted Models
Timy (1.1B)
89
45
47
60.3
DeepSeek (7B)
30
41.3
Llama 3.1 (8B)
85
4
29
39.3
B.2 DATASET-SPECIHT CITATION BLOCKING
As detailed in Section 3 the citation blocker works by pattern matching against the Fiction dataset. the recent tokens exists subsequence in the dataset, further generations on that subsequence is blocked, as soon as the current word has finished. Table 5 contains the ratio of generated sequences that exceeded the token overlap least once.
These results clearly show that all models, including the original ones, are more likely to generate sequences that overlap with the fiction data when generating from the fiction dataset. This is intu- itive, as one would expect higher overlap of phrases and expressions between different fiction texts, compared to Wikipedia and BBC News. However, increased ratio of overlaps when generating from fiction contexts. Notably, when generating from Wikipedia and BBC News, this increase is primarily observed with TinyLlama model.
evident that hyperfitted models exhibit an
B.3 INSTRUCTION-TUNED MODELS
Although this paper mainly focuses on models trained via next-token prediction, this section pro- vides preliminary experiments on how hyperfitting affects already instruction-tuned models. Hence, we hyperfit the official instruct versions DeepSeek 7B and LLama 3.1 with the same procedure and data as described in Section We note that the full iitructiio-tuning procedure of these models is not disclosed.
Identically to how greedy decoding, meaning we simply generate the next token without any additional instruction prompt. For these generated sequences, we report the TTR of the tail-end 96 tokens, along with the model's perplexity on the original context. Additionally, we report the model's average predicted probability mass in the top-1 and top-3 tokens.
generated in Section 4 we generate new for the 300 contexts using
From the results in Table 6 it is clear that very similar trends emerge when applying hyperfitting an instruction-tuned model. Perplexity increases. TTR remains more stable as sequence length increases, and the predicted distributions get narrower. Noticeably, however, the same trend appears smaller degree when comparing the original base models the instruction- models.
B.4 PERFORMANCE ON DOWNSTREAM TASKS
We further explore the effect hyperfitting on downstream tasks using the MMLUU (Hendrycks) al. (222I) benchmark and GLUE benchmark (Wang et al., (018)). MMLU measuring the knowl- edge of the model.and GLUE being more task oriented. For both these tests we do not apply any further fine-tuning, and instead the model's hyperfitted the Fiction dataset as described in Section
As instruction -unddd model's tend to perform significantly better in the MMLU benchmark. include results for these models as well. As elaborated upon in Appendix B.3 these are trained via the same procedure in that of Section 3
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B.4.1 MMLU
The MMLU dataset is benchmark designed measure model's acquired knowledge over 57 subjects. This is achieved via a multiple-choice setup, where the candidate answer with the highest predicted probability is interpreted as the model's s answer. Following the implementation of the official GitHub repository we vary the number of question-answa pairs in the context and report these separately. Additionally, we calculate the models' perplexity on the zero-shot context of each question separately.
The results displayed in Table show clear trend where the hyperfitted models perform slightly worse overall. For DeepSeek, the drop in performance is roughly accuracy point for both the base and instruct models. For the LLaMA 3.1 models, the drop slightly bigger, with a 6-point decrease for the base model and 5-point decrease for the instruct models.
The increase perplexity for hyperfitted models is significantly smaller compared to the open-ended text generation experiment in Section 4 For example. the hyperfitted base version of LLaMA 3.1 increases from 8.7 to 12.3, whereas in Table we sec an increase from 36 to 389. A likely explana- tion for this is that the text of the MMLU questions leaves less room for subjective prediction, which supported by the very low perplexity scores all original models. As Section demonstrates, hyperfitted models retain linguistic knowledge and will correctly assign probability when the next token is forced.
B.4.2 GLUE
The GLUE dataset tasks the model across a range of different tasks. For all these tasks we evaluate the models in both few-shot and zero-shot setting. For the few-shot, include the maximum number of shots allowed by the context length for each task, ranging from to 4 shots. The shots randomly selected from the training data while maintaining the label distribution of the original training set. The models were evaluated on the development sets as the test set labels are not publicly available.
Table shows that hyperfitting has very small impact on most tests, and no large overall trend emerges. Hence, similarly to the results Appendix B.4.1] we conclude that the overall down- stream capabilities of the models remain mostly intact, and hyperfitting does not lead to catastrophic degradation performance across tasks.
Table 6: Distributions predicted for the original texts (context + continuation) in Section indicating the accumulated probability of the N:th tokens with the highest probability.
@N
Model
Context PPL
128 TTR
256 TTR
I Prob
@3 Prob
Original Models
DeepSeek (7B)
34
45.6
32.2
49.1
67.3
Llama 3.1 (8B)
29
56.4
50.6
48.4
67.1
DeepSeek (7B) Chat
49
57.6
56.2
53.0
71.5
Llama 3.1 (8B) Chat
45
58.9
55.7
50.1
69.6
Hyperfitted Models
DeepSeek (7B)
545
62.3
60.5
74.5
90.0
Llama 3.1 (8B)
389
64.5
62.6
74.4
89.2
DeepSeek (7B) Chat
547
63.3
63.3
75.0
90.2
Llama 3.1 (8B) Chat
384
63.6
62.0
74.0
89.3
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Table 7: Results MMLU dataset for different number of question-answe wneweer pairs included in the prompt. The perplexity score is measured on the zero-shot context of all questions.
Model
Perplexity
0-Shot Models
1-Shot
3-Shot
5-Shot
Original
DeepSeek (7 B)
9.3
46.4
47.3
48.8
49.4
Llama 3.1 (8 B)
8.7
65.4
66.0
66.5
66.6
DeepSeek B) Chat
11.4
51.5
50.3
51.7
51.8
Llama 3.1 (8 B) Chat
10.9
68.5
68.2
68.6
68.9
Hyperfitted Models
DeepSeek (7 B)
32.3
44.5
46.6
48.1
48.4
Llama 3.1 (8 B)
12.1
58.9
59.9
59.7
60.2
DeepSeek (7 B) Chat
32.3
49.7
50.3
50.6
50.9
Llama 3.T (8 B) Chat
16.7
63.1
63.0
63.6
64.4
Table 8: Comparison of the original and hyperfitted Llama and Deepseck models across various GLUE tasks, using 0-shot (OS) and few-shot (FS) configurations. The target metrics are accuracy (acc), Fl score (f1), and Matthews correlation (cort.).
DeepSeck (7B)
LLama 3.1 (8B)
Base
Hyperfitted
Base
Hyperfitted 0S
Task
Metric
OS
FS
0S
FS
OS
FS
cola corr.
0.
0,362
0.268
0.215
0.538
0.240
0.297
mnli acc
0.179
0.352
0.088
0.352
0.
0.649
0.004
0.570
mrpc
0.
0.684
0.478
0.691
0.679
0.684
0.642
0.703
0.812
0.573
0.816
0.786
0.812
0.775
0.797
quli acc
0.464
0.573
0.497
0.626
0.710
0.579
0.420
0.548
acc
0.055
0.680
0.398
0.649
0.589
0.806
0.271
0.756
f1
0.042
0.279
0.239
0.102
0.619
0.732
0.418
0.667
rte
0.
0.430
0.354
0.574
0.
0.599
0.646
0.690
sst2
acc
0.594
0.943
0.718
0.923
0.847
0.898
0.393
0.904
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C TEXT GENERATION EXAMPLES
C.I HYPERAITTING AND ORIGINAL MODELS
Context Fiction:
He thought his family and their recent vacation to Jamaica and all the fun they had. He smiled as he remembered his wife screaming
Tiny 1.1B Original
when he told her he was going to be a dad. He had to tell her he was going to be a dad. He had to tell her he going be a dad. He had to tell her he going to be a dad. He had to tell her he was going to be a dad. He had to tell her he was going be dad. He had to tell her he was going to be dad. He had tell her he was going to be a dad. He had to tell her he was going to be a dad. Hc had to tell her was going to be dad. He had to tell her he was going be dad. He had to tell her he was going to be dad. He had to tell her he was going to be a dad. He had to tell her he was going be a dad. He had tell her he was going to be a dad. He had to tell her he was going to be dad. He
TinyLlama 1.1B Hyperfitted
in joy after she saw the ring on his finger. She had been treasured wife, and he treasured husband. He loved the way it felt to be loved unconditionally. He loved that no matter what she would be his number one fan. No matter what the occasion he would be the life of the party! He would be the happiest man alive! CHAPTER THE DAY OF THE WEDDING The bride and her mother arrived gathered. The
in the great hall. The hall was where all the wedding guests hall was also where the wedding dinner was served. "Are you sure this hall can hold all of our guests?" The bride asked she looked at the large hall. "Yes am." The groom replied. "Then think that we should have dinner here this evening." The bride said. "Oh no! I haven't 't tasted food in a long time!" Mother exclaimed as she followed the bride into the large hall. "Let me show you where the kitchen The bride said to her
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Context BBC News:
Negative press coverage of Manchester City forward Raheem Sterling "emboldens racist rhetoric", says the Professional Footballers' Association.
DeepSeek 7B Original
The PFA said it was "deeply concerned" by the "unacceptable" comments made by former England striker Stan Collymore. Collymore said Sterling should "go back to Africa" and "stop being a disgrace". The PFA said it was "disappointed" that the comments were not condemned by the Football Association. Sterling, 20, has been the subject of racist abuse on social media since he was substituted during England's 2-0 defeat by Germany in the World Cup semi-final. The PFA said it was "disappointed" that the FA had not condemned Collymore's 's comments. "The PFA is deeply concerned by the comments made by Stan Collymore, said the union. "The PFA is disappointed that the FA has not condemned these comments and we hope that the FA will take the appropriate action to ensure that this type of behaviour is not tolerated." The PFA said it was "disappointed" that the FA had not condemned Collymore's comments The PFA said was "disappointed"
DeepSeek 7B
Hyperfitted
The 20-year-old has been the subject of abuse social media after he in- volved in a training ground bust-up with Liverpool defender Joe Gomez. PFA chief executive Gordon Taylor told BBC Radio 5 live: "What we've last 48 hours footballer and a role model in his community. "What we've also seen com- plete lack of respect for the PFA, the players' union, and what we stand for. "We will not be lectured by anyone about what we should and shouldn't do. "We are union that represents all players, past and present, and we will not be told what do by anyone." " The PFA has more than 11,000 members, including all current Premier League players as well as many from the lower leagues and abroad. Mr Taylor said the organisation would "monitoring" the situation and would take "whatever action necessary" support Mr Sterling, He added: "We will not be lectured by anyone about
in the complete lack of respect for young man who is a fantastic
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Context Wikipedia:
After the Louisiana Purchase, Breckinridge focused on securing vote present the Twelfth Amendment to the states for ratification.
Llama 3.1 8B Original
The amendment was designed to prevent a repeat of the 1800 election, in which the House Representatives had choose between the two candidates with the most electoral votes. The Twelfth Amendment, which was ratified in 1804, re- quired electors to vote separately for president and vice president. Breckinridge also worked to secure the passage of the Judiciary Act of 1801, which created new federal courts and expanded the jurisdiction of the Supreme Court. The act was designed strengthen the federal judiciary and to ensure that the federal govern- ment had the power to enforce its laws. Breckinridge was a strong supporter of the colonel in the Kentucky militia during the conflict.
War of 1812 and served He was also a member of the Kentucky legislature and served as delegate to the Hartford Convention in 14. After the war, Breckinridge returned to Kentucky and served as member of the state legislature and as a judge on the Kentucky Court of Appeals. He was also member of the Kentucky Constitutional Conven- tion in 1834. Breckinridge was strong supporter of the Whig Party and
Llama 3.1 8B Hyperfitted
The amendment had been defeated in the Senate in 1804, and there was little chance of it passing if presented to the states in its current form. Breckinridge, working with Senate Judiciary Committee chairman William Pinkney (D-MD) and Virginia senator George Hay (D-VA), crafted compromise version of the amendment that was acceptable to both parties. In the spring of 1805, the Senate voted 32 22 submit the amendment the states for ratification. By October 1805, eleven of the twelve state legislatures had approved the amendment, and in December 1805, the remaining state, Delaware, ratified the measure. The Twelfth Amendment, which modified the Electoral College system, was thus added to the Constitution. The amendment was intended to correct what had become known as the "three-fiii fifths rule", by which the number of electoral votes cast by state was equal to the number of senators plus the number of representatives Congress. This gave small states, which had fewer representatives than larger states, an elec- toral advantage. The amendment changed the method of awarding electoral votes
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C.2 DIFFERENT HYPERHITTING DATASETS
Context:
"This vaccine is more than good news, it's a game changer," Dr Mohammed Khaki tells Newsheat.
LLama 3.1 Hyperfitted Fiction
The vaccine, which was developed by Oxford University, is currently being trialled in sev- eral countries, including the UK, China, and the US. It has shown promising results in early tests, and successful, could rolled out to the public as early as next year. "What makes this vaccine so effective is that it targets multiple strains of the virus," Dr Khaki explains. "This means that it can protect against wider range of variants, which is especially impor- tant the virus continues evolve." The doctor says that while the vaccine is not for COVID-19, it will significantly reduce the severity of the discase. This, he says, will be especially helpful for vulnerable groups, such as the eiderly and those with underlying health conditions. "We know that COVID-19 can be a deadly disease, but we also know that there things can do protect ourselves." " Dr Khaki says. "Washing your hands regularly, wearing a mask, and avoiding close contact with those who are sick are all im- portant steps you take stay safe." However, he acknowledges that the pandemic is far from over, and
LLama 3.1 Hyperfitted News
"It will only protect from the current pandemic but will also prevent future pan- demics by protecting us from getting infected in the first place." The technology is based natural protein found in the lining of the nose called angiotensin-cconvrtinn enzyme 2 (ACE2). Coronavirus uses this protein to enter the hody so the vaccine has heen designed to target "The vaccine will train our bodies to produce antibodies that coat the sur- face of the nose and prevent the virus from entering the body." Dr Khaki explains. "It will also train our bodies to produce memory cells that will remain our system and provide long-term protection against future infections." The doctor says the best way to think of the vaceine is not as magic bullet but as a way to "train our bodies to fight the virus the most efficient way". "The virus will continue to change and adapt and our bodies will need continue to change and adapt in order to stay one step ahead of the virus," he says. "There such thing perfect vaceine but the goal is to make the best possible vaceine that we can." There are two vaccines currently being administered to
LLama 3.1 Hyperfitted Wikipedia
"Having vaccine that is effective against all human papillomavirus types should increase uptake and help to protect against the most common sexually transmitted infection in the UK. "HPV infection cancer, and while the vaccine is not a cure for these conditions, it should help to prevent them occurring the first place." Khaki says while the vaccine has been shown be effective in trials, it will only be licensed for use once the manufacturing process has been completed
cause significant health problems, including genital warts and
been given to thousands of people "Once we have that data, we will be able to say with more certainty how effective the vaccine is and what its side effects are," says. "Until then, we have to a little bit cautious, although from what we know already, it looks very promising." The vaccine is expected to be given to 12 to 13-year-olds, in line with the HPV immunisation programme that is already underway in Scotland. It is also likely be given to young women and men who have not already been immunised, in order to provide them
the vaccine
real world.
26


Reviews:
[{"review_id": "8BGmU5MKUb", "reviewer": "Reviewer_KAD8", "summary": "The paper discovers a new phenomenon in fine-tuning LLMs on tiny datasets, which they term Hyperfitting as it's closely connected to overfitting. Surprisingly, while overfitting indeed increases validation perplexity, the model performs much better in open-text generation as rated by human evaluation. They find that models even perform comparably to those with 10x larger parameters in open-ended generation tasks. The phenomenon is demonstrated across three different models and three validation datasets across different domains in text generation. Additionally, they also conduct preliminary experiments in autoregressive image generation and observe similar effects. The authors also find that the fine-tuned models tend to generate a more diverse context, which may help mitigate the common repetition issue in long-text generation.", "questions": "Please refer to the weaknesses part.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The paper is well-written and easy to follow\n- The phenomenon is novel and quite surprising, especially its implication in reducing the repetition issues. The paper opens up a new perspective in understanding repetition in text generation. \n- I enjoyed reading the experiment results presented in the paper. E.g., the experiments in Section 6.2 present an interesting finding that the model fine-tuned on News performs better in human evaluation than the ones fine-tuned on Fiction and Wiki. This result is surprising given that Fiction, with its inherently diverse and creative language, might be expected to enhance performance more. This finding invites further exploration into how different training domains impact model generation quality.", "weaknesses": "- The experiments focus on relatively small models, with the largest at 8B parameters. Since Hyperfitting is studied on small datasets, extending the analysis to larger models could provide insights into whether this phenomenon scales and is influenced by model capacity.\n- A related issue is that the models are only evaluated in a limited range of datasets, which may not fully capture the phenomenon’s applicability across domains and tasks. For example, it would be useful to see how Hyperfitting behaves in specialized domains such as legal summarization [1], medical transcription [2], or dialogue summarization [3]\n- Some implementation details appear to be missing. E.g. are training and evaluation results averaged over different random seeds to ensure consistency? Moreover, are all parameters updated during fine-tuning? If so, does the phenomenon also exist in parameter-efficient fine-tuning, e.g. LoRA [4]?\n\n[1] https://huggingface.co/datasets/lighteval/legal_summarization\n\n[2] https://huggingface.co/datasets/rungalileo/medical_transcription_4\n\n[3] Chen, Yulong, Yang Liu, and Yue Zhang. \"DialogSum challenge: Summarizing real-life scenario dialogues.\" Proceedings of the 14th International Conference on Natural Language Generation. 2021.\n\n[4] Hu, Edward J., et al. \"Lora: Low-rank adaptation of large language models.\" arXiv preprint arXiv:2106.09685 (2021).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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": "LHCvNEaG7A", "reviewer": "Reviewer_gnwy", "summary": "This work studies a surprising phenomenon that occurs when a modern LLM is overfit to a small text corpus finding that a model's greedy decoding capabilities are actually improved in various ways rather than degenerating as one might expect. They observe that after training a model to near zero loss on the small target corpus, while these models become poor \"language models\" for other held out text (poor validation loss), in _generative_ evaluation scenarios, \"hyperfitted\" models produce better outputs according to human judges and exhibit higher diversity in their completions as compared to the base models from which they are derived. They also perform similar experiments with autoregressive image generation models and observe similar effects. They present and analyze an explanation for these observations focusing on the \"sharpening\" of the model's predictive distribution and ablate the effect of the dataset and training curricula on these phenomena.", "questions": "Primary:\n1. Relating to the weakness about lack of benchmark scores, what do the differences in MMLU or other leaderboard tasks look like? I know Tinyllama scores too poorly to measure on many tasks, but the 7B and 8B models should have non trivial base performance. I suspect that since hyperfitting seems to destroy the ability of the model to properly assign loss values to val data (eg. no longer is a good \"language model\" in the technical sense of the term), this will impact these types of benchmarks. If benchmark scores after hyperfitting are near chance or even just significantly degraded, try reformulating the tasks as a generative evaluation and checking the hyperfitted model again.\n2. Relating to the weakness about only including base models, did the authors run any experiments with instruction tuned versions of these same base models? I hypothesize that hyperfitting to these stories or news datasets effectively seems to do some of the work that all of the post training process normally accomplishes. Whatever this unqiue style of training does, seems to sort of take the model out of LM p(x) mode and into generator f(x)->y mode. So... what happens when you try to \"hyperfit\" an already post-trained model? Does it get worse/better/remain unchanged wrt the diversity analyses presented and the benchmark scores noted in prior comments?\n\nMinor:\n\n3.  In Table 3, what do \"@1/3/5 Prob\" mean? They are not defined anywhere and this table has no descriptive caption.\n\n4. TTR seems to be a measure from linguistics and child language acquisition research, please define this inline in the sentence in which it is first used. Alternately use some self-evident description like \"ratio of unique n-grams\" for n=1 and other values. Also, Self-BLEU considers up to what n-value in this analysis? Generally, discussion and analysis of n-gram diversity and n-gram copying rate from training data might be more interpretable.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "1. Work explores an interesting phenomenon and presents it as \"curious\" without resulting to hyperbolic/\"hype\"-y language.\n2. Contextualization in prior work is relatively complete.\n3. A variety of diversity measures are used to examine the difference in output distributions caused by \"hyperfitting\" in a wholistic manner.\n4. The human preference evaluation is a strong test for impact of hyperfitting, and the results for the hyperfitted models are promising -- the recovery of the 7B and 8B models to near equivalence in preference to ground truth completions is surprising.\n5. In the event one wanted to use a hyperfitted model in practice, the \"citation-blocking\" mechanism is a simple and practical solution to the increased generation overlap with the small training corpus that hyperfitting causes.", "weaknesses": "1. There is only a focus on the impact to open-ended generation (continuation/completion) and not enough focus on utility. There are no conversational benchmark scores like AlpacaEval or MT-bench, nor knowledge intensive benchmark scores such as for MMLU or other tasks available in suites like the lm-eval-harness. \n2. Use of base models only in this particular case is an issue since it might reveal more about the observation and hypothesized mechanism being presented if instruction tuned models were included in the analysis.\n3. Diversity and length are not equal to output quality or utility, and this equivalence both implicit in the analysis as well as explicitly stated a few times.\n\nSome of these issues may be addressable within the review window and thereby enable a recommendation of acceptance, see questions.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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": "TT8Ttr0ZRQ", "reviewer": "Reviewer_Mz78", "summary": "The paper introduces the hyperfitting phenomenon, where overfitting pre-trained LLMs on a small dataset until it achieves near-zero training loss enhances the LLMs' long-form generation capabilities, yielding higher-quality texts that are preferred by humans, despite the models achieving significantly worse validation losses. In particular, the paper finds this phenomenon across models hyperfitted on Fiction-Stories dataset, these models rarely repeat training sequences and produce low-entropy predictions. The paper takes an approach to explain this phenomenon with the concept of top-rank encouragement, where hyperfitting prioritizes desirable tokens in the top ranks of predictions, resulting in improved text quality.", "questions": "Please refer to weaknesses", "limitations": "", "rating": 5, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The paper is well written and easy to follow.\n2. The discovery of the hyperfitting phenomenon is interesting, and the paper includes sufficient experiment to support this.\n3. The explanation on the hyperfitting phenomenon is convincing.", "weaknesses": "1. I'm not quite sure what specific applications this phenomenon has, because LLM practitioners rarely use pretrained base models directly for generation tasks. Generally, they first perform SFT (supervised fine-tuning) and then use chat models for generation. However, this paper does not compare the generation quality between hyperfitted models and chat models.\n2. Although this paper shows hyperfitting can improve the generation quality, its impact on the model's internal knowledge, hallucination, and other factors has not been studied in the article. I suggest that the authors test the effects of hyperfitting on model performance using datasets like MMLU and GSM8K.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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": "uJux9xtRvm", "reviewer": "Reviewer_p1Nv", "summary": "This paper focuses on the problem of repetitive generation results with large language models (LLMs) under greedy decoding strategy. Specially, this paper introduces a newly-observed phenomenon called *hyperfitting*, which helps to eliminate the repetition problem. This is achieved by finetuning the LLM on a separated small dataset untill achieving minimal loss. This paper runs abundant experiments to show that, hyperfitting can efficiently increase the token diversity as well as human preference for greedily generated text, with a side effect that the conditional token probability distributions are generally sharpened. Also, such hyperfitting phenomenon is observed in popular LLMs such as Llama 3.1, and even in image generation models (ImageGPT).", "questions": "1. There seems to be a contradiction between line284 \"never appear in the hyperfitting dataset\" and line301 \"neither occur in the training data\", which is right?\n2. Is the finetuning dataset a subset of the training datasets?\n3. What if one apply random decoding with hyperfitted model? Will the generation results be similar to greedy decoding (because of low entropy)?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "* The introduced hyperfitting phenomenon is interesting, and there may be some potential application scenarios.\n* There are abundant analytic experiments in this paper to support the widespread existence of the hyperfitting phenomenon in LLMs, as well as demonstrating the side effects of hyperfitting.", "weaknesses": "The main weakness of this paper lies in its weak contribution. Though the introduced hyperfitting phenomenon is attractive, this paper failed to convey crucial conclusions concerned by readers. Neither did this paper reveal the key reason why such phenonmenon exists and how it works, nor did it validate its advantages in practical downstream tasks. The experiments only answers how the hyperfitted model behaves, but did not answer why. I noticed there are some results for downstream tasks in Tab. 6, however, I found the results are even worse for hyperfitted models, and the experimental setup has its limitations (no comparison with other random decoding strategies, which are more practical in my point of view). \n\nEither of the following suggestions can help for improving this paper:\n1. Explore the key reasons why hyperfitting works for eliminating the repetition problem, as well as why hyperfitting results in sharped predictions.\n2. Demonstrate the advantages of the hyperfitting technique under a variety of realistic downstream tasks, and compare with non-greedy decoding strategies.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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:

Decision: Accept (Poster)