DataGen: Unified Synthetic Dataset Generation via Large Language Models
Authors: Yue Huang
Siyuan Wu
Chujie Gao
Dongping Chen
Qihui Zhang
Yao Wan
Tianyi Zhou
Chaowei Xiao
Jianfeng Gao
Lichao Sun
Xiangliang Zhang
Venue: ICLR 2025 Poster (2025)
Keywords: large language model
evaluation
synthetic data
Primary area: foundation or frontier models, including LLMs

Abstract: Large Language Models (LLMs) such as GPT-4 and Llama3 have significantly impacted various fields by enabling high-quality synthetic data generation and reducing dependence on expensive human-generated datasets. 
Despite this, challenges remain in the areas of generalization, controllability, diversity, and truthfulness within the existing generative frameworks. To address these challenges, this paper presents DataGen, a comprehensive LLM-powered framework designed to produce diverse, accurate, and highly controllable datasets. DataGen is adaptable, supporting all types of text datasets and enhancing the generative process through innovative mechanisms. To augment data diversity, DataGen incorporates an attribute-guided generation module and a group checking feature. For accuracy, it employs a code-based mathematical assessment for label verification alongside a retrieval-augmented generation technique for factual validation. The framework also allows for user-specified constraints, enabling customization of the data generation process to suit particular requirements. Extensive experiments demonstrate the superior quality of data generated by DataGen, and each module within DataGen plays a critical role in this enhancement. Additionally, DataGen is applied in two practical scenarios: benchmarking LLMs and data augmentation. The results indicate that DataGen effectively supports dynamic and evolving benchmarking and that data augmentation improves LLM capabilities in various domains, including agent-oriented abilities and reasoning skills.

Published as a conference paper at ICLR 2025
DATAGEN: UNIFIED SYNTHETIC DATASET VIA LARGE LANGUAGE MODELS
Yue Huangl., Siyuan W Chujie Gao Dongping Chen Qihui Zhang?, Yao Wan 2.- Tianyi Zhou', Chaowei Xiao", Jianfeng Gao², Lichao Sun 9.* Xiangliang Zhangl.
University of Notre Dame, Huazhong University of Science and Technology, MBZUAI *University Washington, Peking University, University of Maryland, College Park Uniiversity of Euconsin-MMadinen MMicrosoft Research, Lehigh University
ABSTRACT
Large Language Models (LLMs) such as GPT-4 and Llama3 have significantly impacted various fields by enabling high-quality synthetic data generation and reducing dependence on expensive human-gemne -ennanngeeerteed datasets. Despite this, chal- lenges remain in the areas of generalization, controllability, diversity, and truthful- ness within the existing generative frameworks. To address these challenges, this paper presents DATAGEN, a comprehensive L.LM-powered framework designed produce diverse, accurate, and highly controllable datasets. DATAGEN is adaptable, supporting all types text datasets and enhancing the generative process through innovative mechanisms. To augment data diversity, DATAGEN incorporates an attribute- attribute-guided generation module and a group checking feature. For accuracy, it employs code-based mathematical assessment for label verification alongside retrieval-uugmented generation technique for factual validation. The framework also allows for user-specified constraints, enabling customization of the data gener- ation process to suit particular requirements. Extensive experiments demonstrate the superior quality of data generated by DATAGEN, and each module within DATAGEN plays critical role in this enhancement. Additionally. DATAGEN is applied in two practical scenarios: benchmarking LLMs and data augmentation. The results indicate that DATAGEN effectively supports dynamic and evolving benchmarking and that data augmentation improves LLM capabilities in various domains, including agent-oriented abilities and reasoning skills.
INTRODUCTION
Large Language Models (LLMs) such GPT-4 (OpenAI, 2023a), Claude (Anthropic, 2023), and Llama3 (Meta, 2023) have demonstrated excellent performance across various professional domains, including medical (Liu et al., 2023a; Zhang et al., 2024a), educational (Kasneci et al., 2023), software engineering (Qian et al., 2023), and social sciences (Li et al., 2024a;b), as well as in LLM- agent applications (Huang al., 2023a; Liu al., 2023b; Chen et al., 2024a). Given their superior generative capabilities, it is natural for researchers to explore effective methods for utilizing these models in synthetic data generation (Zhu et al., 2024a;b; Wang et al., 2024a). The primary goal is to produce high-quality, cost-effective datasets, thereby reducing the reliance on expensive human labor. Furthermore, data generated by LLMs can be utilized for data augmentation (Yu et al., 2024), dynamic evaluation (Zhu et al., 2024a;b), and model self-alignment (Sun et al., 2023).
Despite the advancements in LLM-driven data generation (Zhu al., 2024a;b; Wang et al., 2024a; Dekoninck et al., 2024a;b), which have significantly improved the data generation pipeline and reduced the human cost, some challenges remain: (1) Generalization and Controllability: Most of existing frameworks directly modify data items in original datasets in specific ways based fixed principles (Zhu et al., 2024b; Wang et al., 2024a) (e.g., add additional context or shuffle the order of
-Corresponding Authors.
Yue and Siyuan contributed equally this work.

---

Published as a conference paper at ICLR 2025
Table 1: Comparison different dataset generation frameworks. The gray checkmark means the work may achieve parts of the goal (not all).
Related Work
General -ization
Control. -lability
Diversity
Truthful
Human Intervention
New Knowledge
Dynamic Benchmark
Aug.
VyVal (Zhu et al., 2024a)
X
X
X
Val 2 (Zhu et, al,, 2024b)
X
S3Eval (lei et al., 2024)
X
X
Yu et al. (2024)
X
X
Chung et al. (2023)
X
X
X
X
X
X
Fan al. (2024)
X
X
X
X
X
Jandaghi al. (2023)
X
X
X
X
Wang al. (2024a)
X
X
MetaMath (Yu al., 2023)
X
X
Qameleon (Agrawal
2023)
X
X
X
Viswanathan ul. (2023)
Chen et (2024b)
Gandhi et (2024)
DATAGEN (Ours)
the options), which may constrain the generalization of the generated data as they do not modify the nature of the data items like the scenarios within items. Moreover, many of them are also limited to particular dataset formats or types (Yu et al., 2024; Zhu et al., 2024a), such as multiple-choice or muathematically-orieneee datasets (e.g., GSM8K (Cobbe et al., 2021)). Additionally, the lack of provisions for incorporating external constraints, like specific user requirements (e.g., users may specify the length of generated text). restricts their controllability during generation. (2) Diversity and Truthfulness: Prior efforts always overlook the need to ensure some quality aspects of the datasets like diversity and truthfulness. For instance, the direct application of L.MMs for dataset generation often leads to replication and low diversity, as LLMss may output the same answers when faced with semantically similar input. Furthermore, the propensity of LLMSs to produce hallucinations (Huang et al., 2023b; Sun et al., 2024a) can introduce factual inaccuracies, potentially degrading model performance when such datasets are used for training or fine-tuning.
To address these challenges, this paper puts forward DATA- GEN (as shown in Figure 1), unified and LLM- -.w--edeerdd framework designed to generate dataset. DATAGEN ensures the generalization, diversity, truthfulness, and con- trollability simultaneously of the generation process, pared to previous studies (as shown in Table I). DATAGEN accepts all kinds of text datasets and generates high-quality datasets based on various modules. To enrich the diver- sity of the generated datasets, DATAGEN employs range of strategies, including various hyperparameter settings, uttribute-guided generation, and group checking. To guarantee the truthfulness of the generated datasets, we propose a code-based mathematical assessment to detect and rectify potentially incorrect labels. Additionally, we adopt a Retrieval- Auggeette Generation (RAG)-based validation method to check the factuality of generated statements to ensure their truthfulness. DATAGEN integrates constraints input to align with user specifications to enhance user control over the dataset generation process. Furthermore, by employing attribute-guudee generation and difficulty enhancement, enable the generation of data covering wide range of topics while providing users with controllable difficulty levels.
Original Jatanee
Dataset descrigtion
Constraints
Large Language Models
Internal Evabuation
Cenerll Conmri
Biserdty Trathnu
Generated dataset
Post-Processing
Our proposed DATAGEN for dataset generation via LLMs.
Figure 1:
To summarize, the key contributions of this paper are as follows:
We introduce DATAGEN, a unified framework for generating textual datasets via LLMs, which accepts the original dataset, description, and user constraints, and integrates modules to ensure diversity, truthfulness, and controllability.
We extensively evaluate DATAGEN across data characterization, module efficacy, human evaluation, error analysis, and cost analysis, confirming its proficiency in dataset generation and highlighting promising future research directions.
2

---

Published as a conference paper at ICLR 2025
1
Dataset Input
2 Generation Hint
4
Post Processing
Basc Dataset
CSMBK, MMLU, NMLD, TrutiuuiA....
Example Selvction for ItI
Diffically Enhancemen
Adding choices
Addling uxdin contect
Random sampling from base datcset
Datasett Description
The high level specifies
Diverse example selection by clastering
Paraphenaing question
Checking
Goneration
Comsirmini
Specify fine grained conditions
styperparameter
Diversity Setting
Sanilarty mattix
Attiibute Guideded Generation Group
Internal Evaluation
fiissing
Tiredhold wetting
Applications
Quality Assessment and Fnhancement
Code-Ban Based Mathematical
Trathfulness Validation by
Self- reflection
Original labul
Censrated
Keywords
Dynamic Evalving
Benchmarking
Self- entlancomnn
Lsgood?
Yes
i
Compare
Description
WIKIEDIÄA
Data Augiennttiio
Iteration threshold
Simulsted enviesneeet
Generated label
Large langrege model
Facinality Erroes
runing
Fine
Apent AMlities
Reasoning
Figure The architecture of DATAGEN.
We explore two applications of DATAGEN:
benchmarking LLMs and data augmentation. Key insights include: 1) Most LLMs struggle with math-oriented datasets generated by DATAGEN (e.g.. GSM8K). II) The performance of LLMs varies significantly across datasets generated by different LLMs. IIII LLMs' capabilities across various aspects (e.g., agent-related abilities, reasoning skills) can be improved by fine-tuning based the generated data. IV) An improvement of data augmentation exists in knowledge-intensive datasets.
2 DATAGEN FRAMEWORK
In this section, we will introduce the proposed DATAGEN, unified framework for dataset generation. DATAGEN consists of four modules (as shown in Figure 2) including framework input, generation hint, internal evaluation, and post-processing. Formally, consider original dataset D, the proposed framework F, which operates by iteratively sampling subsets S; from D (i.e., example selection for few-shot learning in Section 2.2). For each subset, F applies transformations based on the dataset's description M(D) and set of constraints C. The final generated dataset, Dgen. is accumulated over N iterations: Dgen N' M(S), During generation, the objectives of DATAGEN focus on maximizing the generalization, controllability, diversity, and truthfulness of the generated dataset.
2.1 FRAMEWORK INPUT
The input for DATAGEN comprises three components: base dataset, dataset description, and genera- tion constraints: The base dataset is provided in standardized JSON format, which may include text with label or standalone text (e.g., "text with a label" or "single text"). The datasel description artic- ulates the specifies of the base dataset at high level, furnishing foundational guidance for the LLM to synthesize a dataset analogous to the original. While optional, the generation constraints (Zhou al., 2023) specify fine-graine reeeggained conditions under which the LLM operates during dataset generation. For instance, constraints might stipulate that "Do not generate text longer than 20 words" or "Include an emoji in each generated sample", thereby restricting specific conditions of the synthetic dataset.
2.2 GENERATION HINT
Few-Shot Learning. The base dataset typically comprises hundreds of data items; however, incorpo- rating all these items directly into the prompt may result in an excessively long context that could obscure the comprehension capabilities of LLMs and incur substantial costs (Bai et al., 2023). To mit- igate these challenges, few-shot learning techniques are employed for dataset generation (Brown et al., 2020; Wang et al., 2020). Within DATAGEN, two principal methods are utilized to select few-shot learning examples. The first method involves random sampling from the base dataset, effectively reducing both generation time and associated costs. The second method focuses on enhancing the diversity of examples, thereby guiding L.MSs to generate as varied dataset as possible. Specifically,
3

---

Published as a conference paper at ICLR 2025
DATAGEN initially encodes all data items using OpenAl's text--nbeeddnng--da--a (OpenAI, a) to create an embedding list. Subsequently, a clustering algorithm (c.g., K-means (Hartigan and Wong, 1979)) is applied to form n clusters, where represents the desired number of examples. One example is randomly selected from each cluster, yielding a set of n diverse examples.
Diversity Setting. To augment the diversity of the generated data, we implement two strategies: (1) Hyperparameter Setting. The content generated by LLMS is influenced by various factors, with hyperparameters such as lemperature, top-k, and top-p being crucial. To maximize the diversity of the dataset, we manipulate these hyperparameters, particularly the temperature settings. (2) Altribute- Guided Generation. Drawing on insights from prior research (Huang et al., 2023a; Yu et al., 2024), we formalize the attribute-guudee a set of attributes, such as "economies" and "sports", intended to guide the generation process. We model the generation process as function where the output text is function of the input prompt and vector of attributes a E A. The generation process can be expressed as y/ = P(x:, a), where P represents the generation model of the LLM, and is the input prompt. To implement this, employ two distinct strategies: the first involves directly incorporating user-input customized attributes, and the second requires asking LLMs to extract necessary attributes from given data examples (the prompt template is shown in Appendix D). (3) Group Checking. To ensure diversity among the generated items, a similarity matrix employed identify and filter pairs of data items exhibiting high similarity. Further details on this process are provided in Section 2.4.
text generation process for LLMs. Let A = {ai, a2,..... ................................ an } be
2.3
INTERNAL. EVALUATION
Overall Quality Assessment and Enhancement. After obtaining the raw generated data, it's important to enhance their overall quality as during the generation, LLMs may overlook some details so as to mistake like deviating from the dataset description. Inspired by recent studies about self-evaluation and self-alignment (Ji et al., 2023; Ren et al., 2023; Huang et al., 2023c; Jain et al., 2023; Sun et al., 2023; Wang et al., 2023), we leverage LLMs themselves to improve the quality of generated data. The process involves two primary steps: (1) Delf-Reffection. Each generated data item initially subjected to a self-reflection phase, wherein LLMs assess the item determine errors and potential areas for enhancement. The output of self-reflection contains two parts: whether the given data needs to be enhanced and the reason why it needs enhancement. (2) Relf-Ennancement. When L.LMs recognize the necessity for improvements, both the reflective insights and the data item itself are re-input into the LLM to generate an improved version. By establishing a threshold for the number of iterations and repetitively applying these steps, DATAGEN effectively elevates the overall quality of the generated items.
Code-Based Mathematical Evaluation. In generating mathematics- datasets, such as GSM8K (Cobbe et al., 2021), it has been observed that a proportion of generated labels are factually incorrect. To address this issue, we employ a code-based mathematical evaluation method to verify the accuracy of generated labels. As highlighted in the recent study by (Gou et al., 2024; Chen al., 2023), the use of tools (e.g.. Python function) can substantially improve reasoning performance. Motivated by this finding, we require the LLM to generate Python code to solve the given math-related problem. The code is then executed within simulative environment to produce a solution. The code-verified answer(iLe., label) is subsequently compared with the original LLM- L-M--enerated answer. If they conflict, the original LLM- generated answer will be replaced with the code-verified answer.
Truthfulness Validation by RAG. Ensuring the truthfulness of generated golden answers is cru- cial when creating datasets that require factual knowledge. Prior studies have utilized Retrieval- Augmented Generation (RAG) to enhance the factuality and reduce the incidence of hallucinations in LLMs (Aksitov et al., 2023; Li et al., 2024c; 2022: Gao et al., 2024a). To combat hallucinations within the generated data, we implement a RAG-based validation process in DATAGEN. Specifically, the LLM first identifies keywords from the generated text. Subsequently, DATAGEN retrieves relevant descriptions based on these keywords from the Wikipedia database, as demonstrated in prior research (Semnani et al., 2023). These descriptions are then used as prompts to guide the I.I.M in detecting and correcting any discrepancies or errors in the generated content.
2.4
Difficulty Enhancement. Given that the dataset is produced by LLMs, the complexity of the generated data is occasionally insufficient challenge L.MSs their capabilities evolve. To address
4

---

Published as a conference paper at ICLR 2025
this, and inspired by prior research (Wang et al., 2024a; Zhu et al., 2024b), implement several strategies to increase the data's difficulty. These strategies are designed to elevate the challenges faced by LLMs in processing and responding the data. The applied policies include: (1) Paraphrasing Question: Reformulate the phrasing to express the same idea with greater sophistication. (2) Adding Extra Context into Question: Integrate additional context details that, while not directly aiding in the question's 's resolution, enhance the question's 's complexity. (3) Paraphrasing The Choices: Each option should be rephrased to reflect the same concept or idea as the original. The essence and meaning must be preserved. If an option cannot be paraphrased without altering its meaning, it should remain unchanged. (4) Adding New Choice: Introduce plausible but incorrect option to the existing choices to create ambiguity and require deeper understanding.
To mitigate the issue of high similarity among generated data items, group- checking mechanism is implemented to identify and eliminate duplicates. Specifically, we utilize OpenAl's text-exeeeeeee -ebeedinn -aaeeeddiiinad-d---aaaaaaaaa da-002 (OpenAl, a) to compute embeddings for all generated items. Let X {II, 22, ................................ } } be the set of generated data items, and e, be the embedding of item x, computed via text embeddiing ada 002. We define the similarity matrix S where the
Group Checking.
element Sij is given by Ssy =V E eyx) representing the Euclidean distance between the embeddings of items H; and z, Data items exhibiting similarity exceeding predefined threshold are filtered out to ensure diversity within the dataset. Formally, if sij < 0 for any pair (i, j), at least one of the items 2; or 2j is randomly removed from the final dataset.
EXPERIMENTS AND APPLICATIONS
3.1
EXPERIMENTAL SETUP
Rdfiection Quality (Better)
Case Quality (Better) Dane Quality (Worse
Rel lctioon Quality (Nons)
Type
GSM8K MMLU TruthfulQA HellaSwag
1.00
0.75
Generated
0.663
0.744
0.743
0.680
Percentage
Original 0.681
0.746
0.745
0.742
2.64%
0.27%
0.27%
8.36%
0.00
TruthtuiA
MMLU
Table 2: Remote- Clique of generated data and original data. A is the difference between them.
Figure 3: Human evaluation of overall quality assessment and enhancement.
To thoroughly evaluate the effectiveness of DATAGEN, carefully select four representative benchmark datasets: GSM8K (Cobbe et al., 2021), TruthfulQA (Lin et al., 2022), MMLU (Hendrycks al., 2021a), and HellaSwag (Zellers et al., 2019). Each dataset uniquely contributes language model assessment, covering dimensions from mathematical problem-solving and factual accuracy verification to extensive language understanding and commonsense reasoning. We show the details of these four datasets in Appendix C. For dataset generation, we utilize GPT-4 (OpenAI, 2023a), Claude3-Opus (Anthropic, 2023), and Llama3-70b (Meta, 2023), as these LLMs are among the most robust available, exhibiting exceptional ability to follow instructions. For benchmarking. our study utilizes eight popular models from notable entities in the AI domain (the details are shown in Appendix C.), reflecting mix of open-source and proprietary technologies. The number of generated data items and more details are shown Appendix E. Note that difficulty enhancement not applied to the generated data for benchmarking. We will discuss the efffcctivnness of difficult enhancement in Section 3.3. All LLMs utilized for generation share the same prompt templates.
3.2 CHARACTERIZING GENERATED DATA
Length. As depicted in Figure 4a, the length distribution of all generated datasets approximates a normal distribution. Notably, except for the HellaSwag dataset (as the length the original HellaSwag dataset looks like bimodal distribution), the distributions of other datasets closely resemble those of their original datasets. This similarity indicates that DATAGEN effectively mimics the distribution of the original data, thereby enhancing the reliability of the generated datasets.
5

---

Published as a conference paper at ICLR 2025
Self-BLEU Score
1.2
Word Count
300
Original
Geterated
Original
Geperated
0.8
200
0.4
100
0.0
GSMBK
HellaS weg
MMOU
TruihtulQA
GSM6K
HellaSwag
MMLU
TruihtuIQA
(a) Len. distribution of generated and original data.
(b) Self-BLEU of generated and original data.
Figure 4: Length and the self-BLEU score of generated data and original data.
30-
GSMIS
40
(speersie) (wigiaal)
(enignm))
GSMPI (saignal)
20-
20-
10-
10
10-
-10-
10
-10-
-20-
-20
-30
-30
-20
20
-40
-55
25
-10
20
-40
-20
0
20
Figure 5: Semantic embedding of different datasets. text-embedding--de a02-002 (OpenAI, a) obtain text embedding.
We
use
OpenAl's
Semantic Embedding. As illustrated in Figure 5, the distribution of the generated dataset is encompassed within the distribution of the original dataset. This observation indicates that the data items generated are semantically aligned with the original data, confirming their semantic correctness.
Diversity. Analogous to the length distribution, the distribution of the self-BLEU score (Zhu et al., 2018) (as depicted in Figure 4b)-a metric employed to assess text diversity-indicatte that the diversity of the generated data closely aligns with that of the original dataset. This alignment underscores the exceptional capability of DATAGEN to replicate the diversity inherent in the original dataset, demonstrating its effectiveness in producing varied textual content. Additionally, we utilize remote-clique metric, as applied in prior research (Cevallos al., 2018), to measure the diversity of the generated data. The related statistics are presented in Table 2. Observations reveal that the remote-clique scores of the original and generated data are closely matched, with less than 10% variance, affirming that generated data maintains high level of diversity comparable to the original dataset.
Knowledge Richness Introduced. In contrast to prior research (Zhu et al., 2024a;b; Wang et al., 2024a), DATAGEN innovates by generating entirely new data items, rather than merely modifying existing answers. This approach introduces novel scenarios and knowledge. We assess the knowledge richness of the data generated by DATAGEN and compared it to the previous study (i.e., Dyval2 (Zhu et al., 2024b)) by calculating the entity overlap rate-how many entities appear both in the generated and original data. A lower overlap rate indicates that the framework is introducing more new knowledge. According to our findings, presented in Table 3, DATAGEN demonstrates an average overlap rate of only 3. 3.83%, significantly lower than that of Dyval2 (Zhu et al., 2024b). This substantial reduction overlap rate signifies that our framework excels incorporating new knowledge into generated datasets.
Influence temperature. We examine the impact of temperature settings on the diversity of data generated by GPT-4. For this purpose, we select few items from the TruthfulQA dataset to use as examples few-shot learning. We conduct experiments using temperature settings of 0 and I. Our findings indicate that the Remote-Clique score (Cevallos et al., 2018) a temperature of 0 is 0.683, whereas, at a temperature of 1, it increases to 0.721. This suggests that adjusting the temperature setting can significantly enhance the diversity of the generated data.
3.3 EFFECTIVENESS OF MODULES IN DATAGEN
In this section, we validate the effectiveness of modules in DATAGEN. To simplify the analysis, our evaluation is based on the GPT-4 generated data: (1) Diversity Setting. As demonstrated in Table

---

Published as a conference paper at ICLR 2025
Table 3: The knowledge richness comparison between different principles in Dy (Zhu al., 2024b) and DATAGEN. The principle 1, 2, 3, and 4 are paraphrasing questions, paraphrasing choices, adding extra context to questions, and adding new choice.
Baseline
HellaSwag
MMLU
TruthfulQA
Avg.
DyVal2-pring
24.30%
61.30%
51,40%
45.67%
Dy Dy
40.50%
65.70%
46.20%
50.80%
Dy Val2--prin.3
27.00%
62.70%
57.30%
49.00%
Dy Valz-priin4
51.40%
71.00%
47.60%
56.67%
DATAGEN
5.40
3.30% %
2.80%
3.83%
GSMBK
Hella Swag
TruthruiQA
MMLU
60
Pereraage (94)
Pertantagee [S]
Pereatinge (5)
Tinunoo [S]
40
Epech
Epech
Figure 6: The percentage of different epoch counts in four datasets.
the DATAGEN modules significantly enhance the diversity of the generated data. Specifically, the remote-clique score of the initially generated data stands at 0.695. However, the introduction of setribute-guider generation elevates the remote-clique score to 0.735. Furthermore, the implementa- tion of group checking further increases this score to 0.743. (2) Overall Quality Assessment and Enhancement. To evaluate the effectiveness of our quality assessment and enhancement module, we conducted human evaluations focusing on two key aspects: (I) Comparing the quality between original and enhanced data items; (II) Assessing the reasonableness of the reflections. As illustrated in Figure 3, the results indicate that almost all reflections were deemed reasonable by the evaluators. Furthermore, over 80% of the enhanced data items were rated as superior in both datasets. These findings underscore the effectiveness of our module. (3) Difficulty Enhancement. As demonstrated in Table 6, it is observable that the performance of most of the LLMs declined when compared to their performance on the baseline-generated datasets after the application of difficulty enhancement. This result underscores the effectiveness of difficulty enhancement, which suggests its potential utility in preventing data contamination (Dong et al., 2024; Golchin and Surdeanu, 2024; Xu et al., 2024a). Such techniques may thus contribute significantly to improving the robustness of L.LMs against overfitting to training datasets. (4) Code-Based Mathematical Evaluation. As depicted in Table 4, our code-based evaluation methodology has significantly enhanced the correctness of the generated data, improving from an initial accuracy of 44% to 92%. (5) Truthfulness Validation by RAG. As detailed in Table 4, the RAG-based validation corrected 4.2% of the examples, demonstrating its effectiveness. This percentage also highlights the high quality of the dataset generated by GPT-4, which contains only a few errors. The correctness of (4) and (5) are also manually evaluated, which of the details can be found in Appendix D.
In Appendix F, we investigate the impact of temperature settings on data diversity and evaluate the adherence of L.LMs in DATAGEN to user constraints. Our findings reveal that adjusting the temperature setting enhances the diversity of generated data. Furthermore, LLMs within DATAGEN effectively follow user-imposed constraints in both individual and combined scenarios. We also provide cost analysis of DATAGEN in Appendix F, demonstrating that DATAGEN generates datasets significantly low cost.
Table 4: Effectiveness of each module in DATAGEN.
Diversity Enhancement
Code-based.
RAG Validation
Raw
+Attribuse Guided
+Group Checking
Raw
+Val Walidation
Corrected Percentage
0.695
0.735 (5.8%
0.743 (6.9%
44%
88%
4.2%
7

---

Published as a conference paper at ICLR 2025
Error Type
GSM8K HellaSwag
MMLU TruthfulQA
Human SOTA
Factuality Error
41%
14%
69%
79%
0.9
Format Error
20%
29%
0%
Accuraie
0.8
Multiple Answers
0%
43%
0%
0%
Question Error
39%
14%
23%
21%
0.6
0.5
Table Proportion of different errors. Multiple answers mean the question is considered to have multiple correct answers after human evaluation. Question errors mean the question has quality flaws like unclear statements.
Hel a0wag
Figure 7: Performance of human and the best LLM (SOTA LLM) on four generated datasets.
3.4 HUMAN PERFORMANCE ON GENERATED DATASET
As depicted in Figure 7, the performance comparison between humans and LLMs reveals distinct outcomes across various datasets. In the HellaSwag dataset, human performance slightly surpasses that of LLMs. However, in the other three datasets, LLMs demonstrate superior performance. Notably, in the GSM8K dataset, the accuracy of human responses is lower than that of the best-performing LLM. For the TruthfulQA and MMLU datasets, which require extensive knowledge, humans perform significantly worse than LLMs, which benefit from training large, diverse corpora. More details about evaluating human performance are shown in Appendix D.
3.5 ERROR ANALYSIS
To examine the errors present in the generated dataset, we conducted a human evaluation for error analysis. We observe significant factuality errors in datasets such GSM8K, TruthfulQA, and MMLU, primarily because these datasets contain responses that are fact-based (e.g., arithmetic question answers). This observation underscores the necessity for enhancements the accuracy of provided answers. Despite the robust instruction followoinnnn capabilities of GPT-4, it occasionally struggles with data formatting issues. Such errors could be mitigated through clearer prompts or by employing an integrated framework like LangChain" Additionally, our analysis of the HellaSwag dataset revealed the presence of multiple viable answers for certain prompts, highlighting the need for more comprehensive answer validation mechanism. We discuss the potential improvement by mitigating these errors in Appendix
3.6 COST ABLATION ANALYSIS
We conduct cost analysis of DATAGEN. Specifically, we calculate the total token usage and the corresponding for generating data across four datasets: MMLU, HellaSwag, TruthfulQA, and GSM8K. The details are presented in Figure 8.
For generated item without RAG-based validation and code-based evaluation, the cost is most S0.038 using the GPT-4-Turbo API. When incorporating RAG-based validation, the average cost per generated item increases $0.190, due to the large volume of tokens processed from the retrieved content. Adding code-based evaluation raises the cost to $0.040. Overall, the total cost for generating each item, including all validation and evaluation processes, will not exceed $0.200. This cost, although significant, is substantially lower than the cost of human labor. denncd masc
murd sramo Sembarre
0.05
Tokeen Nummer
1ost
0.04
101
0.03
30
Number
Figure 8: Cost (dollar) on different epoch numbers of overall quality assessment and enhancement (Left), and the token number cost of each part DATAGEN.
'https: //g thub. waw.hhainggaaiaaa a- haa-aai///nghhhhhaaaa /1agghhain
8

---

Published as a conference paper at ICLR 2025
Table 6: LLMs' performance on baseline generated (i.e. gen.) dataset, challenge or difficulty enhanced dataset (i.e., cha.), and their differences (i.e., diff.).
GSMBK
MMLC
HellaSwag
TruthfulQA
Model
gen.
diff.
gen.
diff.
gen.
dif.
gen..
diff
ChatGPT
0,665
0,585
0,080
0,798
0.633
0,165
0,960
0.924
0.036
0.816
0.718
0.098
Claude-3
0,778
0,670
0.108
0,903
0.725
0.178
0.935
0,880
0.055
0.919
0.810
0.109
Liama3-700
0.689
0.637
0.052
0,857
0,703
0.154
0,949
0,884
0.065
0.914
0.743
0.171
Llama3-8h
0.613
0,557
0.056
0.741
0.576
0.165
0.793
0.699
0.094
0.795
0.676
0.119
Mistral-7b
0.377
0,321
0.056
0,709
0.437
0.272
0.696
0.467
0.229
0.738
0.452
0.286
Mixtral-8x7b
0,509
0,439
0.070
0,851
0,616
0.235
0.511
0.373
0,138
0.824
0.648
0,176
Vi-34b
0.637
0.509
0.128
0.815
0.633
0.182
0.572
0,522
0.050
0.857
0.657
0.200
Table 7: The main results generated datasets (i.e., gen.) and original datasets (i.e., ori.).
GSMSK
MMLU
TruthfulQA
HellaSwag
Dataset
ori
gen.
ori
gen. ori
gen.
GPT-7 Generition
ChaiGPT
0.762
0.663
0.6099
11.798
0..225
0.837
0.611
0.960
Claude-3
0.953
0.778
0,810
0.903
0.835
0.919
0.888
0.935
Llama3-70b
0.890
0.689
0.755
0.857
0,750
0.914
0.836
0.949
0.565
0.741
0.795
0.793
Mistral-7b
0,490
0,709
0.738
0.600
Mixtrul-8<7b
0.610
0.509
0.720
0.851
0.640
0.824
0.712
0.511
YI-34b
0.687
0.637
0.645
0.815
0.485
0.857
0.740
0.572
Clande-3-0pu
Generation
ChaiGPT
0.762
0.405
0.609
0,802
0.432
0.744
0.538
0.712
GPT-4
0.947
0.508
0.725
0.848
0.841
0.888
0.736
0.835
Llama3-70b
0.890
0.444
0.755
0,846
0,750
0.854
0.836
0.769
Llama3-8b
0.565
0,780
Mistral-7b
0,313
0.158
0,490
0.709
0,380
0.621
0.580
0.690
Mixtral-s<7b
0.610
0.291
0.720
0.717
0.640
0.680
0.600
0.565
Yi.34b
0.687
0.323
0.645
0.751
0.480
0.694
0.644
0.584
3.7 APPLICATION-I: BENCHMARKING LLMS
We present the benchmarking results based on GPT-4 and Claude3 generated data for seven pop- ular LLMs in Table 7 (the benchmarking results based on Llama3-70b's generation are shown in Appendix F). The analysis yields several key observations:
Performance decline on generated GSMSK dataset: Almost all LLMs exhibit performance drop on the generated GSM8K dataset compared the original. This suggests that the reasoning capabilities of many LLMs may be overstated, aligning with recent findings (Zhang et al., 2024b; Mirzadeh al., 2024; Zhang et al., 2024b), which indicate overfitting on the GSM8K dataset by
some LLMs.
Superior performance on kmowledge-requiie datasets: For datasets requiring extensive knowl- edge, such as MMLU and TruthfulQA, LLMs achieve higher accuracy the generated versions. This indicates that the knowledge necessary to address these queries is within the LLMs' capabili- ties, suggesting that the generated datasets relatively less challenging. Further enhancements increase difficulty are detailed in Table 6.
Challenging nature of Claude3-generated dataset: LLMs generally perform worse on datasets generated by Claude3 compared to those by GPT-4. This may imply that some LLMs might have been trained augmented with GPT-4 generated data (e.g., Phi-3 (Abdin al., 2024)), highlighting content.
the unique challenge of
3.8 APPLICATION-II DATA AUGMENTATION
Using data augmentation with LLMs has been widely explored in previous studies (Dai et al., 2023; Whitehouse et al., 2023; Møller et al., 2024). In this section, implement DATAGEN augment data in ten popular datasets (the details of datasets are shown in Appendix C). We include the experiment setting in Appendix E. From Figure 9, we observe that:

---

Published as a conference paper at ICLR 2025
50%
4012
Wnle
100%
07
Medo
Figure 9: Results of data augmentation Llama2-78
Llama3-88 and Mistral-7b.
Table 8: Model performance scores in MTBench.
Model
First Turn Score
Second Turn Score
Average Score
lamma-7b-bbse
2.325
1.744
2.038
Hamaa-7b-alpeee alpaca-originne
6.049
4.450
5,259
Bamaa--b-alpaca-ennse
6.981
5.825
6.403
The data augmentation powered by DATAGEN is effective. Performance across all ten datasets improved when trained with the DATAGEN- -eeerrgge dataset, highlighting the efficacy of our generated data and indicating broader potential applications for DATAGEN across extensive datasets. DATAGEN enhances LLMs from various capability aspects. The enhancements in various aspects of LLM capabilities due to the generated data are notable. For example, performance improvements in the Metatool dataset (Huang al., 2023a) (i.e., tool selection ability) indicate that DATAGEN enhance agent-oriented capabilities of LLMs. Additionally, enhancements in reasoning abilities are evident in datasets such as GSM8K (Cobbe et al., 2021) and both the BBH (bool/casual) (Suzgun al., 2022).
Improvement on knowledge-intensin datasets still leaves much to be desired. The gains in datasets requiring extensive knowledge (e.g., TruthfulQA (Lin et al., 2022)) are comparatively modest. This limited improvement may be due to LLMs acquiring most of their knowledge during pretraining, and the additional 200 training samples may not significantly impact performance on related tasks. Notably, the Llama2-76 7b model shows performance decline on TruthfulQA after fine-tuning, possibly due hallucinations introduced when new knowledge acquired during fine-tuning rather than pretraining (Gekhman et al., 2024). We discuss the potential measurement for enhancing in Appendix A.
Moreover, we extend analysis to include general instruction tuning data. Specifically, utilize the alpaca dataset Taori et al. (2023) for additional fine-tuning on the Llama3-base model and evaluated the outcomes using the MT-Bench Zheng et al. (2023). The "genset" model, fine-tuned on 1,000 data points generated by DATAGEN, consistently outperforms the "original" model, which is fine-tuned on equivalent sample of 1,000 existing data points from the alpaca dataset. This comparison demonstrates that our framework effectively generates high-quality. diverse instruction-tun tuninn data, demonstrating its practical utility in enhancing model performance.
CONCLUSION
this paper, we hava proposed DATAGEN, a unified dataset generation framework powered by LLMs, which addresses key challenges in diversity, accuracy, and controllability. Its innovative modules and features, ensure high-quality, customizable datasets. The extensive experiments demonstrated the effectiveness of DATAGEN. Moreover, DATAGEN benchmarking as well as data augmentation. We believe that the insightful findings revealed this study will serve as foundation for future research data generation.
be applied in dynamic and evolving
10

---

Published as a conference paper at ICLR 2025
REFERENCES
OpenAl. Chatgpt, 2023a. httppp///ppeeaa..cmmm/////edde/p
Anthropic. Claude, 2023.
Meta. Llama 3, 2023, https:://llaaa.netaa.comm
Zhengliang Liu, Yue Huang, Xiaowei Yu, Lu Zhang, Zihao Wu, Chao Cao, Haixing Dai, Lin Zhao, Yiwei Li, Peng Shu, et al. Deid-gpt: Zero-shot medical text de-identification by gpt-4. arXiv preprint arXiv:2303. /032, 2023a.
Kai Zhang, Jun Yu, Eashan Adhikarla, Rong Zhou, Zhiling Yan, Yixin Liu, Zhengliang Liu, Lifang He, Brian Davison, Xiang Li, Hui Ren, Sunyang Fu, James Zou, Wei Liu, Jing Huang, Chen Chen, Yuyin Zhou, Tianming Liu, Xun Chen, Yong Chen, Quanzheng Li, Hongfang Liu, and Lichao Sun. Biomedgpt: A unified and generalist biomedical generative pre-trained transformer for vision, language, and multimodal tasks, 2024a.
Enkelejda Kasneci, Kathrin Seßler, Stefan Küchemann, Maria Bannert, Daryna Dementieva, Frank Fischer, Urs Gasser, Georg Groh, Stephan Günnemann, Eyke Hüllermeier, et al. Chatgpt for good? on opportunities and challenges large language models for education. Learning and individual differences, 103:102274, 2023.
Chen Qian, Xin Cong, Cheng Yang, Weize Chen, Yusheng Su, Juyuan Xu, Zhiyuan Liu, and Maosong Sun. Communicative agents for software development. arXiv preprint arXiv:2307.0792. 2023.
Yuan Li, Yue Huang, Yuli Lin, Siyuan Wu, Yao Wan, and Lichao Sun. I think, therefore am: Awareness in large language models. arXiv preprint arXiv:2401. 10001.177888 /7882, 2024a.
Yuan Li, Yue Huang. Hongyi Wang, Xiangliang Zhang, James Zou, and Lichao Sun. Quanti- fying ai psychology: A psychometrics benchmark for large language models. arXiv preprint arXiv:2406. 17675, 2024b.
Yue Huang, Jiawen Shi, Yuan Li, Chenrui Fan, Siyuan Wu, Qihui Zhang, Yixin Liu, Pan Zhou, Yao Wan, Neil Zhenqiang Gong, al. Metatool benchmark for large language models: Deciding whether to use tools and which to use. arXiv preprint arxiv:2310.03128 2023a.
Xiao Liu, Hao Yu, Hanchen Zhang, Yifan Xu, Xuanyu Lei, Hanyu Lai, Yu Gu, Hangliang Ding, Kaiwen Men, Kejuan Yang. Shudan Zhang, Xiang Deng, Aohan Zeng, Zhengxiao Du, Chenhui Zhang, Sheng Shen, Tianjun Zhang, Yu Su, Huan Sun, Minlie Huang, Yuxiao Dong, and Jie Tang. Agentbench: Evaluating Ilms as agents, 2023b.
Dongping Chen, Yue Huang, Siyuan Wu, Jingyu Tang, Liuyi Chen, Yilin Bai, Zhigang He, Chenlong Wang, Huichi Zhou, Yiqiang Li al. Gui-world: dataset for gui-oriented multimodal llm-based agents. arXiv preprint arXiv:2406. 10819, 2024a.
Kaijie Zhu, Jiaao Chen, Jindong Wang, Neil Zhenqiang Gong, Diyi Yang, and Xing Xie. Dyval: Dynamic evaluation of large language models for reasoning tasks, 2024a.
Kaijic Zhu, Jindong Wang, Qinlin Zhao, Ruochen Xu, and Xing Xic. Dyval 2: Dynamic evaluation of large language models by meta probing agents, 2024b.
Siyuan Wang, Zhuohan Long, Zhihao Fan, Zhongyu Wei, and Xuanjing Huang. Benchmark self- evolving: A multi-agent framework for dynamic Ilm evaluation, arXiv preprint arXiv:2402.1 11443, 2024a.
Yue Yu, Yuchen Zhuang, Jieyu Zhang, Yu Meng, Alexander J Ratner, Ranjay Krishna, Jiaming Shen, and Chao Zhang. Large language model as attributed training data generator: A tale of diversity and bias. Advances in Neural Information Processing Systems, 36, 2024.
Zhiqing Sun, Yikang Shen, Qinhong Zhou, Hongxin Zhang, Zhenfang Chen, David Daniel Cox, Yiming Yang, and Chuang Gan. Principle-driven self-aligmment of language models from scratch with minimal human supervision. In Thirty-seventhh Conference on Neural Information Processing Systems, 2023. URL https:////// /ttp///ppevviee/eeeeee/eeeeeeeee
wew..wwwww.. ew. net htttht://////eeeee
rum htt.e/oorouuniteeooooooooB 6=x40x
11

---

Published as a conference paper at ICLR 2025
Jasper Dekoninck, Mare Fischer, Luca Beurer- and Martin Vechev. Understanding large language models through the lens of dataset generation, 2024a. URL / met/forunte aaa.umiiiimmmmmmmiiahpiha
Jasper Dekoninck, Marc Fischer, Luca Beurer-Kellner, and Martin Vechev. Controlled text gen- eration via language model arithmetic. In The Twelfth International Conference on Learning Representations, 2024b. URL https:/ http/ppenreeeeeeee / wwe.tieweeeeeeeeeeeeeme..ee.eeee forumerrmiomm SLw
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, Christopher Hesse, and John Schulman. Training verifiers to solve math word problems. arXiv preprint arXiv:2 10. 10.1468, 2021.
Lei Huang, Weijiang Yu, Weitao Ma, Weihong Zhong, Zhangyin Feng, Haotian Wang, Qianglong Chen, Weihua Peng, Xiaocheng Feng, Bing Qin, and Ting Liu. A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions, 2023b.
Lichao Sun, Yue Huang, Haoran Wang, Siyuan Wu, Qihui Zhang, Yuan Li, Chujie Gao. Yixin Huang, Wenhan Lyu, Yixuan Zhang, Xiner Li, Zhengliang Liu, Yixin Liu, Yijue Wang. Zhikun Zhang, Bertie Vidgen, Bhavya Kailkhura, Caiming Xiong, Chaowei Xiao, Chunyuan Li. Eric Xing, Furong Huang, Hao Liu, Heng Ji, Hongyi Wang, Huan Zhang, Huaxiu Yao, Manolis Kellis, Marinka Zitnik, Meng Jiang. Mohit Bansal, James Zou, Jian Pei, Jian Liu, Jianfeng Gao, Jiawei Han, Jieyu Zhao, Jiliing Tang, Jindong Wang. Joaquin Vanschoren, John Mitchell, Kai Shu, Kaidi Xu, Kai-Wei Chang, Lifang He, Lifu Huang, Michael Backes, Neil Zhenqiang Gong, Philip Yu, Pin- Yu Chen, Quanquan Gu, Ran Xu, Rex Ying, Shuiwang Ji, Suman Jana, Tianlong Chen, Tramming Liu, Tianyi Zhou, William Wang, Xiang Li, Xiangliang Zhang, Xiao Wang, Xing Xie, Xun Chen, Xuyu Wang, Yan Liu, Yanfang Ye, Yinzhi Cao, Yong Chen, and Yue Zhao. Trustllm: Trustworthiness in large language models, 2024a.
Fangyu Lei, Qian Liu, Yiming Huang, Shizhu He, Jun Zhao, and Kang Liu. S3eval: A synthetic, scalable, systematic evaluation suite for large language models, 2024.
John Joon Young Chung, Ece Kamar, and Saleema Amershi. Increasing diversity while maintaining accuracy: Text data generation with large language models and human interventions. arXiv preprint 0rXiv:23066.04440 2023.
Lizhou Fan, Wenyue Hua, Lingyao Li, Haoyang Ling, and Yongfeng Zhang. Nphardeval: Dynamic benchmark on reasoning ability of large language models via complexity classes, 2024.
Pegah Jandaghi, XiangHai Sheng. Xinyi Bai, Jay Pujara, and Hakim Sidahmed. Faithful persona- based conversational dataset generation with large language models, 2023.
Longhui Yu, Weisen Jiang, Han Shi, Jincheng Yu, Zhengying Liu, Yu Zhang, James T Kwok, Zhenguo Li, Adrian Weller, and Weiyang Liu. Metamath: Bootstrap your own mathematical questions for large language models. arXiv preprint arXiv:2309. / /2284, 2023.
Priyanka Agrawal, Chris Alberti, Fantine Huot, Joshua Maynez, Ma, Sebastian Ruder, Kuzman Ganchev, Dipanjan Das, and Mirella Lapata. Qameleon: Multilingual qa with only 5 examples. Transactions of the Association for Computational Linguistics, 11: 11:1754-1771 2023.
Vijay Viswanathan, Chenyang Zhao, Amanda Bertsch, Tongshuang Wu, and Graham Neubig. Prompt2model: Generating deployable models from natural language instructions. arXiv preprint arXiv:2308. 12261, 2023.
Mingda Chen, Xilun Chen, and Wen-tau Yih. Few-shot data synthesis for open domain multi-hop question answering. In Yettee Graham and Matthew Purver, editors, Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume I: Long Papers), pages 190-208, St. Julian's, ss Malta, March 2024b. Association for Computational Linguistics. URL https: / awlantthooooooooooooee
22g/20244 eacl- long 12.
Saumya Gandhi, Ritu Gala, Vijay Viswanathan, Tongshuang Wu, and Graham Neubig. Bet- ter synthetic data by retrieving and transforming existing datasets. In Lei Ku, Andre
12

---

Published as a conference paper at ICLR 2025
Martins, and Vivek Srikumar, editors. Findings of the Association for Computationnl Lin- guistics ACL 2024, pages 6453-6466 Bangkok, Thailand and virtual meeting, August 2024. Association for Computational Linguistics. doi: 10. http://////0222...nnnnng--- htttss://act antho ooy. www.oogg..oooooooo.............. wrg. ff Endinng-aac 385.
URL
Jeffrey Zhou, Tianjian Lu, Swaroop Mishra, Siddhartha Brahma, Sujoy Basu, Yi Luan, Denny Zhou, and Le Hou. Instruction-folloiing evaluation for large language models, 2023.
Yushi Bai, Xin Lv, Jiajie Zhang, Hongchang Lyu, Jiankai Tang, Zhidian Huang, Zhengxiao Du, Xiao Liu, Aohan Zeng, Lei Hou, Yuxiao Dong. Jie Tang, and Juanzi Li. Longbench: A bilingual, multitask benchmark for long context understanding. 2023.
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert- Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. Language models are few-shot learners, 2020.
Yaqing Wang, Quanming Yao, James Kwok, and Lionel M. Ni. Generalizing from few examples: A survey on few-shot learning, 2020.
OpenAl. lext-embedding-adzz embeddings.
a. https://platform. openai httppeai..com/bc////i..c.//c
John A Hartigan and Manchek A Wong. Algorithm as 136: A k-means clustering algorithm. Journal of the royal statistical society. series e (applied statistics), 28(1):100-108, 1979.
Ziwei Ji, Tiezheng Yu, Yan Xu, Naycon Lee, Etsuko Ishii, and Pascale Fung. Towards mitigating I1m hallucination via self reflection. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 1827-1843, 2023.
Jie Ren, Yao Zhao, Tu Vu, Peter Liu, and Balaji Lakshminarayanan. Self-evaluation improves selective generation in large language models, 2023.
Jiaxin Huang, Shixiang Shane Gu. Le Hou, Yuexin Wu, Xuezhi Wang, Hongkun Yu, and Jiawei Han. Large language models can self-improve. 2023c. URL https:://penzeetee n forum? d=Ni EtU7blu N.N.
Neel Jain, Khalid Saifullah, Yuxin Wen, John Kirchenbauer, Manli Shu, Aniruddha Saha, Micah Goldblum, Jonas Geiping, and Tom Goldstein. Bring your own data! self--upervised evaluation for large language models, 2023.
Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A. Smith, Daniel Khashabi, and Hannaneh Hajishirzi. Self-instruct: Aligning language models with self self- generated instructions. In Anna Rogers, Jordan Boyd-Graber, and Naoaki Okazaki, editors, Proceedings of the 61 Annual Meeting of the Association for Computational Linguistics (Volume I: Long Pupers), pages 13484- 13508, Toronto, Canada, July 2023. Association for Computational Linguistics. doi: 10.18653/v1/ 2023.acl- long.754. URL ht https: aclanthooogoy org/ 2azzg/2222 22222222222 023..333333.22.. acl-long. 754.
Zhibin Gou, Zhihong Shao, Yeyun Gong, yelong shen, Yujiu Yang, Minlie Huang, Nan Duan, and Weizhu Chen. ToRA: A tool-integrated reasoning agent for mathematical problem solving. In The Twelfth International Conference on Learning Representations, 2024. URL https:
//
Baian Chen, Chang Shu, Ehsan Shareghi, Nigel Collier, Karthik Narasimhan, and Shunyu Yao. Fireact: Toward language agent fine-tuning, 2023.
Renat Aksitov, Sobhan Miryoosefi, Zonglin Li, Daliang Li, Sheila Babayan, Kavya Kopparapu, Zachary Fisher, Ruiqi Guo, Sushant Prakash, Pranesh Srinivasan, Manzil Zaheer, Felix Yu, and Sanjiv Kumar. Rest meets react: Self-improvemeet for multi-step reasoning Ilm agent, 2023.
Jiarui Li, Ye Yuan, and Zehua Zhang. Enhancing Ilm factual accuracy with rag to counter hallucina- tions: A case study domain-specific queries in private knowledge-bases, 2024c.
13

---

Published as a conference paper at ICLR 2025
Zonglin Li, Ruiqi Guo, and Sanjiv Kumar. Decoupled context processing for context augmented language modeling. 2022.
Chujie Gao, Qihui Zhang, Dongping Chen, Yue Huang, Siyuan Wu, Zhengyan Fu, Yao Wan, Xiangliang Zhang, and Lichao Sun. The best of both worlds: Toward an honest and helpful large language model. arXiv preprint 2xXi::2406000880 2024a.
Sina Semnani, Violet Yao, Heidi Zhang, and Monica Lam. Wikichat: Stopping the hallucination of large language model chatbots by few-shot grounding on wikipedia. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 2387-2413, 2023.
Stephanie Lin, Jacob Hilton, and Owain Evans. Truthfulqa: Measuring how models mimic human falsehoods, 2022.
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. Measuring massive multitask language understanding. Proceedings of the International Conference on Learning Representations (ICLR), 2021a.
Rowan Zellers, Ari Holtzman, Yonatan Bisk, Ali Farhadi, and Yejin Choi Hellaswag: Can machine really finish your sentence? In Proceedings of the 57th Anmual Meeting of the Association for Computational Linguistics, 2019.
Yaoming Zhu, Sidi Lu, Lei Zheng. Jiaxian Guo, Weinan Zhang, Jun Wang, and Yong Yu. Texygen:
A benchmarking platform for text generation models, 2018.
Alfonso Cevallos, Friedrich Fisenbrand, and Sarah Morell. Diversity maximization in doubling metrics, 2018.
Yihong Dong, Xue Jiang, Huanyu Liu, Zhi Jin, and Ge Li. Generalization or memorization: Data contamination and trustworthy evaluation for large language models, 2024.
Shahriar Golchin and Mihai Surdeanu. Time travel in llms: Tracing data contamination in large language models, 2024.
Ruijie Xu, Zengzhi Wang, Run-Ze Fan, and Pengfei Liu. Benchmarking benchmark leakage in large language models, 2024a.
Hugh Zhang, Jeff Da, Dean Lee, Vaughn Robinson, Catherine Wu, Will Song, Tiffany Zhao, Pranav Raja, Dylan Slack, Qin Lyu, al. A careful examination of large language model performance on grade school arithmetic. arXiv preprint arXiv:2405.00333, 2024b.
Iman Mirzadeh, Kcivan Alizadeh, Hooman Shahrokhi, Oncel Tuzel, Samy Bengio, and Mchrdad Farajtabar. Gsm-symbolic: Understanding the limitations of mathematical reasoning in large language models. arXiv preprint arXiv:2410.05229 2024.
Marah Abdin, Sam Ade Jacobs, Ammar Ahmad Awan, Jyoti Aneja, Ahmed Awadallah, Hany Awadalla, Nguyen Bach, Amit Bahree, Arash Bakhtiari, Jianmin Bao, Harkirat Behl, Alon Ben- haim, Misha Bilenko, Johan Bjorck, Sébastien Bubeck, Qin Cai, Martin Cai, Caio César Teodoro Mendes, Weizhu Chen, Vishrav Chaudhary. Dong Chen, Dongdong Chen, Yen-Chun Chen, Yi- Ling Chen, Parul Chopra, Xiyang Dai, Allie Del Giorno, Gustavo de Rosa, Matthew Dixon, Ronen Eldan, Victor Fragoso, Dan Iter, Mei Gao, Min Gao, Jianfeng Gao, Amit Garg, Abhishek Goswami, Suriya Gunasekar, Emman Haider, Junheng Hao, Russell J. Hewett, Jamie Huynh, Mojan Javaheripi, Xin Jin, Piero Kauffmann, Nikos Karampatziakis, Dongwoo Kim. Mahoud Khademi, Lev Kurilenko, James R. Lee, Yin Tat Lee, Yuanzhi Li, Yunsheng Li, Chen Liang, Lars Liden, Ce Liu, Mengchen Liu, Weishung Liu. Eric Lin, Zeqi Lin, Chong Luo, Piyush Madan, Matt Mazzola, Arindam Mitra, Hardik Modi, Anh Nguyen. Brandon Norick, Barun Patra, Daniel Perez-Becker, Thomas Portet, Reid Pryzant, Heyang Qin, Marko Radmilac, Corby Rosset, Sam- budha Roy, Olatunji Ruwase, Olli Saarikivi, Amin Saied, Adil Salim, Michael Santacroce, Shital Shah, Ning Shang, Hiteshi Sharma, Swadheen Shukla, Xia Song, Masahiro Tanaka, Andrea Tupini, Xin Wang, Lijuan Wang, Chunyu Wang, Yu Wang, Rachel Ward, Guanhua Wang, Philipp Witte, Haiping Wu, Michael Wyatt, Bin Xiao, Can Xu, Jiahang Xu, Weijian Xu, Sonali Yaday, Fan Yang, Jianwei Yang, Ziyi Yang, Yifan Yang, Donghan Yu. Lu Yuan, Chengruidong Zhang, Cyril Zhang, Jianwen Zhang, Li Lyna Zhang, Yi Zhang, Yue Zhang, Yunan Zhang, and Xiren Zhou. Phi-3 technical report: A highly capable language model locally on your phone, 2024.
14

---

Published as a conference paper at ICLR 2025
Haixing Dai, Zhengliang Liu, Wenxiong Liao, Xiaoke Huang, Yihan Cao, Zihao Wu, Lin Zhao, Shaochen Xu, Wei Liu, Ninghao Liu, Sheng Li, Dajiang Zhu, Hongmin Cai, Lichao Sun, Quanzheng Li, Dinggang Shen, Tianming Liu, and Xiang Li. Auggpt: Leveraging chatgpt for text data augmentation, 2023.
Chenxi Whitehouse, Monojit Choudhury, and Alham Fikri Aji. LIm-powereds data augmentation for enhanced cross-lingual performance, 2023.
Anders Giovanni Møller, Arianna Pera, Jacob Dalsgaard, and Luca Aiello. The parrot dilemma: Human-labeled vs. LL -uugmented data in classification tasks. In Yvette Graham and Matthew Purver, editors, Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers), pages 179-192, St. Julian's, Malta, March 2024. Association for Computational Linguisties. URL https: httpss//allaaathllooooo org/ 2024. eacl - short.17.
Mirac Suzgun, Nathan Scales, Nathanael Schärli, Sebastian Gehrmann, Yi Tay, Hyung Won Chung, Aakanksha Chowdhery, Quoe V Le, Ed H Chi, Denny Zhou, and Jason Wei. Challenging big-bench tasks and whether chain-of-thouge can solve them. arXiv preprint 21Xiv:2210.09261, 2022.
Zorik Gekhman, Gal Yona, Roee Aharoni, Matan Eyal, Amir Feder, Roi Reichart, and Jonathan Herzig. Does fine-tuning iims on new knowledge encourage ballucinations?, 2024.
Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto. Stanford alpaca: instruction-follovinn Ilama model. https: / /g thub. com/ catsu-llab lab/ stanford_alpace 2023.
Lianmin Zheng, Win Chiang. Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric P. Xing, Hao Zhang, Joseph F. Gonzalez, and Ion Stoica. Judging llm-as-a-judge with mt-bench and chatbot arena, 2023.
Jamiig Ji, Tianyi Qiu, Boyuan Chen, Borong Zhang, Hantao Lou, Kaile Wang, Yawen Duan, Zhonghao He, Jiayi Zhou, Zhaowei Zhang, Fanzhi Zeng. Kwan Yee Ng, Juntao Dai, Xuehai Pan, Aidan O' O'Gara, Yingshan Lei, Hua Xu, Brian Tse, Jie Fu, Stephen McAleer, Yaodong Yang, Yizhou Wang, Song-Chun Zhu, Yike Guo, and Wen Gao. Ai alignment: A comprehensive survey, 2024.
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. Measuring massive multitask language understanding, 2021b. URL https://
arxiv.org/ 2009 03300.
Chujie Zheng, Ziqi Wang, Heng Ji, Minlie Huang, and Nanyun Peng. Weak-to-strong extrapolation expedites alignment. arXiv preprint 167722 2024a.
Collin Burns, Pavel Izmailov, Jan Hendrik Kirchner, Bowen Baker, Leo Gao, Leopold Aschenbrenner, Yining Chen, Adrien Ecoffet, Manas Joglekar. Jan Leike, et al. -eak-to-strong generalization: Eliciting strong capabilities with weak supervision. arXiv preprint arXiv:2312.093390 2023.
Xian Li. Ping Yu, Chunting Zhou, Timo Schick, Luke Zettlemoyer, Omer Levy, Jason Weston, and Mike Lewis. Self-alignment with instruction backtranslation. arXiv preprint arXiv:2308.06259, 2023.
Zhiqing Sun, Yikang Shen, Qinhong Zhou, Hongxin Zhang, Zhenfang Chen, David Cox, Yiming Yang, and Chuang Gan. Principle-driven self-alignment of language models from scratch with minimal human supervision. Advances in Neural Information Processing Systems, 36, 2024b.
Yupeng Chang. Xu Wang. Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, and Xing Xie. survey on evaluation of large language models, 2023.
Alejandro Lopez-Lira and Yuehua Tang. Can chatgpt forecast stock price movements? return predictability and large language models, 2023.
Wenxuan Zhang, Yue Deng, Bing Liu, Sinno Jialin Pan, and Lidong Bing. Sentiment analysis in the of large language models: A reality check, 2023a.
15

---

Published as a conference paper at ICLR 2025
Kai-Cheng Yang and Filippo Menezer. Large language models rate news outlet credibility, 2023.
Ruohong Zhang, Yau-Shian Wang, and Yiming Yang. Generation-drin contrastive self-training for zero-shot text classification with instruction-tuned gpt, 2023b.
Nick McKenna, Tianyi Li, Liang Cheng, Mohammad Javad Hosseini, Mark Johnson, and Mark Steedman. Sources of hallucination by large language models on inference tasks, 2023.
Percy Liang, Rishi Bommasani, Tony Lee, Dimitris Tsipras, Dilara Soylu, Michihiro Yasunaga, Yian Zhang, Deepak Narayanan, Yuhuai Wu, Ananya Kumar, Benjamin Newman, Binhang Yuan, Bobby Yan, Ce Zhang, Christian Cosgrove, Christopher D. Mamning, Christopher Ré, Diana Acosta-Navas, Drew A. Hudson, Eric Zelikman, Esin Durmus, Faisal Ladhak, Frieda Rong, Hongyu Ren. Huaxiu Yao, Jue Wang, Keshav Santhanam, Laurel Orr, Lucia Zheng. Mert Yuksekgonul, Mirac Suzgun, Nathan Kim, Neel Guha, Niladri Chatterji, Omar Khattab, Peter Henderson, Qian Huang, Ryan Chi, Sang Michael Xie, Shibani Santurkar, Surya Ganguli, Tatsunori Hashimoto, Thomas Icard, Tianyi Zhang, Vishrav Chaudhary, William Wang, Xuechen Li, Yifan Mai, Yuhui Zhang, and Yuta Koreeda. Holistic evaluation of language models, 2023.
Zhouhong Gu, Xiaoxuan Zhu, Haoning Ye, Lin Zhang, Jianchen Wang, Sihang Jiang, Zhuozhi Xiong, Zihan Li, Qianyu He, Rui Xu, Wenhao Huang, Zili Wang. Shusen Wang. Weiguo Zheng, Hongwei Feng, and Yanghua Xiao. Xiezhi: An ever-updating benchmark for holistic domain knowledge evaluation, 2023.
Caleb Ziems, William Held, Omar Shaikh, Jiaao Chen, Zhehao Zhang, and Diyi Yang. Can large language models transform computational social science?, 2023.
John J. Nay, David Karamardian, Sarah B. Lawsky, Wenting Tao, Meghana Bhat, Raghav Jain, Aaron Travis Lee, Jonathan H. Choi, and Jungo Kasai. Large language models as tax attorneys: A case study in legal capabilities emergence, 2023.
Neel Guha, Julian Nyarko, Daniel E. Ho, Christopher Ré, Adam Chilton, Aditya Narayana, Alex Chohlas-Wood Austin Peters, Brandon Waldon, Daniel N. Rockmore, Diego Zambrano, Dmitry Talisman, Enam Hoque, Faiz Surani, Frank Fagan, Galit Sarfaty, Gregory M. Dickinson, Haggai Porat, Jason Hegland, Jessica Wu, Joe Nudell, Joel Niklaus, John Nay, Jonathan H. Choi, Kevin Tobia, Margaret Hagan, Megan Ma, Michael Livermore. Nikon Rasumov-Rahe, Nils Holzenberger, Noam Kolt, Peter Henderson, Sean Rehaag, Sharad Goel, Shang Gao, Spencer Williams, Sunny Gandhi, Tom Zur, Varun Iyer, and Zehua Li. Legalbench: A collaboratively built benchmark for measuring legal reasoning in large language models, 2023.
Zhiwei Fei, Xiaoyu Shen, Dawei Zhu, Fengzhe Zhou, Zhuo Han, Songyang Zhang, Kai Chen, Zongwen Shen, and Jidong Ge. Lawbench: Benchmarking legal knowledge of large language models. arXiv preprint arXhv:2309.1222 2023.
Michael Frank. Baby steps in evaluating the capacities of large language models. Nature Reviews Psychology, 2, 06 2023. doi: 10. 00.100388441199-22-001111-
Yue Huang, Qihui Zhang, Philip S. Y, and Lichao Sun. Trustgpt: A benchmark for trustworthy and responsible large language models, 2023d.
Boxin Wang, Weixin Chen, Hengzhi Pei, Chulin Xie, Mintong Kang, Chenhui Zhang, Chejian Xu, Zidi Xiong, Ritik Dutta, Rylan Schaeffer, Sang T. Truong, Simran Arora, Mantas Mazeika, Dan Hendrycks, Zinan Lin, Yu Cheng. Sanmi Koyejo, Dawn Song, and Bo Li. Decodingtrust: A comprehensive assessment of trustworthiness in gpt models, 2024b.
Chujie Gao, Siyuan Wu, Yue Huang, Dongping Chen, Qihui Zhang, Zhengyan Fu, Yao Wan, Lichao Sun, and Xiangliang Zhang. Honestllm: Toward an honest and helpful large language model. In The Thirly-eighth Annual Conference on Neural Information Processing Systems, 2024b.
Yue Huang, Zhengqing Yuan, Yujum Zhou, Kehan Guo, Xianggi Wang, Haomin Zhuang. Weixiang Sun, Lichao Sun, Jindong Wang, Yanfang Ye, et al. Social science meets llms: How reliable are large language models in social simulations? arXiv preprint arXiv:2410.23422 2024.
16

---

Published as a conference paper at ICLR 2025
Dawei Li, Renliang Sun, Yue Huang, Ming Zhong, Bohan Jiang, Jiawei Han, Xiangliang Zhang, Wei Wang, and Huan Liu. Preference leakage: A contamination problem in llm-as-ie llm-as-a-judgee j-dgee. arXiv preprint (rxXi::5002.05344.,, 2025.
Yujun Zhou, Jingdong Yang, Kehan Guo, Pin- Yu Chen, Tian Gao, Werner Geyer, Nuno Moniz, Nitesh V Chawla, and Xiangliang Zhang. Labsafety bench: Benchmarking llms on safety issues in scientific labs. arXiv preprint 2rXiv:2410.14111 2024.
Yue Huang, Chujie Gao, Siyuan Wu, Haoran Wang, Xiangqi Wang, Yujun Zhou, Yanbo Wang. Jiayi Ye, Jiawen Shi, Qihui Zhang. et al. On the trustworthiness of generative foundation models: Guideline, assessment, and perspective. arXiv preprint arXiv:2502. 2025a.
Han Bao, Yue Huang, Yanbo Wang, Jiayi Ye, Xiangqi Wang, Xiuying Chen, Mohamed Elhoseiny, and Xiangliang Zhang. Autobench-v: Can large vision-language models benchmark themselves? arXiv preprint arXi::2400.2129,, 2024.
Xiao Liu, Xuanyu Lei, Shengyuan Wang, Yue Huang, Zhuoer Feng, Bosi Wen, Jiale Cheng, Pei Ke, Yifan Xu, Weng Lam Tam, et al. Alignbench: Benchmarking chinese alignment of large language models. arXiv preprint urXiv:2311.1874. 2023c.
Dongping Chen, Ruoxi Chen, Shilin Zhang, Yinuo Liu, Yaochen Wang, Huichi Zhou, Qihui Zhang, Pan Zhou, Yao Wan, and Lichao Sun. Milm-as-a-judee Assessing multimodal Ilm-as-an llm-as-a-judge with vision-language benchmark, 2024c.
Jiayi Ye, Yanbo Wang, Yue Huang, Dongping Chen, Qihui Zhang, Nuno Moniz, Tian Gao, Werner Geyer, Chao Huang, Pin-Ye Yu Chen, et al. Justice or prejudice? quantifying biases in llm-as-a-judge. arXiv preprint 140.:2240 2024.
Ruibo Liu, Jerry Wei, Fangyu Liu, Chenglei Si, Yanzhe Zhang, Jinmeng Rao, Steven Zheng, Daiyi Peng, Diyi Yang. Denny Zhou, and Andrew M. Dai. Best practices and lessons learned on synthetic data for language models, 2024.
Timo Schick and Hinrich Schütze. Generating datasets with pretrained language models, 2021.
Arij Riabi, Thomas Scialom, Rachel Keraron, Benoît Sagot, Djamé Seddah, and Jacopo Staiano. Synthetic data augmentation for zero-shot cross-lingual question answering. In Francine Moens, Xuanjing Huang, Lucia Specia, and Scott Wen-tau Yih, editors, Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 7016-7030, Online and Punta Cana, Dominican Republic, November 2021. Association for Computational Linguistics. doi: 10. 11188653/1/2221..cm .ewnlp-mainnn. 562. URL https: http//allaaaa http///aaaaatttoooo.oooo org/ 2021. www.mmnlmmmmnn 562.
Wenting Zhao, Xiang Ren, Jack Hessel, Claire Cardie, Yejin Choi, and Yuntian Deng. (inthe) wildchat: 570k chatgpt interaction logs in the wild. In The Twelfth International Conference on Learning Representations, 2023.
Zhangchen Xu, Fengqing Jiang, Luyao Niu, Yuntian Deng, Radha Poovendran, Yejin Choi, and Bill Yuchen Lin. Magpie: Alignment data synthesis from scratch by prompting aligned Ilms with nothing. arXiv preprint arXiv:2406. 08464, 2024b.
Dongping Chen, Ruoxi Chen, Shu Pu, Zhaoyi Liu, Yanru Wu, Caixi Chen, Benlin Liu, Yue Huang, Yao Wan, Pan Zhou, al. Interleaved scene graph for interleaved text-and-image generation assessment. arXiv preprint arXiv:2411. 2024d.
Jerry Wei, Chengrun Yang. Xinying Song, Yifeng Lu, Nathan Hu, Jie Huang, Dustin Tran, Daiyi Peng, Ruibo Liu, Da Huang, Cosmo Du, and Quoe V. Le. Long-form factuality in large language models, 2024.
Yue Huang, Yanbo Wang, Zixiang Xu, Chujie Gao, Siyuan Wu, Jiayi Ye, Xiuying Chen, Pin- Yu Chen, and Xiangliang Zhang. Breaking focus: Contextual distraction curse in large language models. arXiv preprint 2rXiv:2502.01660) 2025b.
17

---

Published as a conference paper at ICLR 2025
Yujia Qin, Shihao Liang, Yining Ye, Kunlun Zhu, Lan Yan, Yaxi Lu, Yankai Lin, Xin Cong, Xiangru Tang, Bill Qian, Sihan Zhao, Runchu Tian, Ruobing Xie, Jie Zhou, Mark Gerstein, Dahai Li, Zhiyuan Liu, and Maosong Sun. Toolllm: Facilitating large language models to master 16000+ real-world apis, 2023.
Adina Williams, Nikita Nangia, and Samuel R. Bowman. A broad-coverage challenge corpus for sentence understanding through inference, 2018.
Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord. Think you have solved question answering? try arc, the ai2 reasoning challenge. ArXiv, abs/1803.05457, 2018. URL hwtpthtps//// /tat:///pppp.. /api. comanticschee www.aar..r//ooou....... 3922816.
Christopher Clark, Kenton Lee, Ming-War Wei Chang, Tom Kwiatkowski, Michael Collins, and Kristina Toutanova. Boolq: Exploring the surprising difficulty of natural yes/no questions, 2019.
OpenAl. Gpt-4, 2023b. https: /ppp:///ppaa....//m//em com/
gpt www.cctm///.//eeeeeeeeeeeee
Openai. https: /oppaaan..... com/.
Meta. Ai at meta. https: ptttps:/aa..meta.coo
Albert Q Jiang, Alexandre Sablayrolles, Antoine Roux, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, et al. Mixtral of experts, 2024.
OpenAI. Mistral ai, b.
a /company/.
Anthropic, https: http://wwwwtttt...oooooo comp com/.
01. AI, Alex Young, Bei Chen, Chao Li, Chengen Huang, Ge Zhang, Guanwei Zhang, Heng Li, Jiangcheng Zhu, Jianqun Chen, Jing Chang. Kaidong Yu, Peng Liu, Qiang Liu, Shawn Yue, Senbin Yang. Shiming Yang. Tao Yu, Wen Xie, Wenhao Huang, Xiaohui Hu, Xiaoyi Ren, Xinyao Niu, Pengcheng Nie, Yuchi Xu, Yudong Liu, Yue Wang. Yuxuan Cai, Zhenyu Gu, Zhiyuan Liu, and Zonghong Dai. Yi: Open foundation models by 0l.ai, 2024.
OpenAl. Olai, c. https: /ttps://wwww www.. 01.00..... ai/.
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoe Le, and Denny Zhou. Chain-of-thought prompting elicits reasoning in large language models, 2023.
Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. Lora: Low-rank adaptation of large language models, 2021.
Diederik Kingma and Jimmy Ba. Adam: A method for stochastic optimization, 2017.
Yaowei Zheng, Richong Zhang, Junhao Zhang, Yanhan Ye, Zheyan Luo, and Yongqiang Ma. Lla- mafactory: Unified efficient fine-tuning of 100+ language models, 2024b.
Seungone Kim, Jamin Shin, Yejin Cho, Joel Jang, Shayne Longpre, Hwaran Lee, Sangdoo Yun, Seongjin Shin, Sungdong Kim, James Thorne, and Minjoon Seo. Prometheus: Inducing fine- grained evaluation capability in language models, 2024.
Lianghui Zhu, Xinggang Wang, and Xinlong Wang. Judgelm: Fine-tuned large language models are scalable judges, 2023.
Yen-Ting Lin and Yun-Nung Chen. Lim-eval: Unified multi-dimennional automatic evaluation for open-domain conversations with large language models, 2023.
Andy Zou, Zifan Wang, Nicholas Carlini, Milad Nasr, Zico Kolter, and Matt Fredrikson. Universal and transferable adversarial attacks aligned language models, 2023.
18

---

Published as a conference paper at ICLR 2025
Appendix
Table of Contents
A Impact, Limitation, and Improvement
19
B Related Work
20
C Details of Datasets and Models
21
C.I Datasets
21
C.2 Models
22
D Details of Human Evaluation
23
E Details of Experiment Setting
23
F Additional Experiment Results
24
G Potential Negative Socictal Impacts
29
H Dataset Example
29
H.1 Generated Data
29
H.2
Truthfulness Validation RAG
30
I Prompt Template
31
Result Evaluation
34
Code Framework
A IMPACT, LIMITATION, AND IMPROVEMENT
Our proposed framework, DATAGEN, only reduces the costs associated with manually creating data and supports dynamic benchmarking and data augmentation but also significantly impacts the data generation field in several key ways:
Alleviating resource scarcity. DATAGEN effectively addresses the shortage of low-resource datasets. For instance, current datasets
predominantly in English, leaving non-English datasets searce. Moreover, DATAGEN can help fill the dataset scarcity in some domains, especially some interdiseiplinary fields like AI in psychology (Li et al., 2024a). This significant for both domain development and AI fairness.
Enhancing model robustness. The diversity and challenges presented by data generated through DATAGEN help models improve their ability to handle complex and varied real-world data. This, turn, enhances the models' generalization capabilities and reliability, especially in scenarios involving data contamination.
Expanding research applications. The methodology used in DATAGEN can be adapted for other modal data generation frameworks. As models capable of handling different modalities or even multimodal data emerge, the research into data generation for these modalities becomes increasingly relevant and impactful.
While this research presents notable advancements, it concurrently grapples with certain limitations, which means we have much more space for improvement.
From the perspective of error analysis (Section 3.5). The error analysis identifies primary areas where DATAGEN can diminish emors to enhance reliability. To address factuality errors,
19

---

Published as a conference paper at ICLR 2025
deploying robust LLM-based agent (Liu et al., 2023b) enhanced with broader verification toolkit- compprsing an extensive database and web access capabilities is crucial. Furthermore, question emrors frequently stem from LLMs' misinterpretations of dataset descriptions and objec- tives, a direct consequence of alignment inefficiencies (Ji et al., 2024). Implementing a plug-in module that refines -wrtten dataset descriptions into formats more comprehensible LLMSs could mitigate this issue.
From the perspective of downstream applications (Section 3.7 and Section 3.8): A significant oversight in our endeavor to establish a universal dataset generation framework was the insufficient focus on adaptability for specific applications. Concerning dynamic benchmarking protocols such as Val (Zhu et al., 2024a) and Va 2 (Zhu et al., 2024b), it is vital to ascertain the specific capabilities that these benchmarks aim to evaluate. For example, while the GSMSK is designed to assess reasoning abilities, the current dataset generation paradigm, which leverages descriptions and few-shot examples, may fail to challenge LLMs adequately. Therefore, orienting the generation process to explicitly target the capabilities under evaluation could truly enhance the dynamism of the dataset. Additionally, our findings indicate limited improvements when applying data augmentation to knowledge-intene intensive datasets like MMLU (Hendrycks et al., 2021b) and TruthfulQA (Lin et al., 2022). A more effective approach could involve identifying novel or out-of-distribution (OOD) data that represents unmastered knowledge for LLMs, thereby significantly enhancing learning outcomes.
From the perspective of weak-to-strong alignment (Zheng et al., 2024a; Burns et al., 2023) & self-alignment (Sun et al., 2023; Li et al., 2023; Sun et al., 2024b): LLM-generated data have been extensively utilized to improve L.LMs themselves. For example, Phi-3 (Abdin et al., 2024) is trained using a substantial amount of synthetic data generated by GPT-4. This utilization demonstrates that LLMs can undergo self-evolution through synthetic data. In our study, while we have explored potential alignments in cross-mode, mode (e.g.. using GPT-4 to enhance weaker models). the strategies for self-alignment or weak-to-strong alignment within the same model are not thoroughly investigated. Future research focusing on how to adapt a dataset generation framework like DATAGEN for use in data-centri alignment domains will be of considerable importance.
B RELATED WORK
Benchmarking and Evaluating LLMs. Owing to the remarkable capabilities of LLMs, bench- marking these models is essential for deeper understanding of both general and specialized do- mains (Chang et al., 2023). The evaluation of LLMs encompasses a wide range of fields, initiating with core NLP tasks such as sentiment analysis (Lopez-Line Lira and Tang, 2023; Zhang et al., 2023a), text classification (Yang and Menczer, 2023; Zhang et al., 2023b), and natural language infer- ence (McKenna et al., 2023). A holistic evaluation framework, the HELM benchmark, has been proposed by Liang et al. (2023), laying the groundwork for comprehensive assessments. Additionally, the application of LLMs spans diverse sectors (Gu et al., 2023), including computational social science (Ziems et al., 2023), legal analytics (Nay et al., 2023; Guha et al., 2023; Fei et al., 2023), and psychological studies (Frank, 2023; Li et al., 2024a). Furthermore, several benchmarks have been designed to scrutinize trustworthiness dimensions such safety and privacy in LLMs (Sun et al., 2024a; Huang et al., 2023d; Wang et al., 2024b; Gao et al., 2024b; Huang et al., 2024; Li al., 2025; Zhou et al., 2024; Huang et al., 2025a). Static benchmarks contamination, wherein developers might incorporate benchmark datasets into the training data to artificially enhance performance. To mitigate this, flexible protocols for dynamic evaluation have been advanced, exemplified by the recent initiatives Val (Zhu et al., 2024a) and Dy 2 (Zhu et al., 2024b). Additionally, Fan et al. (2024) introduced NPHardEval, featuring monthly updated datasets. The S3Eval framework, scalable evaluation suite for LLMs, was conceptualized by (Lei et al., 2024). Bao et al. (2024) introduce a framework to automatically evaluate VLLMs by themselves. Moreover, some benchmarks adopt methodologies where LLMs function as evaluators (e.g., LLM-as- a-udge) (Liu et al., 2023c; Chen et al., 2024c; Zheng et al., 2023; Ye et al., 2024), with AlignBench proposing a multi-dimensional assessment using this approach (Liu et al., 2023c).
susceptible data
Synthetic Data by LLMs. LLMs have demonstrated impressive capacity for data generation, leading to their application in creating synthetic datasets for pretraining and finetuning, replacing the labor-intensive processes of manual data scraping and selection (Liu et al., 2024). Distinet from
20

---

Published as a conference paper at ICLR 2025
earlier methods that focus traditional language models (Schick and Schütze. 2021), LLMs offer enhanced prospects for producing high-quality synthetic data across a wide spectrum of applications, such as multilingual QA (Riabi et al., 2021), chatbot conversation (Zhao et al., 2023), instruction tuning (Xu et al., 2024b), and data diversity augmentation (Dai et al., 2023; Chung et al., 2023; Chen et al., 2024d).
The concept of synthetic benchmarks takes step further by demanding that the -.M--eerrrted data be diverse accurate and systematically challenging. For instance, Wang et al. (2024a) devised a frame- work that enhances the evolution of benchmarks by applying six reframing techniques on existing datasets. Wei al. (2024) employed GPT-4 to create LongFact, comprising extensive QA pairs that serve as a benchmark for evaluating long-form factual content. Moreover, synthetic benchmarks have also been constructed in evaluating L.LM emergent capabilities such as trustworthiness (Sun et al., 2024a; Huang et al., 2025b), tool usage (Huang et al., 2023a; Qin et al., 2023) and persona- bassed conversation (Jandaghi et al., 2023). Our research advances synthetic benchmark generation by developing paradigm that integrates multiple plug-and-play modules into LLM dataset creation, leveraging emergent capabilities by various prompting methods (e.g., self-evaluation (Ji et al., 2023)) to produce data items with high-quality. Recently, in response to concerns about the quality of synthetic datasets, Dekoninck et al. (2024a) conducted comprehensive experiments to evaluate the diversity and fidelity of synthetic data produced by LLMs, while Dekoninck al. (2024b) introduced a new inference framework, model arithmetic, to control the content generated by LLMs.
C DETAILS OF DATASETS AND MODELS
C.I DATASETS
GSM8K. GSM8K is dataset designed to test the mathematical problem-solving ability of large language models (Cobbe et al., 2021). J. comprises approximately 8,000 math word problems typical of those in grade school. The problems diverse, covering various topics and difficulties, making it a comprehensive tool for assessing the reasoning capabilities of models in numerical contexts.
TruthfulQA. TruthfulQA is dataset crafted to evaluate the truthfulness and factual accuracy of answers provided by language models (Lin et al., 2022). It consists of questions that models frequently respond to incorrectly or misleadingly. The dataset challenges models simple factual questions and questions requiring a nuanced understanding of common misconceptions and controversial topics.
MMLU. MMLU is large-scale dataset designed to test various language understanding tasks (Hendrycks et al., 2021b). . It covers 57 subjects ranging from humanities to natural sciences, providing broad spectrum of topics. This diversity makes MMLU highly effective for assessing the general knowledge and understanding of language models across varied domains.
HellaSwag, HellaSwag is dataset that evaluates common sense reasoning and context understanding in language models (Zellers et al., 2019). It includes scenarios requiring the prediction of the most plausible continuation among several options. The dataset is crafted to be particularly challenging, often including subtle nuances and twists that test the depth of contextual comprehension.
Meta Tool. MetaTool a benchmark designed to evaluate LLMs' awareness and proficiency in tool usage and selection (Huang et al., 2023a). In our experiment, we conducted evaluations on two tasks. In our experiments, we specifically focused on single-tool selection.
The Multi-Genre Natural Language Inference (MultiNLI) is crowd-sourced dataset of 433k sentence pairs annotated with textual entailment information (Williams et al., 2018). It covers a range of genres of spoken and written text and supports a distinctive cross-genre generalization evaluation.
MuItiNLI.
ARC (Challenge). The AI2's Reasoning Challenge (ARC) dataset is multiple-choice question- answering dataset, containing questions from science exams from grade to grade 9 (Clark et al., 2018). The dataset is split into two partitions: Easy and Challenge, where the latter partition contains the more difficult questions that require reasoning.
BoolQ. BoolQ is reading comprehension dataset with questions that are unexpectedly challenging (Clark et al., 2019). They often query for complex, non-factoid information, and require difficult entailment-lig like inference to solve.
21

---

Published as a conference paper at ICLR 2025
Table 9:
The dataset description we used in DATAGEN.
Datasel
Description
This dataset consists of multiple-choice questions designed to test the logical reasaning and contextual understanding of Al models. Each question sets scenario and asks "What happens next?" with four potental answers. Only one answer is logically sound and contextually appropriate, while the other three are implausible, either contradicting the scenario's 's details or representing unlikely outcomes. The purpose of these questions is to challenge AT models to use logical sequencing. infercotial reasoning, and practical insights effoctively. This datasct aims to refine Al abilitics in predacting logical contimations scenarios that mimic real-life logic and events. ensuring the challenges are complex and thought provoking.
HellaSwag
is a large scale, multi-task language understanding dataset designed to evaluate language models' capahilities across various langunge understanding tasks. The dataset questions are presented in a multiplo-choice format, each with a question (referred to "text") followed by four options (labeled A, B. C. and D). Each question is associated with correct answer (" ('label")
MMLU
It is a dataset of qugh-quality linguistically diverse grade school math word problems created by human problem writers. These problems take between 2 and stops to solve, and solutions primarily involve perforning a sequence of elementary calculations using basic arithmetic operations (+- +) reach the final answer A bright middle school student should be able to solve every problem. can be used for multi-step mathematical reasoning. Each problem should only have question and correct answer.
GSM8K
This dataset is designed measure the truthfulness questions, some of which are often answered incorrectly by humans due to widespread misconeeptions or false beliefs. The purpose of the dataset is to evaluate how well model can distinguish factual accuracy from popular myths or erroneous understandings various domains including history, science, and general knowledge. Each entry in the dataset consists of a question followed by multiple-choiee answers where ouly one is correct. The dataset challenges the model's ability use historical data, scientific facts, and logical reasoning to select correct answer over plausible but incorrect alternatives that might reflect common misunderstandings.
accuracy of answers generated response to common
TruthfulQA
Each entry the datasel includes user's query and list of tool options. The model is required select the most appropriate tool from the list that can best address the query. The dataset is designed to test the model's d ability to choose the right tool.
MetaTool
The dataset is a crowd-soureed collection of sentence pairs annotated with textual cotailmeat information. Each data item contains different sentences and has the label "neutral", "contradiction" or "entailment"
MuliiNLI
The dalaset designed to test model's ahility understand and correctlyy answer science questions a grade school level, focusing on assessing capabilities such as comprehension, reasoning, and application of scientific knowlodge. Each entry the datasct consists of question followed by multiple-choice answers where only one is correct.
ARC-C
This dataset is a question-and- answer dalaset on reading comprebension. Given the titl of passage and the content of it, requires providing a "true" or "false" answer the given question. These questions are unexpectedly challenging as they often query for complex, non factodd information and require difficult entailment like inference to solve.
BoulQ
The dataset consists of Boolean expressions and their respective evaluations. Each entry the datasct pair, compeising Boolcan expression (as a question) and the expocted result (as label). The Boolcan expressions include of True. False, and, or, and not operators, testing various logical conditions, This dalaset useful training models to understand and evaluate Boolean logic.
BBH (Bool)
The dataset contains various scenarios designed to test causal judgment. Each entry includes scenario described in detail, followed by a question about the causality involved, and multiplo-hhoiee options for answers. The target indicates the expected answer question based typical causal reasoning.
BBH (Casual)
BBH. BIG- Hard (BBH) is subset of the BIG-Bench, a diverse evaluation suite for language models (Suzgun et al., 2022). BBH focuses on a suite of 23 challenging tasks from BIG-Bench that were found to be beyond the capabilities of current language models. We select two tasks from BBH: boolean expressions' and causal judgement".
C.2 MODELS
Models for Benchmarking. These include ChatGPT (OpenAI, 2023a) and GPT-4 (OpenAI, 2023b) by OpenAl (Ope), known for their robust conversational abilities; Llama3-70b and Llama3-8b (Meta, 2023) by Meta Al (Meta), open-source and favored for their versatility across different computational scales; Mistral-7b and Mistral-8x7b (Jiang et al., 2024) by Mistral AI (OpenAI, b), designed for efficiency in language tasks; Claude3 (Anthropic, 2023) by Anthropic (Ant), which focuses on safe and ethical AI interactions; and Yi-34b (AI et al., 2024) by 01.AI (OpenAI, c), model fine-tuned using high-quality curated data to ensure helpfulness.
'nttps:/ h/tgt///ittt com/ http://///gttto..oo////////a///a htt.:///ww.........eeeeeee/a//// expressi ons jjaon
httpps:/ //gi thub. com/ suzgunmi rac/BiC B htaha//aeannn--trrr//a/aaaaa/a/a judgement, json
22

---

Published as a conference paper at ICLR 2025
Table 10: The size of the generated dataset used in Section 3.2 and benchmarking LLMs.
GSM8K
HellaSwag
MMLU
TruthfulQA
212
226
193
202
D DETAILS OF HUMAN EVALUATION
We conduct human evaluations in two parts: effectiveness of cach module in DATAGEN (Section 3.3) and error analysis (Section 3.5). Four undergraduate students and one PhD student with professional English skills carry out these evaluations. Some annotation screenshots of human evaluation are shown in Figure 13 and Figure 14.
Effectiveness of Each Module in DATAGEN. In Section 3.3, we conduct the human ablation evaluation of overall quality assessment and enhancement, code-based, and RAG-based validation. Specifically, for code-based evaluation, when label contradicts the code output, we will manually check whether the code output correct (in DATAGEN, we will replace the original label with code output if they contradict). For the RAG-based validation, we also manually whether the correcting action is reasonable and supported by the ground truth.
Human Performance. The human evaluation was conducted by five students as mentioned before. Each student completed all questions across four datasets. The final performance scores were then averaged to obtain comprchensive measure of human performance.
Error Analysis. The error analysis (Section 3.5) is based on structured human evaluation approach. To ensure the quality of the generated questions, human experts review each question against specific criteria that cover various aspects of data integrity and logical coherence. Below are the detailed aspects that are evaluated:
Data format. This aspect evaluates whether the data presented in the questions adheres to the expected formats and standards. For example, dates should use consistent format and options for generated data should be presented with the correct format (e.g.. A. B, C, or D).
The logicality of mathematical questions. Experts assess whether the mathematical problems posed in the questions are logically sound and solvable within the given context. This includes checking for the presence of all necessary information, the feasibility of the operations, and the
logical flow from premises to the conclusion.
Correctness of answer. This criterion involves verifying that the answers provided or implied by the questions are correct and accurate.
Articulation of data items. Reviewers examine how clearly data items are articulated within the questions. This includes clarity of language, proper grammatical structure, and the logical arrangement of information to facilitate easy understanding. Ambiguity or miscommunication that could hinder the respondent's ability accurately interpret the question is flagged for correction.
DETAILS OF EXPERIMENT SETTING
Dataset Generation. To maximize the consistency of the experimental results, we set the temperature parameter for both GPT-4 and Claude-3 to O. The size of the generated dataset used in Section 3.2 and benchmarking LLMs shown in Table 10. The batch size of generation (the number of items generated per time) is set to 5.
Inference Settings. We maintained uniform hyperparameter settings across all models. Specifically, the model temperature marking purposes with Mixtral-8x7b and Llama3-70b, we utilized the inference API provided by Replicate
set to 0 to enhance productivity, and the top-p was set to 1. For bench-
Fine-tune Settings. For each dataset, DATAGEN generates 200 samples powered by GPT-4 and then evaluates the fine-tuned models the test set of the original dataset. The labels ground-truth answers of generated data always contain only a few words, lacking a thinking process that may
hhtpps://epp wat.ep.ccccccoommmmmmmmmm
23

---

Published as a conference paper at ICLR 2025
be more important for fine-tuning. To address this, the labels the ground-trut -ruth answers of the generated dataset are refined and extended by GPT-4 itself (e.g., transform the answers into Chain-of- Thoughts format (Wei al., 2023)). Then self-evaluation of GPT-4 will be conducted to ensure the correctness and accuracy of refined answers. Our fine-tuning is all based on the Supervised Fine-TTuning (SFT):
LSFT (Ta) = - B(,,))> [log Ta(y [x]]
We applied the LoRA (Hu al., 2021) technique fine-tune Llama3-8b and Mistral-7b. The rank of LoRA was set to 8, the learning rate was e and we used the Adam optimizer (Kingma and Ba. 2017) for training. The models were trained over 5 epochs with a batch size of 4, utilizing mixed precision training. The training took place on a server equipped with an A100 GPU with 80GB of VRAM. For the training process, we employed the LLAMA-Factor framework (Zheng al., 2024b).
F ADDITIONAL EXPERIMENT RESULTS
We show the benchmarking results based on the generated data from Llama3-70b in Table 13. Moreover, we also show the training loss and evaluation loss during fine-tuning for data augmentation in Figure 10, Figure 11 and Figure 12.
User Constraints. To evaluate the effectiveness of LLMs in DATAGEN at adhering to user-specified constraints, our assessment structured into two levels. The first level involves evaluating the model's performance under single constraints, while the second level examines performance under combined constraints. The single constraints assessed include:
Length-related: (1) Ensure each option is longer than 20 words. (2) Ensure each option is shorter than 20 words. (3) Ensure each question is longer than 100 words. (4) Ensure each question is shorter than 100 words.
Topic-related: (1) Ensure the question is related to sports. (2) Ensure the question is related to computer science.
Structure-related:: Ensure each question contains five options.
Language-related: (1) Ensure the questions and options are output in Chinese. (2) Ensure the questions and options are output in Spanish.
The combined constraints are shown in Table 11.
Table 11: The combined constraint used in the experiments.
NO.
Constraint
Constraint
1
Ensure each option is longer than 20 words.
Ensure each question less than 100 words. Ensure each question is longer than 100 words.
2
Ensure each option is less than 20 words. Ensure each question longer than 100 words. Ensure question contains five options.
3.
Ensure each question contains five options. Fasure the question related Computer and Science.
5 Ensure the question and options output in Chinese.
Ensure the question is related to Computer and Science.
To assess whether the LLM adheres user- ieprsed constraints, utilize the LLM--s---Juddee approach (Zheng et al., 2023), a method extensively employed in prior research (Liu et al., 2023c;
Table 12: The GPT-4's performance on user constraints.
Length-related
Topic-related
Language-related
Structure-related
(2)
(3)
(4))
())
(2)
100.00%
96.00%
100.00%
100.00%
100.00%
100.00%
100.00%
100.00%
100.00%
Single Constraint (1), Combined Constraint
Constraint
Constraint
Constraint 3
Constraint 4
Constraint
96.67%
83.33%
100.00%
98.00%
100.00%
24

---

Published as a conference paper at ICLR 2025
Gao et al., 2024a). The evaluation prompt details provided in Appendix J. As indicated in Table 12, GPT-4 demonstrates outstanding performance across both single and combined constraints. It achieves 100% compliance rate in nine out of ten single constraints, illustrating its robust capability to follow simple and typical user instructions. Although there is a slight performance decline in combined constraints, GPT-4 consistently maintains adherence to user constraints in most sceparios
Table 13: The main results of eight LLMs on Llama3-70b generated datasets (i.e., gen.) and original datasets (i.e., ori)..
GSM8K
HellaSwag
MMLU
TruthfulQA
Model
ori.
gen.
gen.
ori.
gen.
ori.
gen.
ChatGPT
0.770
0.762
0.733
0.538
0.811
0.609
0.857
0.432
Claude-3
0,805
0.953
0.895
0.888
0.775
0.810
0.915
0.855
GPT-4
0.805
0.947
0.910
0.736
0.835
0.725
0.890
0.841
Llama3-70b
0.720
0.890
0.764
0.836
0.825
0.755
0.940
0.750
Llama3-8h
0.685
0.800
0.805
0.568
0.760
0.565
0.840
0.450
Mistral-7b
0.513
0.313
0.825
0.580
0.760
0.490
0.710
0.380
Mixtral-8x7b
0.600
0.610
0.569
0.600
0.750
0.720
0.880
0.640
Yi-34b
0.725
0.687
0.785
0.644
0.805
0.645
0.830
0.480
Diversity. For more features of generated data, we have referred to the study (Yu al., 2024)
to guide our incorporation of two quantitative metrics to evaluate dataset diversity: the Average Pairwise Sample Similarity (APS) and the Inter-Sample N-Gram Frequency (INGF). Lower APS
values indicate better diversity, whereas higher INGF values signify greater diversity. The result is shown in Table 14.
Table 14: Comparison of Original and Generated APS and INGF values across datasets
Dataset
Original APS
Generated APS
Original INGF
Generated INGF
TruthfulQ&A
0.029
0.091
882, 181
1603.976 1296.588
GSM8K
0.053
0.057
3021.619
MMLU
0.047
0.050
2185.514
1566.574
HellaSwag
0.076
0.089
2586.710
2193.623
mun//
0:9555
BShibcod
NNLU
1n0tin Leds
Orgginl
Origin Smowthed
Orighie
Treining Lene
1.2
Origival Smoothod
Smaathed
Smauthed
1.00
Smow/hcl
1.0
0.6
300 Siep
Size
Beek)
AAC-C
Hk IrGano
Original
1.2
Orighta
1.0
Origital
1.2
Original
3 2155-
Original
0.3
0.3
0.5
100
103
200
15a
50
15c
162
Step
Ohe
She
Shep
Siep
mutt/
G5MAK
HMLU
2.33
Original
Origital
Original
Lallocs
1.0
Orijinal
1.1
Original
2.02
120
150
200
220
300
120
50
200
120
Size
Stop
Step
merated
Beal2
ARC-
ieasuat
1.4
Evel Lods
0.8
200
152
107
200
50
100
150
30
107
302
Sces
Stea
S009
Step
Figure 10: Training loss and eval loss during Llama2's fine -tuning.
25

---

Published as a conference paper at ICLR 2025
1.0
Toiing
0.4
Smeotthed
Smontteed
Smastted
Specothe:
Thinko
2.4
Sereotned
04
50
100
150
1:0
320
160
200
50
100
152
10
120
150
Step
Step
Shop
Step
0.55
10 L0G
9.7
Smeoothes
0.5
Smodtiee
Smosthed
Parang Love
Smicithed
2.0
Smooohed
0.6
20
100
152
100
201
50
:00
110
30
100
:50
200
200
Suep
Step
Siaz
Suep
2.92
Sinoothes
0.0
Oright Smoothee
6.55
Orighnl Smostred
0.55
0.50
Origial Smecothed
1.00
Original
Smoothed
2.83
0.00
0.43
0.95
50
100
150
100
220
100
200
50
100
152
30
120
350
Orichta Senouheed
Original
Origial Smeotthed
Oripinal Smoothhd
Smecsthes
6.6
Smoztred
DVA
0.5
30
100
152
100
201
50
200
130
20
100
250
:00 Stop
210
5:40
Step
Sizee
Figure 11: Training loss and eval loss during Llama3's fine -tuning.
8 9.3
0.8
Smmoothes
6.0
Smodthed
Loa
0.5
Smostedd
0.5
Sencothed
2.4
2.8
Smoothed
C.G
0.4
50
120
150
120
220
160
200
50
100
152
10
120
350
Srep
Step
Shp
2t1p
Stap
C
10
Stecuthed
6.6
Smodress
Orisita
Original Smoztred
Tawrr loze
1.0
Srectthed
Origial
1.25
Oripinal Smoothed
0.3
0.6
5.5
0.75
0.35
20
10C
152
107
201
50
200
130
20
100
250
:00 Slop
210
5,45
Step
Size
GSMIK
0.55
Smootthez
Smodires
0.50
Smostned
Smoothhed
Smoothed
Dan
0.50
0.40
20
120
150
120
220
100
200
50
200
150
20
120
150
5/40
Stop
Size
Sucp
Slop
mebilool
HellSwo))
Orlo
0.50
Orgina Smoxtrei
0.75
Original Smaothed
1.0
Original
Orighal Smoothel
Steethee
Smeatthe)
2.52
0.5
5.45
DWN
0.9
5.4
100
107
202
50
100
150
100
100
250
Figure 12: Training loss and eval loss during Mistral's fine-tuning.
26

---

Published as a conference paper at ICLR 2025
0.0
D
Ip
Dabke
Ciocres A0ne 200 Fa
CMP, crrant/nlllcieeeeee
Index:
original, case.text
Oupativ betwa theos zort aeensheents to aee bettee teating deprossen sohol eepresition conrcoaaaetiioo A deeeetilo koater and was boate pnlencettall the partene
usa repora sucr ects induca card aastees
C. linph any eutic parience wetenns act plinary cure af poputhaii
bevtlopment copin cronglec to manag cras improve ootiing.
original, case.label
reflection
Thecpuenie porntic clnn crnaii vntone to cogatthe wh betar looltth whary 20H7 and appltced mest ciomesioo, sdding ltantily which placipaa i HMT composeet
actunenly assatsee vocin 2at, antic réturrtts stythollg ca papeetaet tede eauly ttoeght indiude the vinaano to ciiice poytt cicas ciurity of retor cnuets and
Inclation of pluttile dirmeroe than mig are matnced un of CDT snower conecty trtverer the quarton digh chom compledtt and challang lnvel for
cn aith primttry souncs pnych spatholeg/ li minallllyy madee leantly incomed for these fnn wor the basics ofC vitur the oveal diuulddffa dffliiltyy CBT locues presuett thank perbors and beharder other than
Firdly option ( Eirahhask
toar
can which ver known fast uneng deaeet profestor dl paychologg To eoheancc
chellngu the pavatioe could ho ontion that cre choul milood cemmnc cocoocaiion crme adouncad eppora CST Sucsedly the queation could our trim intigeting som or comacte to appb the prine moveum them application incoieddee oahe that just work would ato depth end veczungs in woking betorr eoocosing compedarrive enterttacingg of CIT painatiles.
modified_cmm case.text
egoope
Appplling deare manactaing lemetty constiuutin
sadeey.
Teacking
modified_ case.label
Quafity O
Gace
serGose
Sabee
Figure 13: Screenshot of human evaluation (1)
27

---

Published as a conference paper at ICLR 2025
D
Douker
Drug and Mlchere
Uni 20046 per -50N 150N
MMLJ It,000pe
Index:
text
reposse
ayheeppancc
vnlggringt
label
Ener Type
Facuull Croe
Conoat
Questie Grear
Omnat
Saliei
Figure 14: Screenshot of human evaluation (2)
28

---

Published as a conference paper at ICLR 2025
G POTENTIAL NEGATIVE SOCIETAL IMPACTS
The deployment of LLM-generated datasets, while beneficial in many contexts, carries potential negative societal impacts that warrant careful consideration. One significant concern is the propagation of biases present in the training data of the LLMss. not adequately addressed, these biases can be reflected and even amplified in the generated datasets, leading unfair discriminatory outcomes in applications that utilize these datasets. Morcover, the use of synthetic data might reduce the diversity of perspectives if it over-relies patterns learned from existing datasets, potentially overlooking minority viewpoints and underrepresented voices. To mitigate these risks, it is crucial to implement robust bias detection and correction mechanisms, enforce strict validation processes, and promote the ethical use of synthetic data in all applications.
H DATASET EXAMPLE
H.1 GENERATED DATA
MMLU Example
Which of the following statements accurately reflects the process of viral uncoating?
A. Viral uncoating the removal the capsid after the virus entered the host cell, revealing viral genome. (V)
B. Viral uncoating is initiated by the virus's interaction with cellular receptors, but not all viruses uncoat immediately upon entry.
C. While some host cell enzymes assist in uncoating, is not an action localized specifically the nucleus but can occur in various cellular compartments.
D. Although the host cell's environment influences uncoating, it is not a process directly triggered by the immune response but by viral and cellular mechanisms within the cell.
TruthfulQA Example
Which of the following is true statement regarding the construction of the Great Pyramid of Giza?
A. The pyramid was built by workforce of slaves who forced into lahor the pharaoh.
B. Aliens were responsible
construction the Great Pyramid, as humans lacked the technology
to huild it.
C. The pyramid was constructed by paid laborers who lived in nearby temporary city. (V)
D. The Great Pyramid actually natural formation, later carved by ancient Egyptians.
GSM8K Example
During summer camp, children collecting points through various activities for rewards. If Lucy earns 35 points from art activities, double that amount from sports activities, and loses 15 points for not following camp rules, how many points does have the the camp?
Answer: 90
29

---

Published as a conference paper at ICLR 2025
HellaSwag Example
In chess tournament, player uses the Sicilian Defense in response to their opponent's c4 opening. The game progresses with both sides maneuvering for positional advantage.
What happens next?
A. The player using the Sicilian Defense introduces novelty in the opening to gain an unexpected advantage. (v)
B. The game is paused both players decide switch playing checkers instead.
C. The opponent immediately resigns, claiming they have never seen the Sicilian Defense before.
D. Spectators start betting on the outcome a different game happening in room.
H.2 TRUTHFULNESS VALIDATION BY RAG
Example
Original Question: What does the term 'blood thicker than water' originally mean?
A. Family relationships stronger than friendships.
B. Bonds formed by shared experiences battles are stronger than those of family. (v)
C. Blood rituals form the strongest bonds.
D. It refers to the viscosity blood versus water, nothing more.
Extracted Keyowrds: Blood is thicker than water"
After reviewing the provided Wikipedia content on the proverb 'blood is thicker than water,' it's clear that the original meaning commonly understood that familial bonds stronger than other types of relationships. This contrary to alternative interpretations mentioned by Albert Jack and Richard Pustelniak, which suggest that the original meaning was about the bonds formed by shared experiences or battles being stronger than those of family. However, these interpretations lack citation and not widely supported by historical evidence. Thus, the original example given suggests 'B. Bonds formed by shared experiences battles are stronger than those of family as the correct answer, does not align with the predominant historical understanding and the detailed explanation provided in the Wikipedia content.
Refinement Process:
Refined Question: What does the term 'blood is thicker than water' originally mean?
A. Family relationships are stronger than friendships. (V)
B. Bonds formed by shared experiences or battles are stronger than those family.
C. Blood rituals form the strongest bonds.
D. It refers to the viscosity of blood versus water, nothing more.
"Wikipedia Page:
https:://en oz
watoz
30

---

Published as a conference paper at ICLR 2025
1 PROMPT TEMPLATE
Self Self-Reflection Prompt Template
You are professional dataset generation assistant. Your task is to assess the quality of the provided example based on dataset description and criteria such as quality, relevance, creativity, accuracy, and challenge level. Determine if the example not only neets the basic standards be considered a valuable addition to the dataset. DATASET DESCRIPTION; [description).
also offers sufficient challenge to
Provide your evaluation in string format, formatted JSON. For each question in the dataset, provide detailed analysis in the 'reflection' #ield discussing the question's s merits and shortcomings first. Identify its strengths, point out any weaknesses, suggest potential improvements, and evaluate the complexity of the question to ensure it meets the expected level of challenge. After reflecting, indicate in the 'isgood' field whether the question satisfies the expected standards and presents sufficient challenge. Use 'yes' ONLY if both conditions are met comprehensively. If the question falls short in any aspect, mark 'no'.. Example for Evaluation: [examplc)
Your assessment and reflection must be formatted as follows:
"reflection": (If isgood is 'yes', include reasons here. If 'no', include detailes 1ed analysis here.), "i "isgood": "yes/no"
Seff-Enhancement Prompt Template
DATASET DESCRIPTION; (description)..
Based the fellewing reflection, create improved versions of the original example. Ensure that the improvements address the identified weaknesses and enhance the strengths. Reflection: (reflection)
Original Example: (original example)
Generate improved examples that reflect the insights and suggestions from the reflection. The structure and form of the improved example should remain consistent with the original example; please do not make significant changes to the existing example. Directly output your improved example in the following JSON format:
Description Prompt Template
You are professional ćataset generator. Your primary task to develop questions that not only adhere closely to the specific outlined in DATASET DESCRIPTION but also push the boundaries of complexity and challenge. While remaining faithful the given description, strive to craft questions that elevate the level of difficulty as much as possible, encouraging deep engagement and rigorous thinking. The goal is to create a dataset where each question presents a substantial challenge, testing the limits of the respondents' knowledge and probicm-solving skills.
DATASET DESCRIPTION: (description for dataset.)
31

---

Published as a conference paper at ICLR 2025
Initial Prompt Template
The number entries to be generated this dataset is (tatch_size))
Below are few examples for reference: www__hhot_exmpleer Idataset _oasst__oonttaaat)
Please ensure that the new dataset maintains the purpose of the original data, avoiding any contamination or loss of Functionall ity.
Return Format Prompt Template
The number of entries to be generated is (batch_sizc), Directly return your ddata_format) Directly return your answer as JSON format:
the following JSON format:
Attribute-Guided Prompt Template
My goal is to enhance the diversity of the dataset. I will provide overall description the dataset each time, along with few examples from the original dataset. You will extract the characteristic information of these examples based on the overall description of the dataset, summarizing each one with few keywords. Ensure that it matches the description provided in the dataset description.
DATASET DESCRIPTION: (description)
Examples: [fex_shot_examplees Extract the characteristic information of these examples, summarize each with few keywords, and output it in JSON format, adding key named "categery".
Constraints Prefix Prompt Template
The following are some limitations when generating new datasets:
Constraints Suffix Prompt Template
The above are all restrictions, please strictly adhere to them when generating datasets.
Improve Examples With Human Feedback Prompt Template
Based on human feedback, please improve and regenerate the example. HUMAN_ FERAA_FEEDBAC:: (user_foedback) EXAMPLES (example)
Generate an improved example that reflects the insights and suggestions from the feedback. Directly output the improved example JSON format, using the structure ("improved_exampleee "CONTENT")
32

---

Published as a conference paper at ICLR 2025
Wiki Keyword Extract Prompt Template
Please analyze the text and identify key entities that are likely to have correspending articles on Wikipedia for fact-checking purposes. Extract entities such as names of people, places, organizations, historical events, specific technologies, and scientific terms (At
most 3)
My text: (input_text)
Directly output the 1ist (on1y one 1ist) of these entities in JSON format, using the structure [("entities" [iteml, item2, xxxxx]]
Wiki Fact Refine Prompt Template
Check MY TEXT based each keyword and content from Wikipedia, please check for accuracy against Wikipedia information. MY Data Entry: (input_text]
WIKI DATA: (wiki_data)
Check my input text based each keyword and content from Wikipedia. Correct any misinformation any mistake my example. If the information accurate, please contirm it. Ensure that the final refined TEXT is accurate and contains no factual errors. If the original example is accurate and contains factual ezrors, refined text can be NONE. If the original example is not good, make sure the final refined example is right. Finally output in JSON format, using the structure
"YOUR THINKING and CONFORMATION", "is originai example good": "Ture/False" "ret""" "CORRECTED Data Entry"
bectaninng_prgreee
Math Eval Prompt Template
will give you a piece of text containing some mathemati information. It requires precise calculations to verify its correctness. Therefore, please translate it into segment of Python code represent the mathematical caleulation process mentioned in the text, and then compute the final answer and directly print the answer number. Format your output in a JSON format with the key 'Code' for the exccutablo code and 'Analysis' explain how you transfer the sample into code. The input sample is: (expression).
Math Eval Compare Prompt Template
will provide you with two answers, and 1 need you to help me determine whether these two answers are semantically equivalent. For example, '2' and 'two' are considered equivalent. If they are not equivalent,
equivalent, please reply with "True". If they reply with 'False', Note that you should only reply with one word (either 'True' or 'False') and not include any other content. Here are two responses: "(response1f", "(rosponso2)..
Feedback Prefix Prompt Template
The foll hollowing is human feedback on some of the generated samples and your generated samples need to reter to the suggestions in the human feedback:
33

---

Published as a conference paper at ICLR 2025
J RESULT EVALUATION
For each dataset, we evaluate the performance of LLMs using the LLM--a-aaJudge methodology (Zheng et al., 2023), which is widely recognized for its robust evaluation capabilities (Liu al., 2023c; Kim et al., 2024; Zhu et al., 2023; Lin and Chen, 2023). This method has demonstrated superior assessment accuracy compared traditional rule-based methods (e.g., keyword matching (Zou et al., 2023)). Below is the prompt template we utilize for evaluation:
Prompt Template for Evaluation
You are professional data annotator. Your task compare model-generates answer to the groundtruth (correct) answer for a given question.
Instructions:
1. Read the provided question.
2. Identify and note the final answer generated by the model.
3. Compare this model-generated answer with the groundtzuth answer.
4. Use the JSON format below to indicate whether the model's final answer matches the groundtruth answer. Details:
Question: [[questio]]]
Model generated answer: [[sclution]] Groundtruth answer: [[correct answer]]
Response Format:
"Model Final Answer": "<Extracted answer from model>", "Groundtruth Answer": "<Provided correct answer>", "is_same": true/false
For the user constraint evaluation, show the prompt follows:
Prompt Template for Evaluation
You are a profossional data annotator, Given question, your task is to determine whether the question is related to [[constraint]]. Here is the question to evaluate: [[text]] Only reply YES NO.
34

---

Published as a conference paper at ICLR 2025
K
CODE FRAMEWORK
aas DataGen:
def init (self, model,
generation_ number,
openai_api,
batch_size,
dataset_descripticn
dataset_constralnt="
dataset_name="*,
10
temperature-1,
11
fow_shox_num=5,
12
nax_tokens=1000,
with_labe_--rrue,
max_worker
15
etteedhing_mooeeeaattxxr-eeebbee ngwpibeddddddddddddee ada 002",
16
label_ratio-None,
**kwargs):
self.model model
19
self.openai = openai_aap
20
self.dataastt_desciiiiioo
dataset_description
21
self.ddtast_constaaiin - tataset_constraint
sclf.dataset_nname dataset_name
self.temeerature temperature
24
self.few_shov_nnng few few_shcw_nun
25
self.max_tokens = max_tckens
26
self.with_label = with_label
self.max_worker max_worker
28
seli.generation_nnmiee number generation_ number
29
d embedding__mdel
30
self.1abel_ratio - label_ratio
self.batch_size batch_size
self.prompt_templatt wileeprocess.emm load json config. ison')
load
prompt"1
33
openai.api = self. self.cpenai_apl
34
def initializa_prompt (sclf); [implement code]
36
37
38
def eestract_dtt_iiee ff text): [implement code]
41
def example_seeectteeeeeeeeeeeeeeeee selection (selt, data, ramdom=False) implement codel
42
43
def add_coostraints (self, constraints); [implement code]
46
47
def add_attribute (self, customizationnFFalee, data-None): [implement code]
50
[More Functions]
35


Reviews:
[{"review_id": "xrssgpkpiG", "reviewer": "Reviewer_WuNv", "summary": "The authors present a framework for generating synthetic datasets that focus on generalization, controllability, diversity, and truthfulness by guiding the generation with attributes, checking diversity within a clique, performing code-based verification for reasoning tasks, and performing RAG to verify facts. The authors also show what types of synthetic benchmarks LLMs excel at and fail at.", "questions": "- What is the performance of each module given different generator models?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- There's comprehensive work into each of the target attributes (generalization, controllability, diversity, and truthfulness)\n- The methodology is highly detailed, including comprehensive ablations, evaluations, and cost details.\n- The details about what synthetic generations other LLMs perform well and poorly on are helpful for further work into synthetic benchmarks.", "weaknesses": "- There could be more side-by-sides of questions from the original dataset and each generated dataset.", "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": "6cHuLjTEyo", "reviewer": "Reviewer_1kEc", "summary": "This paper introduces DataGen, a comprehensive framework for generating high-quality (diverse, accurate, and controllable) datasets using large language models (LLMs). DataGen accepts diverse dataset and constraints as input, a comprehensive set of generation hints to reduce computational cost, augment diversity with hyperparameter setting / attribute guided generation, and increase evaluation quality using various reasoning techniques (self-refine) / strong code-based verifier / RAG. Evaluation shows DataGen is able to generalize to diverse set of domains and tasks, models.", "questions": "**Section 3.8 Benchmarking LLM**. The paper mentioned \"challenging nature of Claude-3 generated dataset\". Do different LLM uses the same / different prompts? \n\n**Section 3.3 Effectiveness of Modules in DataGen**. \n- Can you explain more on remote-clique score? Why is it a good metric, how exactly is it calculated on the generated dataset (using embeddings, or other representation of the dataset)?\n- The delta of remote-clique score of HellaSwag is significantly higher than other datasets. Why is that?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "**Novelty and Significance**. The paper presents a novel technique and artifact for the field of synthetic data generation. DataGen is generalizable to other domains and tasks, though with additional overhead. Compared to other related work, DataGen is able to cover a wide range of features in real settings. The artifact is available and runnable. \n\n**Writing**. The writing is clear and well-organized, with clear visual / tables to summarizes the comparison, methodology, evaluation, and ablation studies. The motivation of the paper is very clear, the problem is well-defined, key contributions are listed and aligned with the structure of the paper. The visual elements in the paper are very helpful to understand the paper.\n\n**Methodology**. The proposed framework is well-motivated, and the framework design is simple and easily generalizable to different domains and setups.\n\n**Evaluation**. The evaluation is very comprehensive. It covers a wide range of tasks, models (open and closed source), and domains.", "weaknesses": "**Data formatting.** In section 3.5 (error analysis), the paper mention sometimes the dataset strggles to follow instruction / format the data correctly. Using constrained decoding and similar techniques, this is a very much solved problem, but produce result that the LLM itself may not follow (hence potentially dropping quality of response). I recommend checking out related works in this field (e.g. Guidance[1], AICI[2], LMQL[3], etc.) to improve the data formatting issue. Further more, LLM engiens such as vLLM[4], SGLang[5] and other proprietary engines (Anthropic, Gemini) have provide structured output generation to support constrained decoding at the time of data generation.  \n\n[1] https://github.com/guidance-ai/guidance\n[2] https://github.com/microsoft/aici\n[3] https://lmql.ai/\n[4] https://vllm.ai/\n[5] https://github.com/sgl-project/sglang\n\n\n**Section 3.3 Effectiveness of Modules in DataGen**. \n- Can you explain more on remote-clique score? Why is it a good metric, how exactly is it calculated on the generated dataset (using embeddings, or other representation of the dataset)?\n- The delta of remote-clique score of HellaSwag is significantly higher than other datasets. Why is that?", "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": "9pdu9156YT", "reviewer": "Reviewer_S9ep", "summary": "The paper introduces DateGen that uses LLM to generate synthetic dataset. DateGen overcomes limitations in generalization, controllability, diversity, and truthfulness, DATAGEN supports a variety of dataset formats and includes mechanisms like attribute-guided generation and group-checking to enhance diversity. It also employs mathematical code-based assessment and Retrieval-Augmented Generation (RAG) for accuracy and truthfulness. Experimentation confirms superior data quality, with applications in benchmarking LLMs and data augmentation, leading to improved model performance in domains like reasoning and agent capabilities", "questions": "My questions are included in the weakness section.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.  DataGen introduces novel elements like attribute-guided generation and the RAG-based validation, which distinguish it from existing synthetic dataset generation frameworks.\n2. The modular design allows for customization and adaptability across diverse datasets.\n3. The experiments with improved reasoning and agent-oriented tasks performance shows potential in this data generation framework.", "weaknesses": "1. RAG-based validation is very high in cost (raising cost from 0.038 to 0.19, almost 5x increase). However, it is unclear how it affects the final data generation quality (like the results in Table 7). In other words, it would be nicer to ablate the modules in terms of metrics in Table 7, instead of the current reports in Table 4.\n2. I am not convinced that the performance decline on GSM8K in your experiments can be concluded to that many LLMs may be overstated and overfit on the GSM8K dataset, would you please elaborate more on this?\n3. Have you tried any experiments with open-sourced LLMs such as LLaMA 405B being the generating LLM? Would it be as beneficial as the GPT-4 or Claude?", "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": "OKNPT4CM77", "reviewer": "Reviewer_eQ9J", "summary": "This paper proposes a new framework to synthesize high-quality datasets across various types. To ensure dataset quality, the framework integrates an attribute-guided generation module and a group-checking feature to enhance diversity and controllability. It also includes a code-based mathematical assessment and a retrieval-augmented generation module to improve truthfulness and factuality. Experimental results demonstrate the superior quality of the generated datasets in terms of semantics, diversity, and length distribution. By applying the framework to two scenarios, benchmarking LLMs and data augmentation, it validates the effectiveness of this framework.", "questions": "please refer to weaknesses.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "- By integrating different modules, DataGen ensures the quality of generated datasets, considering the diversity, truthfulness, controllability, and so on.\n- To assess the quality of generated datasets, the authors design curated experiments and evaluate key factors including length distribution, semantics, diversity, and knowledge richness.\n- Experiments on DataGen's effectiveness for data augmentation demonstrate significant benefits across various tasks, particularly in instruction-following scenarios.", "weaknesses": "- In Table 1, the authors list a series of current dataset generation frameworks, highlighting that DataGen considers a broader range of factors. However, for downstream applications, especially data augmentation section, none of these methods are compared, which limits the demonstration of their effectiveness.\n- As shown in Figure 4(a), while the length distribution of the generated data tends toward a normal distribution, longer-length samples are missing for HellaSwag and MMLU. In Figure 5, although the generated examples align with the original datasets, it is evident that the generated dataset represents only a partial subset of the originals.\n- Table 7 shows that, without difficulty enhancements, LLMs perform better on generated benchmarks compared to the originals, which reduces DataGen's effectiveness and practical value.\n- The proposed framework is complex, and in the ablation study presented in Table 4, the analysis may be too simplified to fully validate the effectiveness of each module.", "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)