| MMLU-Pro |
TIGER-AI-Lab |
MMLU-Pro |
MMLU-Pro is an improved version of the MMLU dataset. MMLU has long been a reference for multiple-choice knowledge datasets. However, recent studies have shown that it contains noise (some questions are unanswerable) and is too easy (due to the evolution of model capabilities and increased contamination). MMLU-Pro provides ten options instead of four, requires reasoning on more questions, and has undergone expert review to reduce noise. It is of higher quality and more challenging than the original. MMLU-Pro reduces the impact of prompt variations on model performance, a common issue with its predecessor MMLU. Research indicates that models using "Chain of Thought" reasoning perform better on this new benchmark, suggesting that MMLU-Pro is better suited for evaluating the subtle reasoning abilities of AI. (2024-05-20) |
| TrustLLM Benchmark |
TrustLLM |
TrustLLM |
TrustLLM is a benchmark for evaluating the trustworthiness of large language models. It covers six dimensions of trustworthiness and includes over 30 datasets to comprehensively assess the functional capabilities of LLMs, ranging from simple classification tasks to complex generative tasks. Each dataset presents unique challenges and has benchmarked 16 mainstream LLMs (including commercial and open-source models). |
| DyVal |
Microsoft |
DyVal |
Concerns have been raised about the potential data contamination in the vast training corpora of LLMs. Additionally, the static nature and fixed complexity of current benchmarks may not adequately measure the evolving capabilities of LLMs. DyVal is a general and flexible protocol for dynamically evaluating LLMs. Leveraging the advantages of directed acyclic graphs, DyVal dynamically generates evaluation samples with controllable complexity. It has created challenging evaluation sets for reasoning tasks such as mathematics, logical reasoning, and algorithmic problems. Various LLMs, from Flan-T5-large to GPT-3.5-Turbo and GPT-4, have been evaluated. Experiments show that LLMs perform worse on DyVal-generated samples of different complexities, highlighting the importance of dynamic evaluation. The authors also analyze failure cases and results of different prompting methods. Furthermore, DyVal-generated samples not only serve as evaluation sets but also aid in fine-tuning to enhance LLM performance on existing benchmarks. (2024-04-20) |
| RewardBench |
AIAI |
RewardBench |
RewardBench is an evaluation benchmark for language model reward models, assessing the strengths and weaknesses of various models. It reveals that existing models still exhibit significant shortcomings in reasoning and instruction following. It includes a Leaderboard, Code, and Dataset (2024-03-20). |
| LV-Eval |
Infinigence-AI |
LVEval |
LV-Eval is a long-text evaluation benchmark featuring five length tiers (16k, 32k, 64k, 128k, and 256k), with a maximum text test length of 256k. The average text length of LV-Eval is 102,380 characters, with a minimum/maximum text length of 11,896/387,406 characters. LV-Eval primarily consists of two types of evaluation tasks: single-hop QA and multi-hop QA, encompassing 11 sub-datasets in Chinese and English. During its design, LV-Eval introduced three key technologies: Confusion Facts Insertion (CFI) to enhance challenge, Keyword and Phrase Replacement (KPR) to reduce information leakage, and Answer Keywords (AK) based evaluation metrics (combining answer keywords and word blacklists) to improve the objectivity of evaluation results (2024-02-06). |
| LLM-Uncertainty-Bench |
Tencent |
LLM-Uncertainty-Bench |
A new benchmark method for LLMs has been introduced, incorporating uncertainty quantification. Based on nine LLMs tested across five representative NLP tasks, it was found that: I) More accurate LLMs may exhibit lower certainty; II) Larger-scale LLMs may display greater uncertainty than smaller models; III) Instruction fine-tuning tends to increase the uncertainty of LLMs. These findings underscore the importance of including uncertainty in LLM evaluations (2024-01-22). |
| Psychometrics Eval |
Microsoft Research Asia |
Psychometrics Eval |
Microsoft Research Asia has proposed a generalized evaluation method for AI based on psychometrics, aiming to address limitations in traditional evaluation methods concerning predictive power, information volume, and test tool quality. This approach draws on psychometric theories to identify key psychological constructs of AI, design targeted tests, and apply Item Response Theory for precise scoring. It also introduces concepts of reliability and validity to ensure evaluation reliability and accuracy. This framework extends psychometric methods to assess AI performance in handling unknown complex tasks but also faces open questions such as distinguishing between AI "individuals" and "populations," addressing prompt sensitivity, and evaluating differences between human and AI constructs (2023-10-19). |
| CommonGen-Eval |
AllenAI |
CommonGen-Eval |
A study using the CommonGen-lite dataset to evaluate LLMs, employing GPT-4 for assessment and comparing the performance of different models, with results listed on the leaderboard (2024-01-04). |
| felm |
HKUST |
felm |
FELM is a meta-benchmark for evaluating the factual assessment of large language models. The benchmark comprises 847 questions spanning five distinct domains: world knowledge, science/technology, writing/recommendation, reasoning, and mathematics. Prompts corresponding to each domain are gathered from various sources, including standard datasets like TruthfulQA, online platforms like GitHub repositories, ChatGPT-generated prompts, or those drafted by authors. For each response, fine-grained annotation at the segment level is employed, including reference links, identified error types, and reasons behind these errors as provided by annotators (2023-10-03). |
| just-eval |
AI2 Mosaic |
just-eval |
A GPT-based evaluation tool for multi-faceted and explainable assessment of LLMs, capable of evaluating aspects such as helpfulness, clarity, factuality, depth, and engagement (2023-12-05). |
| EQ-Bench |
EQ-Bench |
EQ-Bench |
A benchmark for evaluating the emotional intelligence of language models, featuring 171 questions (compared to 60 in v1) and a new scoring system that better distinguishes performance differences among models (2023-12-20). |
| CRUXEval |
MIT CSAIL |
CRUXEval |
CRUXEval is a benchmark for evaluating code reasoning, understanding, and execution. It includes 800 Python functions and their input-output pairs, testing input prediction and output prediction tasks. Many models that perform well on HumanEval underperform on CRUXEval, highlighting the need for improved code reasoning capabilities. The best model, GPT-4 with chain-of-thought (CoT), achieved pass@1 rates of 75% and 81% for input prediction and output prediction, respectively. The benchmark exposes gaps between open-source and closed-source models. GPT-4 failed to fully pass CRUXEval, providing insights into its limitations and directions for improvement (2024-01-05). |
| MLAgentBench |
snap-stanford |
MLAgentBench |
MLAgentBench is a suite of end-to-end machine learning (ML) research tasks for benchmarking AI research agents. These agents aim to autonomously develop or improve an ML model based on a given dataset and ML task description. Each task represents an interactive environment that directly reflects what human researchers encounter. Agents can read available files, run multiple experiments on compute clusters, and analyze results to achieve the specified research objectives. Specifically, it includes 15 diverse ML engineering tasks that can be accomplished by attempting different ML methods, data processing, architectures, and training processes (2023-10-05). |
| AlignBench |
THUDM |
AlignBench |
AlignBench is a comprehensive and multi-dimensional benchmark for evaluating the alignment performance of Chinese large language models. It constructs a human-in-the-loop data creation process to ensure dynamic data updates. AlignBench employs a multi-dimensional, rule-based model evaluation method (LLM-as-Judge) and combines chain-of-thought (CoT) to generate multi-dimensional analyses and final comprehensive scores for model responses, enhancing the reliability and explainability of evaluations (2023-12-01). |
| UltraEval |
OpenBMB |
UltraEval |
UltraEval is an open-source foundational model capability evaluation framework offering a lightweight and easy-to-use evaluation system that supports mainstream large model performance assessments. Its key features include: (1) a lightweight and user-friendly evaluation framework with intuitive design, minimal dependencies, easy deployment, and good scalability for various evaluation scenarios; (2) flexible and diverse evaluation methods with unified prompt templates and rich evaluation metrics, supporting customization; (3) efficient and rapid inference deployment supporting multiple model deployment solutions, including torch and vLLM, and enabling multi-instance deployment to accelerate the evaluation process; (4) a transparent and open leaderboard with publicly accessible, traceable, and reproducible evaluation results driven by the community to ensure transparency; and (5) official and authoritative evaluation data using widely recognized official datasets to guarantee evaluation fairness and standardization, ensuring result comparability and reproducibility (2023-11-24). |
| IFEval |
google-research |
Instruction Following Eval |
Following natural language instructions is a core capability of large language models. However, the evaluation of this capability lacks standardization: human evaluation is expensive, slow, and lacks objective reproducibility, while automated evaluation based on LLMs may be biased by the evaluator LLM's capabilities or limitations. To address these issues, researchers at Google introduced Instruction Following Evaluation (IFEval), a simple and reproducible benchmark focusing on a set of "verifiable instructions," such as "write over 400 words" and "mention the AI keyword at least 3 times." IFEval identifies 25 such verifiable instructions and constructs approximately 500 prompts, each containing one or more verifiable instructions (2023-11-15). |
| LLMBar |
princeton-nlp |
LLMBar |
LLMBar is a challenging meta-evaluation benchmark designed to test the ability of LLM evaluators to identify instruction-following outputs. It contains 419 instances, each consisting of an instruction and two outputs: one faithfully and correctly following the instruction, and the other deviating from it. Each instance also includes a gold label indicating which output is objectively better (2023-10-29). |
| HalluQA |
Fudan, Shanghai AI Lab |
HalluQA |
HalluQA is a Chinese LLM hallucination evaluation benchmark, featuring 450 data points including 175 misleading entries, 69 hard misleading entries, and 206 knowledge-based entries. Each question has an average of 2.8 correct and incorrect answers annotated. To enhance the usability of HalluQA, the authors designed a GPT-4-based evaluation method. Specifically, hallucination criteria and correct answers are input as instructions to GPT-4, which evaluates whether the model's response contains hallucinations. |
| FMTI |
Stanford |
FMTI |
The Foundation Model Transparency Index (FMTI) evaluates the transparency of developers in model training and deployment across 100 indicators, including data, computational resources, and labor. Evaluations of flagship models from 10 companies reveal an average transparency score of only 37/100, indicating significant room for improvement. |
| ColossalEval |
Colossal-AI |
ColossalEval |
A project by Colossal-AI offering a unified evaluation workflow for assessing language models on public datasets or custom datasets using traditional metrics and GPT-assisted evaluations. |
| LLMEval²-WideDeep |
Alibaba Research |
LLMEval² |
Constructed as the largest and most diverse English evaluation benchmark for LLM evaluators, featuring 15 tasks, 8 capabilities, and 2,553 samples. Experimental results indicate that a wider network (involving many reviewers) with two layers (one round of discussion) performs best, improving the Kappa correlation coefficient from 0.28 to 0.34. WideDeep is also utilized to assist in evaluating Chinese LLMs, accelerating the evaluation process by 4.6 times and reducing costs by 60%. |
| Aviary |
Ray Project |
Aviary |
Enables interaction with various large language models (LLMs) in one place. Direct comparison of different model outputs, ranking by quality, and obtaining cost and latency estimates are supported. It particularly supports models hosted on Hugging Face and in many cases, also supports DeepSpeed inference acceleration. |
| Do-Not-Answer |
Libr-AI |
Do-Not-Answer |
An open-source dataset designed to evaluate the safety mechanisms of LLMs at a low cost. It consists of prompts that responsible language models should not respond to. In addition to human annotations, it implements model-based evaluation, where a BERT-like evaluator fine-tuned 600 million times achieves results comparable to humans and GPT-4. |
| LucyEval |
Oracle |
LucyEval |
Chinese LLM maturity evaluation—LucyEval can objectively test various aspects of model capabilities, identify model shortcomings, and help designers and engineers more accurately adjust and train models, aiding LLMs in advancing toward greater intelligence. |
| Zhujiu |
Institute of Automation, CAS |
Zhujiu |
Covers seven capability dimensions and 51 tasks; employs three complementary evaluation methods; offers comprehensive Chinese benchmarking with English evaluation capabilities. |
| ChatEval |
THU-NLP |
ChatEval |
ChatEval aims to simplify the human evaluation process of generated text. Given different text fragments, roles (played by master's students) in ChatEval can autonomously discuss nuances and differences, providing judgments based on their designated roles. |
| FlagEval |
Zhiyuan/Tsinghua |
FlagEval |
Produced by Zhiyuan, combining subjective and objective scoring to offer LLM score rankings. |
| InfoQ Comprehensive LLM Evaluation |
InfoQ |
InfoQ Evaluation |
Chinese-oriented ranking: ChatGPT > Wenxin Yiyang > Claude > Xinghuo. |