# A Survey of Useful LLM Evaluation

arXiv: https://arxiv.org/abs/2406.00936 · HTML: https://mlsanity.com/paper/2406.00936/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 103 of its 168 references that are in the map.
- First posted on arXiv 3 Jun 2024.

## Builds on (most cited first, 30 of 103)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Measuring Mathematical Problem Solving With the MATH Dataset](https://mlsanity.com/paper/2103.03874/) (2021)
- [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering](https://mlsanity.com/paper/1809.09600/) (2018)
- [TruthfulQA: Measuring How Models Mimic Human Falsehoods](https://mlsanity.com/paper/2109.07958/) (2021)
- [PIQA: Reasoning about Physical Commonsense in Natural Language](https://mlsanity.com/paper/1911.11641/) (2019)
- [Self-Instruct: Aligning Language Models with Self-Generated Instructions](https://mlsanity.com/paper/2212.10560/) (2022)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering](https://mlsanity.com/paper/1809.02789/) (2018)
- [WebArena: A Realistic Web Environment for Building Autonomous Agents](https://mlsanity.com/paper/2307.13854/) (2023)
- [Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models](https://mlsanity.com/paper/2206.04615/) (2022)
- [ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs](https://mlsanity.com/paper/2307.16789/) (2023)
- [Language Models as Knowledge Bases?](https://mlsanity.com/paper/1909.01066/) (2019)
- [CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge](https://mlsanity.com/paper/1811.00937/) (2018)
- [Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them](https://mlsanity.com/paper/2210.09261/) (2022)
- [Gorilla: Large Language Model Connected with Massive APIs](https://mlsanity.com/paper/2305.15334/) (2023)
- [Code as Policies: Language Model Programs for Embodied Control](https://mlsanity.com/paper/2209.07753/) (2022)
- [Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting](https://mlsanity.com/paper/2305.04388/) (2023)
- [Inner Monologue: Embodied Reasoning through Planning with Language Models](https://mlsanity.com/paper/2207.05608/) (2022)
- [Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents](https://mlsanity.com/paper/2201.07207/) (2022)
- [A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity](https://mlsanity.com/paper/2302.04023/) (2023)
- [BeaverTails: Towards Improved Safety Alignment of LLM via a Human-Preference Dataset](https://mlsanity.com/paper/2307.04657/) (2023)
- [VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models](https://mlsanity.com/paper/2307.05973/) (2023)
- [AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback](https://mlsanity.com/paper/2305.14387/) (2023)

## Built on by (most cited first, 29 of 29)

- [Why Do Multi-Agent LLM Systems Fail?](https://mlsanity.com/paper/2503.13657/) (2025)
- [Multi-Agent Collaboration Mechanisms: A Survey of LLMs](https://mlsanity.com/paper/2501.06322/) (2025)
- [A Survey of Context Engineering for Large Language Models](https://mlsanity.com/paper/2507.13334/) (2025)
- [The Scales of Justitia: A Comprehensive Survey on Safety Evaluation of LLMs](https://mlsanity.com/paper/2506.11094/) (2025)
- [What Makes a Good Story and How Can We Measure It? A Comprehensive Survey of Story Evaluation](https://mlsanity.com/paper/2408.14622/) (2024)
- [Long Context Scaling: Divide and Conquer via Multi-Agent Question-driven Collaboration](https://mlsanity.com/paper/2505.20625/) (2025)
- [Open Ko-LLM Leaderboard2: Bridging Foundational and Practical Evaluation for Korean LLMs](https://mlsanity.com/paper/2410.12445/) (2024)
- [MedOmni-45deg: A Safety-Performance Benchmark for Reasoning-Oriented LLMs in Medicine](https://mlsanity.com/paper/2508.16213/) (2025)
- [Towards Alignment-Centric Paradigm: A Survey of Instruction Tuning in Large Language Models](https://mlsanity.com/paper/2508.17184/) (2025)
- [TestAgent: Automatic Benchmarking and Exploratory Interaction for Evaluating LLMs in Vertical Domains](https://mlsanity.com/paper/2410.11507/) (2024)
- [Noise, Adaptation, and Strategy: Assessing LLM Fidelity in Decision-Making](https://mlsanity.com/paper/2508.15926/) (2025)
- [A Principle-based Framework for the Development and Evaluation of Large Language Models for Health and Wellness](https://mlsanity.com/paper/2512.08936/) (2025)
- [Rethinking Role-Playing Evaluation: Anonymous Benchmarking and a Systematic Study of Personality Effects](https://mlsanity.com/paper/2603.03915/) (2026)
- [A Survey on Complex Tasks for Goal-Directed Interactive Agents](https://mlsanity.com/paper/2409.18538/) (2024)
- [Representing the Under-Represented: Cultural and Core Capability Benchmarks for Developing Thai Large Language Models](https://mlsanity.com/paper/2410.04795/) (2024)
- [Cross-Entropy Games for Language Models: From Implicit Knowledge to General Capability Measures](https://mlsanity.com/paper/2506.06832/) (2025)
- [Reflections and New Directions for Human-Centered Large Language Models](https://mlsanity.com/paper/2605.06901/) (2026)
- [Can LLM Assist in the Evaluation of the Quality of Machine Learning Explanations?](https://mlsanity.com/paper/2502.20635/) (2025)
- [Model Consistency as a Cheap yet Predictive Proxy for LLM Elo Scores](https://mlsanity.com/paper/2509.23510/) (2025)
- [Benchmarking Open-Source Large Language Models for Persian in Zero-Shot and Few-Shot Learning](https://mlsanity.com/paper/2510.12807/) (2025)
- [If I Could Turn Back Time: Temporal Reframing as a Historical Reasoning Task for LLMs](https://mlsanity.com/paper/2511.04432/) (2025)
- [Key Considerations for Domain Expert Involvement in LLM Design and Evaluation: An Ethnographic Study](https://mlsanity.com/paper/2602.14357/) (2026)
- [When Passing Tests Hides Vulnerabilities: An Empirical Study of Silent Failures in Agentic Systems](https://mlsanity.com/paper/2609.10548/) (2026)
- [QUENCH: Measuring the gap between Indic and Non-Indic Contextual General Reasoning in LLMs](https://mlsanity.com/paper/2412.11763/) (2024)
- [Make a Video Call with LLM: A Measurement Campaign over Six Mainstream Apps](https://mlsanity.com/paper/2510.00481/) (2025)
- [Validity Is What You Need](https://mlsanity.com/paper/2510.27628/) (2025)
- [Conversational No-code, Multi-agentic Disease Module Identification and Drug Repurposing Prediction with ChatDRex](https://mlsanity.com/paper/2511.21438/) (2025)
- [Towards Privacy-Preserving Code Generation: Differentially Private Code Language Models](https://mlsanity.com/paper/2512.11482/) (2025)
- [Beyond Aggregate Scores: Behavioral Correctness Assumptions for Assessing Reference-Based Automatic Evaluation Methods](https://mlsanity.com/paper/2609.05289/) (2026)

## Abstract (arXiv)

LLMs have gotten attention across various research domains due to their exceptional performance on a wide range of complex tasks. Therefore, refined methods to evaluate the capabilities of LLMs are needed to determine the tasks and responsibility they should undertake. Our study mainly discussed how LLMs, as useful tools, should be effectively assessed. We proposed the two-stage framework: from ``core ability'' to ``agent'', clearly explaining how LLMs can be applied based on their specific capabilities, along with the evaluation methods in each stage. Core ability refers to the capabilities that LLMs need in order to generate high-quality natural language texts. After confirming LLMs possess core ability, they can solve real-world and complex tasks as agent. In the "core ability" stage, we discussed the reasoning ability, societal impact, and domain knowledge of LLMs. In the ``agent'' stage, we demonstrated embodied action, planning, and tool learning of LLMs agent applications. Finally, we examined the challenges currently confronting the evaluation methods for LLMs, as well as the directions for future development.