# UltraEval: A Lightweight Platform for Flexible and Comprehensive Evaluation for LLMs

arXiv: https://arxiv.org/abs/2404.07584 · HTML: https://mlsanity.com/paper/2404.07584/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 36 references that are in the map.
- First posted on arXiv 11 Apr 2024.

## Builds on (most cited first, 21 of 21)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge](https://mlsanity.com/paper/1803.05457/) (2018)
- [HellaSwag: Can a Machine Really Finish Your Sentence?](https://mlsanity.com/paper/1905.07830/) (2019)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Program Synthesis with Large Language Models](https://mlsanity.com/paper/2108.07732/) (2021)
- [Mistral 7B](https://mlsanity.com/paper/2310.06825/) (2023)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them](https://mlsanity.com/paper/2210.09261/) (2022)
- [OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems](https://mlsanity.com/paper/2402.14008/) (2024)
- [MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies](https://mlsanity.com/paper/2404.06395/) (2024)
- [A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT](https://mlsanity.com/paper/2302.11382/) (2023)
- [PromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts](https://mlsanity.com/paper/2306.04528/) (2023)
- [XuanYuan 2.0: A Large Chinese Financial Chat Model with Hundreds of Billions Parameters](https://mlsanity.com/paper/2305.12002/) (2023)
- [Can GPT-3 Perform Statutory Reasoning?](https://mlsanity.com/paper/2302.06100/) (2023)
- [LatestEval: Addressing Data Contamination in Language Model Evaluation through Dynamic and Time-Sensitive Test Construction](https://mlsanity.com/paper/2312.12343/) (2023)

## Built on by (most cited first, 24 of 24)

- [xVerify: Efficient Answer Verifier for Reasoning Model Evaluations](https://mlsanity.com/paper/2504.10481/) (2025)
- [Do These LLM Benchmarks Agree? Fixing Benchmark Evaluation with BenchBench](https://mlsanity.com/paper/2407.13696/) (2024)
- [Beyond the Turn-Based Game: Enabling Real-Time Conversations with Duplex Models](https://mlsanity.com/paper/2406.15718/) (2024)
- [xFinder: Large Language Models as Automated Evaluators for Reliable Evaluation](https://mlsanity.com/paper/2405.11874/) (2024)
- [A User-Centric Multi-Intent Benchmark for Evaluating Large Language Models](https://mlsanity.com/paper/2404.13940/) (2024)
- [Scaling Physical Reasoning with the PHYSICS Dataset](https://mlsanity.com/paper/2506.00022/) (2025)
- [LongDPO: Unlock Better Long-form Generation Abilities for LLMs via Critique-augmented Stepwise Information](https://mlsanity.com/paper/2502.02095/) (2025)
- [Benchmarking Large Language Models on Multiple Tasks in Bioinformatics NLP with Prompting](https://mlsanity.com/paper/2503.04013/) (2025)
- [MALoRA: Mixture of Asymmetric Low-Rank Adaptation for Enhanced Multi-Task Learning](https://mlsanity.com/paper/2410.22782/) (2024)
- [Enabling Real-Time Conversations with Minimal Training Costs](https://mlsanity.com/paper/2409.11727/) (2024)
- [UltraEval-Audio: A Unified Framework for Comprehensive Evaluation of Audio Foundation Models](https://mlsanity.com/paper/2601.01373/) (2026)
- [Compound-QA: A Benchmark for Evaluating LLMs on Compound Questions](https://mlsanity.com/paper/2411.10163/) (2024)
- [Towards Efficient Multi-LLM Inference: Characterization and Analysis of LLM Routing and Hierarchical Techniques](https://mlsanity.com/paper/2506.06579/) (2025)
- [LOOM-Scope: a comprehensive and efficient LOng-cOntext Model evaluation framework](https://mlsanity.com/paper/2507.04723/) (2025)
- [Behavioral Fingerprinting of Large Language Models](https://mlsanity.com/paper/2509.04504/) (2025)
- [Do We Need Distinct Representations for Every Speech Token? Unveiling and Exploiting Redundancy in Large Speech Language Models](https://mlsanity.com/paper/2604.06871/) (2026)
- [CoCo-Bench: A Comprehensive Code Benchmark For Multi-task Large Language Model Evaluation](https://mlsanity.com/paper/2504.20673/) (2025)
- [Beyond Benchmarks: LLM Evaluation with an Anthropomorphic and Lifecycle-oriented Roadmap](https://mlsanity.com/paper/2508.18646/) (2025)
- [GlotEval: A Test Suite for Massively Multilingual Evaluation of Large Language Models](https://mlsanity.com/paper/2504.04155/) (2025)
- [Integration of TinyML and LargeML: A Survey of 6G and Beyond](https://mlsanity.com/paper/2505.15854/) (2025)
- [Eka-Eval: An Evaluation Framework for Low-Resource Multilingual Large Language Models](https://mlsanity.com/paper/2507.01853/) (2025)
- [DEP: A Decentralized Large Language Model Evaluation Protocol](https://mlsanity.com/paper/2603.01167/) (2026)
- [Putting HUMANS first: Efficient LAM Evaluation with Human Preference Alignment](https://mlsanity.com/paper/2605.00022/) (2026)
- [SkillAtlas: An Attack Trace Library for Agent Skills](https://mlsanity.com/paper/2609.13353/) (2026)

## Abstract (arXiv)

Evaluation is pivotal for refining Large Language Models (LLMs), pinpointing their capabilities, and guiding enhancements. The rapid development of LLMs calls for a lightweight and easy-to-use framework for swift evaluation deployment. However, considering various implementation details, developing a comprehensive evaluation platform is never easy. Existing platforms are often complex and poorly modularized, hindering seamless incorporation into research workflows. This paper introduces UltraEval, a user-friendly evaluation framework characterized by its lightweight nature, comprehensiveness, modularity, and efficiency. We identify and reimplement three core components of model evaluation (models, data, and metrics). The resulting composability allows for the free combination of different models, tasks, prompts, benchmarks, and metrics within a unified evaluation workflow. Additionally, UltraEval supports diverse models owing to a unified HTTP service and provides sufficient inference acceleration. UltraEval is now available for researchers publicly.