One-Eval: An Agentic System for Automated and Traceable LLM Evaluation

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References (9)

[1]
Towards Engineering Multi-Agent LLMs: A Protocol-Driven Approach
2025Zhenyu Mao, Jacky W. Keung et al.
[2]
SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering
2024Carlos E. Jimenez, K. Lieret et al.
[3]
GPQA: A Graduate-Level Google-Proof Q&A Benchmark
2023David Rein, Betty Li Hou et al.
[4]
AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation
2023Qingyun Wu, Gagan Bansal et al.
[5]
AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models
2023Wanjun Zhong, Ruixiang Cui et al.
[6]
ReAct: Synergizing Reasoning and Acting in Language Models
2022Shunyu Yao, Jeffrey Zhao et al.
[7]
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
2022Aarohi Srivastava, Abhinav Rastogi et al.
[8]
Training Verifiers to Solve Math Word Problems
2021K. Cobbe, Vineet Kosaraju et al.
[9]
Measuring Mathematical Problem Solving With the MATH Dataset
2021Dan Hendrycks, Collin Burns et al.

Founder's Pitch

"One-Eval automates and streamlines the evaluation of large language models through customizable workflows based on natural language requests."

LLM EvaluationScore: 8View PDF ↗

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