EvalLM: Interactive Evaluation of Large Language Model Prompts on User-Defined Criteria

Author:

Kim Tae Soo1ORCID,Lee Yoonjoo1ORCID,Shin Jamin2ORCID,Kim Young-Ho2ORCID,Kim Juho1ORCID

Affiliation:

1. School of Computing, KAIST, Korea, Republic of

2. NAVER AI Lab, Korea, Republic of

Funder

Ministry of Science and ICT, South Korea

Publisher

ACM

Reference75 articles.

1. Afra Feyza Akyürek, Ekin Akyürek, Aman Madaan, Ashwin Kalyan, Peter Clark, Derry Wijaya, and Niket Tandon. 2023. RL4F: Generating Natural Language Feedback with Reinforcement Learning for Repairing Model Outputs. arXiv preprint arXiv:2305.08844 (2023).

2. Out of One, Many: Using Language Models to Simulate Human Samples

3. Paper Plain : Making Medical Research Papers Approachable to Healthcare Consumers with Natural Language Processing

4. Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, 2022. Constitutional ai: Harmlessness from ai feedback. arXiv preprint arXiv:2212.08073 (2022).

5. Yushi Bai, Jiahao Ying, Yixin Cao, Xin Lv, Yuze He, Xiaozhi Wang, Jifan Yu, Kaisheng Zeng, Yijia Xiao, Haozhe Lyu, 2023. Benchmarking Foundation Models with Language-Model-as-an-Examiner. arXiv preprint arXiv:2306.04181 (2023).

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