Summary of Foundational Autoraters: Taming Large Language Models For Better Automatic Evaluation, by Tu Vu et al.
Foundational Autoraters: Taming Large Language Models for Better Automatic Evaluation
by Tu Vu, Kalpesh Krishna, Salaheddin Alzubi, Chris Tar, Manaal Faruqui, Yun-Hsuan Sung
First submitted to arxiv on: 15 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces FLAMe, a family of large language models designed to reliably evaluate the output of other language models. The authors train FLAMe on a diverse collection of quality assessment tasks, comprising over 5 million human judgments, and demonstrate its ability to generalize to various held-out tasks. Compared to proprietary LLMs like GPT-4 and Claude-3, FLAMe outperforms them on many tasks. Additionally, the paper proposes a computationally efficient approach using tail-patch fine-tuning to optimize FLAMe for reward modeling evaluation. The results show that FLAMe variants outperform popular proprietary LLM-as-a-Judge models across 8 out of 12 autorater evaluation benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are getting better and better, but it’s hard to tell if they’re really good or not because we need humans to evaluate them. To solve this problem, the researchers created FLAMe, a special kind of model that can help us understand how well other models are doing. They trained FLAMe on lots of different tasks where people had already judged what was right and wrong. Then they tested it against some other big models and found that FLAMe was better at guessing what the correct answers were. |
Keywords
* Artificial intelligence * Claude * Fine tuning * Gpt
