Summary of Autodetect: Towards a Unified Framework For Automated Weakness Detection in Large Language Models, by Jiale Cheng et al.
AutoDetect: Towards a Unified Framework for Automated Weakness Detection in Large Language Models
by Jiale Cheng, Yida Lu, Xiaotao Gu, Pei Ke, Xiao Liu, Yuxiao Dong, Hongning Wang, Jie Tang, Minlie Huang
First submitted to arxiv on: 24 Jun 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 This paper presents a unified framework, AutoDetect, to automatically identify weaknesses in Large Language Models (LLMs) across various tasks. The framework consists of three agents: Examiner, Questioner, and Assessor, inspired by the educational assessment process. By collaborating with each other, these agents realize comprehensive weakness identification, achieving an identification success rate exceeding 30% in prominent models like ChatGPT and Claude. The identified weaknesses can guide specific model improvements, proving more effective than untargeted data augmentation methods. The approach has led to substantial enhancements in popular LLMs, including the Llama series and Mistral-7b, boosting their performance by over 10% across several benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding and fixing mistakes in Large Language Models (LLMs). These models are getting very good at understanding and generating human-like text, but they still make some errors. The researchers created a new way to automatically find these mistakes, called AutoDetect. It works by asking the LLMs questions and checking their answers. This helps identify areas where the models need improvement. By fixing these weaknesses, the models can get even better at tasks like writing and understanding text. |
Keywords
» Artificial intelligence » Boosting » Claude » Data augmentation » Llama
