Summary of Revisiting Benchmark and Assessment: An Agent-based Exploratory Dynamic Evaluation Framework For Llms, by Wanying Wang et al.
Revisiting Benchmark and Assessment: An Agent-based Exploratory Dynamic Evaluation Framework for LLMs
by Wanying Wang, Zeyu Ma, Pengfei Liu, Mingang Chen
First submitted to arxiv on: 15 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 presents a novel approach to evaluating the performance of large language models (LLMs) across different domains, introducing two key concepts: Benchmark+ and Assessment+. The traditional question-answer benchmark is extended into a more flexible “strategy-criterion” format, enabling deeper exploration and supporting analysis from broader perspectives. The proposed TestAgent framework implements these concepts using retrieval-augmented generation and reinforcement learning, allowing for automatic dynamic benchmark generation and in-depth assessment across diverse vertical domain scenarios. The effectiveness of TestAgent is demonstrated through experiments on tasks ranging from constructing multiple vertical domain evaluations to converting static benchmarks into dynamic forms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops a way to test big language models that work well across many different areas. Right now, it’s hard to compare how good these models are in different domains because we need lots of data and it doesn’t match what people really want or need. The authors come up with two new ideas: Benchmark+ and Assessment+. They make the traditional testing method more flexible so that people can explore deeper and understand things from many perspectives. Then, they create a framework called TestAgent that uses these ideas to generate tests automatically and assess how well models do in different areas. |
Keywords
» Artificial intelligence » Reinforcement learning » Retrieval augmented generation
