Summary of Toolbehonest: a Multi-level Hallucination Diagnostic Benchmark For Tool-augmented Large Language Models, by Yuxiang Zhang et al.
ToolBeHonest: A Multi-level Hallucination Diagnostic Benchmark for Tool-Augmented Large Language Models
by Yuxiang Zhang, Jing Chen, Junjie Wang, Yaxin Liu, Cheng Yang, Chufan Shi, Xinyu Zhu, Zihao Lin, Hanwen Wan, Yujiu Yang, Tetsuya Sakai, Tian Feng, Hayato Yamana
First submitted to arxiv on: 28 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 A comprehensive benchmark called ToolBH is introduced to diagnose hallucination issues within large language models (LLMs) used in real-world applications. The benchmark assesses LLMs through two perspectives: depth and breadth. For depth, a multi-level diagnostic process is proposed, including solvability detection, solution planning, and missing-tool analysis. For breadth, three scenarios are considered based on the characteristics of the toolset. A total of seven tasks are developed, with 700 evaluation samples collected through manual annotation. The results show significant challenges presented by the ToolBH benchmark, with advanced models Gemini-1.5-Pro and GPT-4o achieving scores of 45.3 and 37.0 out of 100. Larger model parameters do not guarantee better performance; training data and response strategies also play crucial roles in tool-enhanced LLM scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are being used more and more in real-life applications, but they can make mistakes or “hallucinate.” To fix this problem, researchers created a special test to see how well these models do. The test is called ToolBH and it looks at the models from two different angles: depth and breadth. Depth means looking at how well the model does on specific tasks, and breadth means looking at how well it does overall. The test also looked at what makes the models make mistakes, like what kind of training they had or what kind of responses they gave. |
Keywords
» Artificial intelligence » Gemini » Gpt » Hallucination
