Summary of Learning to Ask: When Llm Agents Meet Unclear Instruction, by Wenxuan Wang et al.
Learning to Ask: When LLM Agents Meet Unclear Instruction
by Wenxuan Wang, Juluan Shi, Zixuan Ling, Yuk-Kit Chan, Chaozheng Wang, Cheryl Lee, Youliang Yuan, Jen-tse Huang, Wenxiang Jiao, Michael R. Lyu
First submitted to arxiv on: 31 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
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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 In this paper, researchers investigate the capabilities of large language models (LLMs) in utilizing external tools to perform various tasks that exceed their linguistic abilities. They find that while LLMs can leverage these tools, they often require precise user instructions, which may not be feasible in real-world scenarios. To address this issue, the authors develop a benchmark called Noisy ToolBench (NoisyToolBench) and propose a novel framework, Ask-when-Needed (AwN), that prompts LLMs to ask users questions when faced with unclear instructions. They also design an automated evaluation tool named ToolEvaluator to assess LLM performance in tool utilization from both accuracy and efficiency perspectives. The results show that AwN significantly outperforms existing frameworks for tool learning in the NoisyToolBench. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can do many things, but they often need help using special tools to get the job done. This is because these tools require specific instructions, which might not always be clear or easy to understand. To make sure these language models are working correctly, researchers created a special test called Noisy ToolBench (NoisyToolBench). They also developed a new way for language models to ask questions when they’re unsure what to do next. This is important because it helps the language models work more accurately and efficiently with these tools. |
