Summary of Rotbench: a Multi-level Benchmark For Evaluating the Robustness Of Large Language Models in Tool Learning, by Junjie Ye et al.
RoTBench: A Multi-Level Benchmark for Evaluating the Robustness of Large Language Models in Tool Learning
by Junjie Ye, Yilong Wu, Songyang Gao, Caishuang Huang, Sixian Li, Guanyu Li, Xiaoran Fan, Qi Zhang, Tao Gui, Xuanjing Huang
First submitted to arxiv on: 16 Jan 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 novel approach to evaluate the robustness of Large Language Models (LLMs) in tool learning is proposed, filling a gap in current research that primarily focuses on LLMs’ ability to use tools in structured environments. The authors introduce RoTBench, a multi-level benchmark featuring five external environments with varying levels of noise, to assess the models’ resilience across three critical phases: tool selection, parameter identification, and content filling. Experiments with six widely-used models highlight the need for enhancing LLMs’ robustness in tool learning, as performance drops significantly when encountering mild noise. The authors propose RoTTuning, a strategy that enriches training environments to improve model adaptability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Tool learning is an important way for Large Language Models (LLMs) to interact with the physical world. Right now, most research focuses on how well LLMs can use tools in controlled situations. But what happens when things get messy and noisy? To answer this question, researchers created RoTBench, a special test that simulates different levels of noise. They used six popular models to see how they would perform in these different environments. The results showed that even the best models struggled with noise, which is important to know if we want LLMs to be useful in real-life situations. |
