ToolBeHonest: A Multi-level Hallucination Diagnostic Benchmark for Tool-Augmented Large Language Models

• URL: http://arxiv.org/abs/2406.20015v2
• Date: Fri, 04 Oct 2024 07:51:29 GMT
• Title: ToolBeHonest: A Multi-level Hallucination Diagnostic Benchmark for Tool-Augmented Large Language Models
• Authors: 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,
• Abstract summary: Tool-augmented large language models (LLMs) are rapidly being integrated into real-world applications. To address this challenge, we introduce a comprehensive diagnostic benchmark, ToolBH. For breadth, we consider three scenarios based on the characteristics of the toolset: missing necessary tools, potential tools, and limited functionality tools. The results show the significant challenges presented by the ToolBH benchmark.
• Score: 43.895478182631116
• License:
• Abstract: Tool-augmented large language models (LLMs) are rapidly being integrated into real-world applications. Due to the lack of benchmarks, the community has yet to fully understand the hallucination issues within these models. To address this challenge, we introduce a comprehensive diagnostic benchmark, ToolBH. Specifically, we assess the LLM's hallucinations through two perspectives: depth and breadth. In terms of depth, we propose a multi-level diagnostic process, including (1) solvability detection, (2) solution planning, and (3) missing-tool analysis. For breadth, we consider three scenarios based on the characteristics of the toolset: missing necessary tools, potential tools, and limited functionality tools. Furthermore, we developed seven tasks and collected 700 evaluation samples through multiple rounds of manual annotation. The results show the significant challenges presented by the ToolBH benchmark. The current advanced models Gemini-1.5-Pro and GPT-4o only achieve total scores of 45.3 and 37.0, respectively, on a scale of 100. In this benchmark, larger model parameters do not guarantee better performance; the training data and response strategies also play crucial roles in tool-enhanced LLM scenarios. Our diagnostic analysis indicates that the primary reason for model errors lies in assessing task solvability. Additionally, open-weight models suffer from performance drops with verbose replies, whereas proprietary models excel with longer reasoning.

Related papers

• Adaptive Tool Use in Large Language Models with Meta-Cognition Trigger [49.81945268343162]
  We propose MeCo, an adaptive decision-making strategy for external tool use. MeCo captures high-level cognitive signals in the representation space, guiding when to invoke tools. Our experiments show that MeCo accurately detects LLMs' internal cognitive signals and significantly improves tool-use decision-making.
  arXiv  Detail & Related papers  (2025-02-18T15:45:01Z)
• ACEBench: Who Wins the Match Point in Tool Usage? [68.54159348899891]
  ACEBench is a comprehensive benchmark for assessing tool usage in Large Language Models (LLMs) It categorizes data into three primary types based on evaluation methodology: Normal, Special, and Agent. It provides a more granular examination of error causes across different data types.
  arXiv  Detail & Related papers  (2025-01-22T12:59:08Z)
• ToolHop: A Query-Driven Benchmark for Evaluating Large Language Models in Multi-Hop Tool Use [51.43211624452462]
  We present ToolHop, a dataset comprising 995 user queries and 3,912 associated tools. ToolHop ensures diverse queries, meaningful interdependencies, locally executable tools, detailed feedback, and verifiable answers. We evaluate 14 LLMs across five model families, uncovering significant challenges in handling multi-hop tool-use scenarios.
  arXiv  Detail & Related papers  (2025-01-05T11:06:55Z)
• Diagnosing Robotics Systems Issues with Large Language Models [5.30112395683561]
  Large language models (LLMs) excel at analyzing large amounts of data. Here, we extend this work to the challenging and largely unexplored domain of robotics systems.
  arXiv  Detail & Related papers  (2024-10-06T11:58:12Z)
• A Comprehensive Evaluation of Large Language Models on Mental Illnesses [0.8458496687170665]
  GPT-4 and Llama 3 exhibited superior performance in binary disorder detection, with accuracies reaching up to 85% on certain datasets. prompt engineering played a crucial role in enhancing model performance. Despite promising results, our analysis identified several challenges, including variability in performance across datasets and the need for careful prompt engineering.
  arXiv  Detail & Related papers  (2024-09-24T02:58:52Z)
• Towards Completeness-Oriented Tool Retrieval for Large Language Models [60.733557487886635]
  Real-world systems often incorporate a wide array of tools, making it impractical to input all tools into Large Language Models. Existing tool retrieval methods primarily focus on semantic matching between user queries and tool descriptions. We propose a novel modelagnostic COllaborative Learning-based Tool Retrieval approach, COLT, which captures not only the semantic similarities between user queries and tool descriptions but also takes into account the collaborative information of tools.
  arXiv  Detail & Related papers  (2024-05-25T06:41:23Z)
• 3DBench: A Scalable 3D Benchmark and Instruction-Tuning Dataset [13.808860456901204]
  We introduce a scalable 3D benchmark, accompanied by a large-scale instruction-tuning dataset known as 3DBench. Specifically, we establish the benchmark that spans a wide range of spatial and semantic scales, from object-level to scene-level. We present a rigorous pipeline for automatically constructing scalable 3D instruction-tuning datasets, covering 10 diverse multi-modal tasks with more than 0.23 million QA pairs generated in total.
  arXiv  Detail & Related papers  (2024-04-23T02:06:10Z)
• ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models [102.63817106363597]
  We build ELEVATER, the first benchmark to compare and evaluate pre-trained language-augmented visual models. It consists of 20 image classification datasets and 35 object detection datasets, each of which is augmented with external knowledge. We will release our toolkit and evaluation platforms for the research community.
  arXiv  Detail & Related papers  (2022-04-19T10:23:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.