Existing tool learning benchmarks have limitations in evaluating Large Language Models (LLMs) ability to use tools effectively in complex, real-world scenarios. These limitations include a lack of multi-turn dialogue evaluations, limited assessment of fine-grained function calls, and high evaluation costs. This paper proposes ACEBench, a comprehensive benchmark designed to address these shortcomings.
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https://arxiv.org/abs/2501.12851
Original Problem 🤔:
→ Current benchmarks inadequately assess LLM tool use in realistic multi-turn dialogues.
→ Evaluations lack granularity in assessing specific function call components.
→ Existing evaluation methods often incur high costs.
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Solution in this Paper 💡:
→ ACEBench offers a diverse dataset encompassing single-turn, multi-turn, and agent-based scenarios, categorized as normal, special, and agent interactions.
→ It employs a multi-stage data verification process to ensure data quality.
→ Evaluation leverages automated parsing and metric calculation.
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Key Insights from this Paper 🔎:
→ Closed-source LLMs like GPT-4 outperform open-source counterparts, though the gap is narrowing.
→ Fine-tuning on specific datasets can hinder performance on general tool usage.
→ LLMs struggle with complex multi-turn interactions in agent-based scenarios.
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Results 📊:
→ GPT-4 achieves an overall accuracy of 86% on ACEBench.
→ Qwen2.5-Coder-32B-Instruct achieves 80%.
→ Agent-based scenarios pose the greatest challenge, with most models achieving less than 50% accuracy.
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1ST SET OF HOOKS
ACEBench provides a robust framework for evaluating LLM tool use in realistic scenarios.
ACEBench evaluates LLMs across diverse tool-usage scenarios with automated metrics.
ACEBench offers fine-grained evaluation of LLM function calling capabilities.
ACEBench assesses LLM tool learning in complex multi-turn dialogues.
2nd SET OF HOOKS
ACEBench: Putting LLMs' tool skills to the ultimate test.
ACEBench: Is your LLM truly tool-savvy? Find out now.
ACEBench: A new benchmark for LLM tool mastery.
ACEBench: Level up your LLM tool evaluation game.
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