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| Format: | Preprint |
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2024
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| Online-Zugang: | https://arxiv.org/abs/2410.05669 |
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| _version_ | 1866911474145820672 |
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| author | Kokel, Harsha Katz, Michael Srinivas, Kavitha Sohrabi, Shirin |
| author_facet | Kokel, Harsha Katz, Michael Srinivas, Kavitha Sohrabi, Shirin |
| contents | There is an increasing body of work using Large Language Models (LLMs) as agents for orchestrating workflows and making decisions in domains that require planning and multi-step reasoning. As a result, it is imperative to evaluate LLMs on core skills required for planning. In this work, we present ACPBench, a benchmark for evaluating the reasoning tasks in the field of planning. The benchmark consists of 7 reasoning tasks over 13 planning domains. The collection is constructed from planning domains described in a formal language. This allows us to synthesize problems with provably correct solutions across many tasks and domains. Further, it allows us the luxury of scale without additional human effort, i.e., many additional problems can be created automatically. Our extensive evaluation of 22 LLMs and OpenAI o1 reasoning models highlights the significant gap in the reasoning capability of the LLMs. Our findings with OpenAI o1, a multi-turn reasoning model, reveal significant gains in performance on multiple-choice questions, yet surprisingly, no notable progress is made on boolean questions.
The ACPBench collection is available at https://ibm.github.io/ACPBench. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_05669 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ACPBench: Reasoning about Action, Change, and Planning Kokel, Harsha Katz, Michael Srinivas, Kavitha Sohrabi, Shirin Artificial Intelligence There is an increasing body of work using Large Language Models (LLMs) as agents for orchestrating workflows and making decisions in domains that require planning and multi-step reasoning. As a result, it is imperative to evaluate LLMs on core skills required for planning. In this work, we present ACPBench, a benchmark for evaluating the reasoning tasks in the field of planning. The benchmark consists of 7 reasoning tasks over 13 planning domains. The collection is constructed from planning domains described in a formal language. This allows us to synthesize problems with provably correct solutions across many tasks and domains. Further, it allows us the luxury of scale without additional human effort, i.e., many additional problems can be created automatically. Our extensive evaluation of 22 LLMs and OpenAI o1 reasoning models highlights the significant gap in the reasoning capability of the LLMs. Our findings with OpenAI o1, a multi-turn reasoning model, reveal significant gains in performance on multiple-choice questions, yet surprisingly, no notable progress is made on boolean questions. The ACPBench collection is available at https://ibm.github.io/ACPBench. |
| title | ACPBench: Reasoning about Action, Change, and Planning |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2410.05669 |