Summary of Escapebench: Pushing Language Models to Think Outside the Box, by Cheng Qian and Peixuan Han and Qinyu Luo and Bingxiang He and Xiusi Chen and Yuji Zhang and Hongyi Du and Jiarui Yao and Xiaocheng Yang and Denghui Zhang and Yunzhu Li and Heng Ji
EscapeBench: Pushing Language Models to Think Outside the Box
by Cheng Qian, Peixuan Han, Qinyu Luo, Bingxiang He, Xiusi Chen, Yuji Zhang, Hongyi Du, Jiarui Yao, Xiaocheng Yang, Denghui Zhang, Yunzhu Li, Heng Ji
First submitted to arxiv on: 18 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 The proposed benchmark suite, EscapeBench, challenges language model agents in creative adaptation tasks, such as reasoning, unconventional tool use, and iterative problem-solving to uncover implicit goals. The current models excel in long-session planning but struggle with creativity, achieving only 15% average progress without hints. To bridge this gap, the authors propose a framework called EscapeAgent that enhances creative reasoning through Foresight (innovative tool use) and Reflection (identifying unsolved tasks). Experiments show that EscapeAgent executes action chains over 1,000 steps while maintaining logical coherence, outperforming current models with up to 40% fewer steps and hints. The framework achieves higher action success rates with more efficient and innovative puzzle-solving strategies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Language model agents are very good at planning ahead and making decisions, but they often struggle when faced with creative challenges that require thinking outside the box. To help these agents improve their creativity, researchers have developed a new benchmark test called EscapeBench. This test involves solving puzzles and problems in a virtual “escape room” environment. The results show that current language models are not very good at this type of task, only achieving 15% progress without any hints. To help them improve, the authors propose a new framework called EscapeAgent that helps agents think more creatively by using innovative tools and strategies. |
Keywords
» Artificial intelligence » Language model
