Summary of Exploring and Benchmarking the Planning Capabilities Of Large Language Models, by Bernd Bohnet et al.
Exploring and Benchmarking the Planning Capabilities of Large Language Models
by Bernd Bohnet, Azade Nova, Aaron T Parisi, Kevin Swersky, Katayoon Goshvadi, Hanjun Dai, Dale Schuurmans, Noah Fiedel, Hanie Sedghi
First submitted to arxiv on: 18 Jun 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 paper proposes a comprehensive benchmark suite for evaluating large language models’ (LLMs’) planning capabilities, encompassing both classical and natural language scenarios. The authors develop algorithms to generate instances of tasks with varying levels of difficulty, allowing for systematic evaluation of LLM performance. They also investigate the use of many-shot in-context learning, fine-tuning LLMs, and chain-of-thought reasoning methods to improve LLM planning. The results show that these approaches enhance LLM planning performance, even in out-of-distribution scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps big language models get better at planning by creating a set of tasks they can practice on. It’s like creating a harder workout routine for them! They test how well the models do on different types of problems and find that giving them more context to work with makes them better planners. The authors also look at what happens when the models encounter new, unexpected challenges. |
Keywords
» Artificial intelligence » Fine tuning
