Summary of Plancraft: An Evaluation Dataset For Planning with Llm Agents, by Gautier Dagan et al.

Plancraft: an evaluation dataset for planning with LLM agents

by Gautier Dagan, Frank Keller, Alex Lascarides

First submitted to arxiv on: 30 Dec 2024

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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 A novel multi-modal evaluation dataset for Large Language Model (LLM) agents is presented in this paper, which includes both text-only and multi-modal interfaces based on the Minecraft crafting GUI. The dataset, called Plancraft, evaluates tool use, Retrieval Augmented Generation (RAG), and decision-making through a realistic challenge that requires agents to decide whether tasks are solvable or not. Benchmarking open-source and closed-source LLMs and strategies on this task reveals that they struggle with the planning problems introduced by Plancraft, suggesting avenues for improvement.
Low	GrooveSquid.com (original content)	Low Difficulty Summary This paper creates a special dataset called Plancraft that helps test how well computer programs can make decisions and solve problems. The dataset uses the popular video game Minecraft to design a challenge that is very realistic. The goal is to see if these computer programs can figure out when they should give up trying to solve a problem, not just try everything. So far, the results show that these computer programs are not very good at this and need to be improved.