Summary of Processtbench: An Llm Plan Generation Dataset For Process Mining, by Andrei Cosmin Redis et al.
ProcessTBench: An LLM Plan Generation Dataset for Process Mining
by Andrei Cosmin Redis, Mohammadreza Fani Sani, Bahram Zarrin, Andrea Burattin
First submitted to arxiv on: 13 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET)
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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 This paper presents a novel large-scale dataset called ProcessTBench, which aims to bridge the gap in evaluating Large Language Models (LLMs) for plan generation. The existing datasets lack complexity, hindering the evaluation of advanced tool use scenarios, such as handling paraphrased query statements, supporting multiple languages, and managing parallel actions. This new dataset enables researchers to study LLMs from a process perspective, examining typical behaviors and challenges in executing processes under different conditions or formulations. By leveraging this dataset, the paper aims to advance the capabilities of LLMs in real-world applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to teach an AI to generate plans for complex tasks, like writing code or solving puzzles. Current datasets are too simple and don’t cover important scenarios, such as understanding different languages or handling tricky questions. This paper introduces a new dataset called ProcessTBench that helps researchers test AI models in more realistic situations. The goal is to make these AI models better at generating plans for real-world tasks, which can improve things like language translation and problem-solving. |
Keywords
» Artificial intelligence » Translation
