Learning to Reduce: Optimal Representations of Structured Data in Prompting Large Language Models

arXiv:2402.14195v1 Announce Type: new
Abstract: Large Language Models (LLMs) have been widely used as general-purpose AI agents showing comparable performance on many downstream tasks. However, existing work shows that it is challenging for LLMs to integrate structured data (e.g. KG, tables, DBs) into their prompts; LLMs need to either understand long text data or select the most relevant evidence prior to inference, and both approaches are not trivial.
In this paper, we propose a framework, Learning to Reduce, that fine-tunes …

abstract agents ai agents arxiv cs.cl data dbs general language language models large language large language models llms performance prompting prompts reduce shows structured data tables tasks type work