Emotional RAG LLMs: Reading Comprehension for the Open Internet

Benjamin Reichman; Adar Avsian; Kartik Talamadupula; Toshish Jawale; Larry Heck

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arXiv · 2408.11189

Emotional RAG LLMs: Reading Comprehension for the Open Internet

Benjamin Reichman·Adar Avsian·Kartik Talamadupula·Toshish Jawale·Larry Heck

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Abstract

Queries to large language models (LLMs) can be divided into two parts: the instruction/question and the accompanying context. The context for retrieval-augmented generation (RAG) systems in most benchmarks comes from Wikipedia-like texts written in a neutral and factual tone. However, real-world RAG applications often retrieve internet-based text with diverse tones and linguistic styles, posing challenges for downstream tasks. This paper introduces (a) a dataset that transforms RAG-retrieved passages into emotionally inflected and sarcastic text, (b) an emotion translation model for adapting text to different tones, and (c) a prompt-based method to improve LLMs' pragmatic interpretation of retrieved text.

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Benjamin Reichman, Adar Avsian, Kartik Talamadupula, Toshish Jawale, Larry Heck. 2024-08-20. Emotional RAG LLMs: Reading Comprehension for the Open Internet. https://arxiv.org/abs/2408.11189

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