Summary of Rewriting Conversational Utterances with Instructed Large Language Models, by Elnara Galimzhanova et al.
Rewriting Conversational Utterances with Instructed Large Language Models
by Elnara Galimzhanova, Cristina Ioana Muntean, Franco Maria Nardini, Raffaele Perego, Guido Rocchietti
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Information Retrieval (cs.IR)
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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 investigates the ability of instructed large language models (LLMs) to improve conversational search effectiveness by rewriting user questions in a conversational setting. LLMs have shown state-of-the-art performance on various NLP tasks, including question answering, text summarization, coding, and translation. This study focuses on the capability of LLMs to perform zero-shot or few-shot prompting, which enables them to be trained using reinforcement learning with human feedback to follow user requests directly. The paper presents reproducible experiments conducted on publicly-available TREC CAST datasets, achieving significant improvements in metrics such as MRR, Precision@1, NDCG@3, and Recall@500 over state-of-the-art techniques. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study explores how instructed large language models can help make search results more relevant. These powerful AI systems are already good at tasks like answering questions and summarizing text. But what if we teach them to rewrite user queries in a way that makes it easier for them to find the right answers? The researchers tested this idea on special datasets designed for conversational search, and found that it made a big difference – up to 25% better results! |
Keywords
» Artificial intelligence » Few shot » Nlp » Precision » Prompting » Question answering » Recall » Reinforcement learning » Summarization » Translation » Zero shot
