Summary of Learning When to Retrieve, What to Rewrite, and How to Respond in Conversational Qa, by Nirmal Roy et al.
Learning When to Retrieve, What to Rewrite, and How to Respond in Conversational QA
by Nirmal Roy, Leonardo F. R. Ribeiro, Rexhina Blloshmi, Kevin Small
First submitted to arxiv on: 23 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 proposes a method for augmenting Large Language Models (LLMs) with information retrieval capabilities to improve knowledge-intensive tasks, particularly in conversational question answering (QA). The approach, called Retrieval-Augmented Generation (RAG), is beneficial for understanding users’ contextual search intent and generating responses. However, this extension requires LLMs to comprehend conversational context and manage retrieved passages over multiple turns, making it more challenging than single-turn QA. The proposed method, SELF-multi-RAG, builds upon the single-turn SELF-RAG framework (Asai et al., 2023) and enables LLMs to decide when to retrieve in RAG settings given a conversational context. When retrieval is necessary, the LLM rewrites the conversation for passage retrieval and judges the relevance of returned passages before response generation. The authors demonstrate improved capabilities over single-turn variants with respect to retrieving relevant passages (by using summarized conversational context) and assessing the quality of generated responses. Experiments on three conversational QA datasets validate the enhanced response generation capabilities of SELF-multi-RAG, with improvements of ~13% measured by human annotation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, researchers developed a new method for improving the way language models answer questions in a conversation. They added information retrieval skills to large language models (LLMs) to help them understand what people are searching for when they ask a question. This makes it easier for LLMs to provide accurate and helpful responses. The method works by allowing the LLM to decide when to look up more information to answer a question, rather than just relying on its own knowledge. When necessary, the LLM will summarize the conversation so far and use that to find relevant passages of text to help answer the question. It then judges how good those passages are before generating an actual response. The researchers tested their method on three different datasets and found that it improved the quality of responses generated by language models by about 13%. This could have important implications for applications like virtual assistants and chatbots, which rely on language models to understand and respond to user queries. |
Keywords
» Artificial intelligence » Question answering » Rag » Retrieval augmented generation
