Summary of Beyond Retrieval: Generating Narratives in Conversational Recommender Systems, by Krishna Sayana et al.
Beyond Retrieval: Generating Narratives in Conversational Recommender Systems
by Krishna Sayana, Raghavendra Vasudeva, Yuri Vasilevski, Kun Su, Liam Hebert, James Pine, Hubert Pham, Ambarish Jash, Sukhdeep Sodhi
First submitted to arxiv on: 22 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Machine Learning (cs.LG)
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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 recent advances in Large Language Model’s (LLM) generation and reasoning capabilities present an opportunity to develop truly conversational recommendation systems, but effectively integrating recommender system knowledge into LLMs for natural language generation tailored towards recommendation tasks remains a challenge. This paper addresses this challenge by making two key contributions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models can generate and reason about conversations, but it’s hard to integrate them with recommender systems to make natural language recommendations. The paper solves this problem by making two important contributions. |
Keywords
» Artificial intelligence » Large language model
