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Found 3 papers, 1 papers with code
Graph-Guided Reasoning for Multi-Hop Question Answering in Large Language Models
Specifically, we first leverage LLMs to construct a "question/rationale graph" by using knowledge extraction prompting given the initial question and the rationales generated in the previous steps.
Integrating Summarization and Retrieval for Enhanced Personalization via Large Language Models
To overcome these limitations, we propose a novel summary-augmented approach by extending retrieval-augmented personalization with task-aware user summaries generated by LLMs.
Learning to Retrieve Engaging Follow-Up Queries
The retrieval system is trained on a dataset which contains ~14K multi-turn information-seeking conversations with a valid follow-up question and a set of invalid candidates.
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