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Found 6 papers, 1 papers with code
Memory-Aware and Uncertainty-Guided Retrieval for Multi-Hop Question Answering
Multi-hop question answering (QA) requires models to retrieve and reason over multiple pieces of evidence.
Mitigating the Risk of Health Inequity Exacerbated by Large Language Models
Recent advancements in large language models have demonstrated their potential in numerous medical applications, particularly in automating clinical trial matching for translational research and enhancing medical question answering for clinical decision support.
ReasoningRank: Teaching Student Models to Rank through Reasoning-Based Knowledge Distillation
Reranking documents based on their relevance to a given query is a critical task in information retrieval.
Learning from Committee: Reasoning Distillation from a Mixture of Teachers with Peer-Review
While reasoning capabilities typically emerge in large language models (LLMs) with tens of billions of parameters, recent research focuses on improving smaller open-source models through knowledge distillation (KD) from commercial LLMs.
RAG-RLRC-LaySum at BioLaySumm: Integrating Retrieval-Augmented Generation and Readability Control for Layman Summarization of Biomedical Texts
This paper introduces the RAG-RLRC-LaySum framework, designed to make complex biomedical research understandable to laymen through advanced Natural Language Processing (NLP) techniques.
Effects of Different Prompts on the Quality of GPT-4 Responses to Dementia Care Questions
Evidence suggests that different prompts lead large language models (LLMs) to generate responses with varying quality.
