Tool Calling: Enhancing Medication Consultation via Retrieval-Augmented Large Language Models

• URL: http://arxiv.org/abs/2404.17897v1
• Date: Sat, 27 Apr 2024 13:11:42 GMT
• Title: Tool Calling: Enhancing Medication Consultation via Retrieval-Augmented Large Language Models
• Authors: Zhongzhen Huang, Kui Xue, Yongqi Fan, Linjie Mu, Ruoyu Liu, Tong Ruan, Shaoting Zhang, Xiaofan Zhang,
• Abstract summary: Large-scale language models (LLMs) have achieved remarkable success across various language tasks but suffer from hallucinations and temporal misalignment. We propose a new textitDistill-Retrieve-Read framework instead of the previous textitRetrieve-then-Read.
• Score: 10.04914417538886
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large-scale language models (LLMs) have achieved remarkable success across various language tasks but suffer from hallucinations and temporal misalignment. To mitigate these shortcomings, Retrieval-augmented generation (RAG) has been utilized to provide external knowledge to facilitate the answer generation. However, applying such models to the medical domain faces several challenges due to the lack of domain-specific knowledge and the intricacy of real-world scenarios. In this study, we explore LLMs with RAG framework for knowledge-intensive tasks in the medical field. To evaluate the capabilities of LLMs, we introduce MedicineQA, a multi-round dialogue benchmark that simulates the real-world medication consultation scenario and requires LLMs to answer with retrieved evidence from the medicine database. MedicineQA contains 300 multi-round question-answering pairs, each embedded within a detailed dialogue history, highlighting the challenge posed by this knowledge-intensive task to current LLMs. We further propose a new \textit{Distill-Retrieve-Read} framework instead of the previous \textit{Retrieve-then-Read}. Specifically, the distillation and retrieval process utilizes a tool calling mechanism to formulate search queries that emulate the keyword-based inquiries used by search engines. With experimental results, we show that our framework brings notable performance improvements and surpasses the previous counterparts in the evidence retrieval process in terms of evidence retrieval accuracy. This advancement sheds light on applying RAG to the medical domain.

Related papers

• Improving Retrieval-Augmented Generation in Medicine with Iterative Follow-up Questions [42.73799041840482]
  i-MedRAG is a system that iteratively asks follow-up queries based on previous information-seeking attempts. Our zero-shot i-MedRAG outperforms all existing prompt engineering and fine-tuning methods on GPT-3.5. i-MedRAG can flexibly ask follow-up queries to form reasoning chains, providing an in-depth analysis of medical questions.
  arXiv  Detail & Related papers  (2024-08-01T17:18:17Z)
• Retrieval-Enhanced Machine Learning: Synthesis and Opportunities [60.34182805429511]
  Retrieval-enhancement can be extended to a broader spectrum of machine learning (ML) This work introduces a formal framework of this paradigm, Retrieval-Enhanced Machine Learning (REML), by synthesizing the literature in various domains in ML with consistent notations which is missing from the current literature. The goal of this work is to equip researchers across various disciplines with a comprehensive, formally structured framework of retrieval-enhanced models, thereby fostering interdisciplinary future research.
  arXiv  Detail & Related papers  (2024-07-17T20:01:21Z)
• SeRTS: Self-Rewarding Tree Search for Biomedical Retrieval-Augmented Generation [50.26966969163348]
  Large Language Models (LLMs) have shown great potential in the biomedical domain with the advancement of retrieval-augmented generation (RAG) Existing retrieval-augmented approaches face challenges in addressing diverse queries and documents, particularly for medical knowledge queries. We propose Self-Rewarding Tree Search (SeRTS) based on Monte Carlo Tree Search (MCTS) and a self-rewarding paradigm.
  arXiv  Detail & Related papers  (2024-06-17T06:48:31Z)
• OLAPH: Improving Factuality in Biomedical Long-form Question Answering [15.585833125854418]
  We introduce MedLFQA, a benchmark dataset reconstructed using long-form question-answering datasets related to the biomedical domain. We also propose OLAPH, a simple and novel framework that utilizes cost-effective and multifaceted automatic evaluation. Our findings reveal that a 7B LLM trained with our OLAPH framework can provide long answers comparable to the medical experts' answers in terms of factuality.
  arXiv  Detail & Related papers  (2024-05-21T11:50:16Z)
• Mitigating Hallucinations in Large Language Models via Self-Refinement-Enhanced Knowledge Retrieval [14.58181631462891]
  Large language models (LLMs) have demonstrated remarkable capabilities across various domains. Their susceptibility to hallucination poses significant challenges for their deployment in critical areas such as healthcare. We propose Self-Refinement-Enhanced Knowledge Graph Retrieval (Re-KGR) to augment the factuality of LLMs' responses.
  arXiv  Detail & Related papers  (2024-05-10T15:40:50Z)
• AI Hospital: Benchmarking Large Language Models in a Multi-agent Medical Interaction Simulator [69.51568871044454]
  We introduce textbfAI Hospital, a framework simulating dynamic medical interactions between emphDoctor as player and NPCs. This setup allows for realistic assessments of LLMs in clinical scenarios. We develop the Multi-View Medical Evaluation benchmark, utilizing high-quality Chinese medical records and NPCs.
  arXiv  Detail & Related papers  (2024-02-15T06:46:48Z)
• MKRAG: Medical Knowledge Retrieval Augmented Generation for Medical Question Answering [45.84961106102445]
  Large Language Models (LLMs) often perform poorly on domain-specific tasks such as medical question answering (QA) We propose a comprehensive retrieval strategy to extract medical facts from an external knowledge base, and then inject them into the LLM's query prompt. Our retrieval-augmented Vicuna-7B model exhibited an accuracy improvement from 44.46% to 48.54%.
  arXiv  Detail & Related papers  (2023-09-27T21:26:03Z)
• Retrieval Augmented Generation and Representative Vector Summarization for large unstructured textual data in Medical Education [0.0]
  Retrieval Augmented Generation (RAG) allows to easily attach and manipulate a non-parametric knowledgebases to Large Language Models. A combined extractive and abstractive summarization method for large unstructured textual data using representative vectors is proposed.
  arXiv  Detail & Related papers  (2023-08-01T12:04:50Z)
• Synergistic Interplay between Search and Large Language Models for Information Retrieval [141.18083677333848]
  InteR allows RMs to expand knowledge in queries using LLM-generated knowledge collections. InteR achieves overall superior zero-shot retrieval performance compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-05-12T11:58:15Z)
• An Iterative Optimizing Framework for Radiology Report Summarization with ChatGPT [80.33783969507458]
  The 'Impression' section of a radiology report is a critical basis for communication between radiologists and other physicians. Recent studies have achieved promising results in automatic impression generation using large-scale medical text data. These models often require substantial amounts of medical text data and have poor generalization performance.
  arXiv  Detail & Related papers  (2023-04-17T17:13:42Z)
• Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback [127.75419038610455]
  Large language models (LLMs) are able to generate human-like, fluent responses for many downstream tasks. This paper proposes a LLM-Augmenter system, which augments a black-box LLM with a set of plug-and-play modules.
  arXiv  Detail & Related papers  (2023-02-24T18:48:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.