Summary of Halo: Hallucination Analysis and Learning Optimization to Empower Llms with Retrieval-augmented Context For Guided Clinical Decision Making, by Sumera Anjum et al.
HALO: Hallucination Analysis and Learning Optimization to Empower LLMs with Retrieval-Augmented Context for Guided Clinical Decision Making
by Sumera Anjum, Hanzhi Zhang, Wenjun Zhou, Eun Jin Paek, Xiaopeng Zhao, Yunhe Feng
First submitted to arxiv on: 16 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposed framework, called HALO, targets the detection and mitigation of hallucinations in medical question-answering (QA) systems. Hallucinations occur when large language models (LLMs) generate inaccurate or unreliable responses, which can be detrimental in critical domains like health and medicine. To address this issue, HALO generates multiple query variations using LLMs and retrieves relevant information from external open knowledge bases to enrich the context. The framework utilizes maximum marginal relevance scoring to prioritize the retrieved context, providing it to LLMs for answer generation, thereby reducing the risk of hallucinations. The integration of LangChain streamlines this process, resulting in notable increases in accuracy for both open-source and commercial LLMs, such as Llama-3.1 (44% to 65%) and ChatGPT (56% to 70%). This framework highlights the importance of addressing hallucinations in medical QA systems, ultimately improving clinical decision-making and patient care. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to make sure medical question-answering systems give accurate answers. Sometimes these systems can provide wrong information, which is very important because it affects people’s health. The solution is called HALO and it uses special techniques to reduce the risk of giving bad answers. It works by using different versions of a question and then finding relevant information from other sources. This makes sure the answer is accurate and reliable. The system even improves existing language models, like Llama-3.1 and ChatGPT. |
Keywords
» Artificial intelligence » Llama » Question answering
