Summary of Llm Robustness Against Misinformation in Biomedical Question Answering, by Alexander Bondarenko et al.
LLM Robustness Against Misinformation in Biomedical Question Answering
by Alexander Bondarenko, Adrian Viehweger
First submitted to arxiv on: 27 Oct 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 A new approach called Retrieval-Augmented Generation (RAG) helps reduce the likelihood of large language models (LLMs) providing inaccurate answers to questions. By incorporating external knowledge sources into the prompt, RAG aims to provide more accurate and informed responses. However, this method also risks introducing incorrect information that can lead LLMs to generate misleading answers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary To help large language models answer questions more accurately, researchers have developed a new approach called Retrieval-Augmented Generation (RAG). This approach adds context from external knowledge sources to the prompt, helping the model provide better responses. However, it’s important to ensure that the added information is correct, or it could actually make things worse. |
Keywords
» Artificial intelligence » Likelihood » Prompt » Rag » Retrieval augmented generation
