Summary of A Novel Approach to Eliminating Hallucinations in Large Language Model-assisted Causal Discovery, by Grace Sng et al.
A Novel Approach to Eliminating Hallucinations in Large Language Model-Assisted Causal Discovery
by Grace Sng, Yanming Zhang, Klaus Mueller
First submitted to arxiv on: 16 Nov 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 paper presents the first comprehensive survey of popular large language models (LLMs) for causal discovery, highlighting the importance of optimal model selection due to the increasing use of LLMs as substitutes for human domain experts. The authors demonstrate that hallucinations exist when using LLMs in causal discovery and propose two methods to reduce these errors: Retrieval Augmented Generation (RAG) and a novel method employing multiple LLMs with an arbiter in a debate to audit edges in causal graphs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding the right language model for discovering causes. Right now, people are using big models like BERT and RoBERTa as substitutes for human experts, but they can make mistakes. The authors looked at these models and found that some of them have a problem called hallucination, where they make things up. They suggest two ways to fix this: one is called Retrieval Augmented Generation (RAG), and the other uses multiple models working together to check each other. |
Keywords
» Artificial intelligence » Bert » Hallucination » Language model » Rag » Retrieval augmented generation
