A Novel Approach to Eliminating Hallucinations in Large Language Model-Assisted Causal Discovery
The increasing use of large language models (LLMs) in causal discovery as a substitute for human domain experts highlights the need for optimal model selection. This paper presents the first hallucination survey of popular LLMs for causal discovery. We show that hallucinations exist when using LLMs in causal discovery so the choice of LLM is important. We propose using Retrieval Augmented Generation (RAG) to reduce hallucinations when quality data is available. Additionally, we introduce a novel method employing multiple LLMs with an arbiter in a debate to audit edges in causal graphs, achieving a comparable reduction in hallucinations to RAG.
Code (0)
등록된 구현이 없습니다.
Tasks
Causal DiscoveryHallucinationLanguage ModelingLanguage ModellingLarge Language ModelModel SelectionRAGRetrievalRetrieval-augmented GenerationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Exploring Causal Effect of Social Bias on Faithfulness Hallucinations in Large Language Models
Large language models (LLMs) have achieved remarkable success in various tasks, yet they remain vulnerable to faithfulness hallucinations, where the output does not align with the input. In this study, we investigate whe…
Causal-LLaVA: Causal Disentanglement for Mitigating Hallucination in Multimodal Large Language Models
Multimodal Large Language Models (MLLMs) have demonstrated strong performance in visual understanding tasks, yet they often suffer from object hallucinations--generating descriptions of objects that are inconsistent with…
DisentanglementHallucinationLanguage ModelingLanguage Modelling+1CausalGuard: A Smart System for Detecting and Preventing False Information in Large Language Models
While large language models have transformed how we interact with AI systems, they have a critical weakness: they confidently state false information that sounds entirely plausible. This "hallucination" problem has becom…
Medical DiagnosisCOPO: Causal-Oriented Policy Optimization for Hallucinations of MLLMs
Despite Multimodal Large Language Models (MLLMs) having shown impressive capabilities, they may suffer from hallucinations. Empirically, we find that MLLMs attend disproportionately to task-irrelevant background regions …
Mitigating Hallucinations in Large Language Models via Causal Reasoning
Large language models (LLMs) exhibit logically inconsistent hallucinations that appear coherent yet violate reasoning principles, with recent research suggesting an inverse relationship between causal reasoning capabilit…