concept

Domain-Specific Knowledge

Facts (11)

Sources

LLM Hallucinations: Causes, Consequences, Prevention - LLMs llmmodels.org llmmodels.org May 10, 2024 2 facts

claimLarge language models (LLMs) experience hallucinations due to knowledge gaps and a lack of context awareness, specifically struggling with domain-specific knowledge or understanding context.

claimLarge language models may hallucinate when they assume a level of domain-specific knowledge or cultural context that is not universally shared.

MedHallu: Benchmark for Medical LLM Hallucination Detection emergentmind.com Emergent Mind Feb 20, 2025 1 fact

measurementProviding domain-specific knowledge enhances hallucination detection performance across both general-purpose and medical fine-tuned LLMs, with some general models seeing up to a 32% improvement in F1 scores.

Enhancing LLMs with Knowledge Graphs: A Case Study - LinkedIn linkedin.com LinkedIn Nov 7, 2023 1 fact

claimIntegrating Large Language Models with enterprise data and domain-specific knowledge reduces the risk of hallucination in the model's output.

A survey on augmenting knowledge graphs (KGs) with large ... link.springer.com Springer Nov 4, 2024 1 fact

claimLarge Language Models (LLMs) trained on generic corpora may not efficiently generalize domain-specific or novel knowledge.

[2502.14302] MedHallu: A Comprehensive Benchmark for Detecting ... arxiv.org arXiv Feb 20, 2025 1 fact

claimIncorporating domain-specific knowledge and introducing a 'not sure' category as one of the answer categories improves precision and F1 scores by up to 38% relative to baselines in the MedHallu benchmark.

A Comprehensive Benchmark for Detecting Medical Hallucinations ... aclanthology.org Shrey Pandit, Jiawei Xu, Junyuan Hong, Zhangyang Wang, Tianlong Chen, Kaidi Xu, Ying Ding · ACL Anthology 1 fact

claimIncorporating domain-specific knowledge and introducing a 'not sure' category as an answer option improves precision and F1 scores by up to 38% relative to baselines in medical hallucination detection.

[Literature Review] MedHallu: A Comprehensive Benchmark for ... themoonlight.io The Moonlight 1 fact

claimIncorporating domain-specific knowledge and adding a 'not sure' response category significantly improves detection accuracy in large language models by allowing them to abstain from uncertain answers.

Efficient Knowledge Graph Construction and Retrieval from ... - arXiv arxiv.org arXiv Aug 7, 2025 1 fact

claimIn enterprise settings, Retrieval-Augmented Generation (RAG) allows organizations to integrate proprietary data so that generated responses align with the latest domain-specific knowledge.

LLM-KG4QA: Large Language Models and Knowledge Graphs for ... github.com GitHub 1 fact

referenceThe paper titled 'Injecting Domain-Specific Knowledge into Large Language Models: A Comprehensive Survey' was published on arXiv in 2025.

Medical Hallucination in Foundation Models and Their ... medrxiv.org medRxiv Mar 3, 2025 1 fact

measurementSurvey respondents identified lack of domain-specific knowledge (30 mentions) as the most critical limitation of AI/LLMs, followed by privacy and data security concerns (25), accuracy issues (24), lack of standardization/validation of AI tools (23), difficulty in explaining AI decisions (21), and ethical considerations (20).