Knowledge Tree
instruction tuning
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Survey and analysis of hallucinations in large language models frontiersin.org Sep 29, 2025 3 facts
procedureMitigation strategies for large language model hallucinations at the modeling level include Reinforcement Learning from Human Feedback (RLHF) (Ouyang et al., 2022), retrieval fusion (Lewis et al., 2020), and instruction tuning (Wang et al., 2022).
claimMistral-7B has a balanced profile where instruction tuning makes it responsive to prompts, but it requires well-structured prompts to perform optimally and shows improvement with Chain-of-Thought and few-shot cues.
referenceInstruction tuning and reinforcement learning from human feedback (RLHF) improve prompt responsiveness but do not eliminate deep-seated model hallucinations, as noted by Ouyang et al. (2022) and Kadavath et al. (2022).
Hallucination Causes: Why Language Models Fabricate Facts mbrenndoerfer.com Mar 15, 2026 3 facts
claimHuman annotators rating large language model responses during instruction tuning and RLHF tend to prefer responses that sound knowledgeable and direct over responses that sound uncertain and hedged.
claimInstruction-tuning can teach large language models to express uncertainty with phrases like 'I'm not certain,' but this is learned as a surface pattern rather than a calibrated epistemic state.
claimInstruction tuning and reinforcement learning from human feedback (RLHF) improve a large language model's ability to express uncertainty and abstain from answering when knowledge is insufficient, but they do not retroactively fill knowledge gaps or undo exposure bias present in the base model.
Building Trustworthy NeuroSymbolic AI Systems - arXiv arxiv.org 2 facts
claimInstruction Tuning is a method used to align Large Language Models (LLMs) with human expectations, though it requires a substantial amount of training samples and currently lacks a perfect quantifiable method to measure the 'instruction following' nature of the models.
referenceLongpre et al. (2023) authored 'The flan collection: Designing data and methods for effective instruction tuning', published as an arXiv preprint (arXiv:2301.13688).
Medical Hallucination in Foundation Models and Their ... medrxiv.org Mar 3, 2025 2 facts
procedureResearchers adapt LLMs for medicine using domain-specific corpora, instruction tuning, and retrieval-augmented generation (RAG) to align outputs with clinical practice, as described by Wei et al. (2022) and Lewis et al. (2020).
referenceA survey by Nazi and Peng (2024) provides a comprehensive review of LLMs in healthcare, highlighting that domain-specific adaptations like instruction tuning and retrieval-augmented generation can enhance patient outcomes and streamline medical knowledge dissemination, while noting persistent challenges regarding reliability, interpretability, and hallucination risk.
Bridging the Gap Between LLMs and Evolving Medical Knowledge arxiv.org Jun 29, 2025 1 fact
referenceRohanian et al. (2024) published 'Exploring the effectiveness of instruction tuning in biomedical language processing' in Artificial intelligence in medicine, volume 158, article 103007.
The Synergy of Symbolic and Connectionist AI in LLM-Empowered ... arxiv.org Jul 11, 2024 1 fact
claimInstruction tuning and reinforcement learning from human feedback (RLHF) are proposed methods applied on top of fine-tuning to ensure Large Language Models follow human instructions, align with human values, and exhibit desired behaviors.
The construction and refined extraction techniques of knowledge ... nature.com Feb 10, 2026 1 fact
referenceThe GPT4Tool framework connects large language models with massive tools via instruction tuning, published in the ACL 2023 proceedings.
Medical Hallucination in Foundation Models and Their Impact on ... medrxiv.org Nov 2, 2025 1 fact
claimDomain-specific adaptations like instruction tuning and retrieval-augmented generation can improve patient outcomes and streamline medical knowledge dissemination, though they face persistent challenges regarding reliability, interpretability, and hallucination risk.
A Survey on the Theory and Mechanism of Large Language Models arxiv.org Mar 12, 2026 1 fact
claimWei et al. (2023) found that instruction tuning in large language models notably enhanced the utilization of semantic priors compared to learning input-label mappings from contextual demonstrations.
Practices, opportunities and challenges in the fusion of knowledge ... frontiersin.org 1 fact
referenceCurrent research addresses the gap between temporal knowledge graphs and large language models through retrieval-augmented generation frameworks, such as GenTKG (Liao et al., 2024), and by integrating few-shot learning and instruction tuning to reduce computational costs.