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Relations (1)
related 3.17 — strongly supporting 8 facts
Instruction tuning is a critical methodology applied to Large Language Models to align their outputs with human instructions and expectations, as evidenced by [1], [2], and [3]. Furthermore, it is used to enhance model capabilities in specific domains and tasks, such as tool usage and medical applications, as described in [4], [5], and [6].
Facts (8)
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Medical Hallucination in Foundation Models and Their ... medrxiv.org 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.
Building Trustworthy NeuroSymbolic AI Systems - arXiv arxiv.org 1 fact
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.
Survey and analysis of hallucinations in large language models frontiersin.org 1 fact
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).
The Synergy of Symbolic and Connectionist AI in LLM-Empowered ... arxiv.org 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 1 fact
referenceThe GPT4Tool framework connects large language models with massive tools via instruction tuning, published in the ACL 2023 proceedings.
Hallucination Causes: Why Language Models Fabricate Facts mbrenndoerfer.com 1 fact
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.
A Survey on the Theory and Mechanism of Large Language Models arxiv.org 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.