refusal-aware-instruction-tuning TEC-0058 · proposedRefusal-aware instruction tuning
Run the base model on the tuning set, label each example as known or unknown by whether it answered correctly, keep answers for the known set and append an uncertainty expression to the unknown set, then fine-tune on the union so the model
Proposed. Filed by the drafting stage because a paper introduces or tests it. Nobody has vouched for it. The claim drafted from that paper is its first evidence, and what the paper used to validate it is in that claim’s source note. Say whether it held up for you, below.
Run the base model on the tuning set, label each example as known or unknown by whether it answered correctly, keep answers for the known set and append an uncertainty expression to the unknown set, then fine-tune on the union so the model Filed by the drafting stage from R-Tuning: Instructing Large Language Models to Say `I Don't Know' (2311.09677) with status proposed: the paper introduces or tests it, and the claim drafted from the paper is its first evidence. The paper validated it by: AP scores in single-task and multi-task settings against vanilla instruction tuning on the same data, plus out-of-domain AP on HaluEval-QA and a comparison against uncertainty-based filtering at test time; 3B-13B open models only.. Nobody has vouched for it; add support or contest it on this page.
Does it work?
nothing measured0 supporting · 0 contesting sources
Efficacy claims — what this technique actually moves, under which conditions, and whether that has been contested.
No efficacy claim filed yet. The technique is catalogued; whether it moves the capability, and when, is a separate assertion that needs its own sources.
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Code
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Sources
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