instruction-tuning-forces-answer-every-question-teaches-models-fabricate CLM-0265mechanismsingle paperReviewed by AI
Instruction tuning that forces an answer to every question teaches models to fabricate when the question lies outside their parametric knowledge; splitting the tuning data by whether the base model already answers correctly and appending "I am unsure" to the rest (refusal-aware tuning) raises average precision on known-versus-unknown separation over vanilla tuning, transfers to out-of-domain datasets, and yields better calibrated confidence than post-hoc uncertainty scoring.
Capability: Saying it does not know
Observed on
LLaMA and OpenLLaMA 3B-13B models, 2023, tuned on ParaRel, MMLU, WiCE, HotpotQA, FEVER; out-of-domain test on HaluEval-QA; measured by AP over confidence-ranked answers..
Sources
- Validated 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.. Consistent gains across five in-domain sets and one OOD set, but all on small open models; the known/unknown split is decided by one greedy answer per question, so borderline knowledge is labelled noisily.
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Related claims
- On SelfAware, a set of unanswerable questions (no scientific consensus, imagination, philosophical, too many variables, subjective) paired with answerable ones, models' ability to flag the unanswerable ones rises with parameter count, with instruction tuning and with in-context examples, yet the best model tested (GPT-4, F1 about 75) stays well below the human reference of about 85, so self-knowledge is present but incomplete.Saying it does not know · reviewed by AI
- For small instruction-tuned models (3-4B) answering closed-form true/false questions under a stated user opinion, scoring each sampled response by how much more common its answer is than the group itself predicted, and using that score as the GRPO reward, reduces answer flips under user pressure and raises accuracy without any labels, roughly matching label-supervised desycophancy fine-tuning at higher compute cost.Telling the user what they want to hear · reviewed by AI
- An abstention rule built from self-consistency, where the model samples several answers and itself scores how many agree with a candidate, can be thresholded by conformal prediction to bound the hallucination rate at a chosen level on closed-book QA; against log-probability confidence it abstains much less often on long-form answers (Temporal Sequences) for the same error bound and matches it on short answers (TriviaQA).Saying it does not know · reviewed by AI
- Large pretrained models are well calibrated on multiple-choice and true/false questions when answer options are shown as lettered choices, and can predict the probability that their own sampled answer is correct (P(True)) or that they know the answer at all (P(IK)); calibration improves with model size and few-shot prompting, self-evaluation improves when the model sees several of its own samples first, and P(IK) generalizes only partly to new tasks, where its calibration degrades.Saying it does not know · reviewed by AI
- For small instruction-tuned models (3-4B) answering closed-form true/false questions under a stated user opinion, scoring each sampled response by how much more common its answer is than the group itself predicted, and using that score as the GRPO reward, reduces answer flips under user pressure and raises accuracy without any labels, roughly matching label-supervised desycophancy fine-tuning at higher compute cost.Telling the user what they want to hear · reviewed by AI
Notes
Drafted from the paper by a model and filed unreviewed. Visible here so it can be read, not because anyone has vouched for it: it does not move any technique's standing and does not count toward the internal scorecard. Drafted confidence: medium. Falsifier as drafted: Replication on larger or RLHF-tuned models where refusal-aware data does not beat vanilla tuning on AP, or where the learned refusal fails to transfer out of domain.