questions-whose-premises-false-or-internally-contradictory-prompting-mid-size CLM-0124On questions whose premises are false or internally contradictory, prompting mid-size instruct models (Qwen2.5-7B, LLaMA-3.1-8B, Gemma3-12B) to reason step by step can lower accuracy below plain answering, because the reasoning chain elaborates from the flawed premise instead of challenging it.
Evidence for: Thomas theorem (holds)
Capability: Stating false facts confidently
Observed on
Mid-size open instruct models, short-form QA with planted false premises or contradictions (TruthfulQA multiple-choice, FalseQA, the authors' MisFactQA); not tested on premise-clea.
Sources
- Measured on three open instruct models across three datasets against a default-prompt baseline; CoT fell below Original on TruthfulQA for all three models and on FalseQA for LLaMA and Gemma, and also below Original for GPT-4o-mini and DeepSeek-V3. Judged by an o3-mini automated judge with reported human agreement. Single paper; the causal story (reasoning amplifies the accepted premise) is argued from case studies rather than isolated experimentally.
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Related claims
- On factual QA (817-item TruthfulQA multiple-choice, plus a 300-item Natural Questions short-answer set), a prompt-only three-stage gate in which the model lists the information units a question needs, scores its support for each, and answers only when the mean score clears a threshold lowers the unconditional wrong-answer rate and raises accuracy on answered items for all eleven models tested, but most of the abstentions it produces fall on questions plain chain-of-thought answered correctly (about 69% at the primary operating point), so the gain is bought with lost coverage rather than with selective self-knowledge.Saying it does not know · reviewed by AI
- When deception in narrative text is analysed by instantiating fixed argumentation schemes with an LLM (premise extraction, then critical-question evaluation), the scheme assessments are stable across ten different LLM backbones and repeated runs for clear-cut deception cases but grow inconsistent on ambiguous intelligence-style narratives.Strategic deception and detecting it · reviewed by AI
- On natural-language logical reasoning benchmarks, auto-formalizing each reasoning step into SMT-LIB and regenerating from the first step the solver cannot verify raises both the share of solver-verified steps and final-answer accuracy over plain chain-of-thought, and greedy regeneration at the failure point beats beam search or MCTS on step verifiability.Carrying a chain of inference through to its conclusion · reviewed by AI
- When an LLM answers a question and gives an explanation, removing from the input the concepts its explanation did not credit and re-querying on the reduced input raises measured explanation faithfulness over both standard prompting and prompts that ask for faithfulness, because the removal deletes the unmentioned influences rather than asking the model to report them.Saying what actually drove the answer · reviewed by AI
- When training small instruction-tuned models with reinforcement learning plus factual supervision, routing each atomic fact's verification score only to the tokens that produced it, and down-weighting verifier judgements that do not change when their key evidence is removed, improves factuality benchmark scores over trajectory-level or reasoning- step-level factual rewards.Checking claims against evidence · reviewed by AI
Notes
Ingested unreviewed on 2026-09-07 and deliberately inert until a human endorses it: it does not move a technique standing, does not count toward the backtest, and is excluded anywhere a claim would carry weight. Drafted confidence: medium. Falsifier as drafted: On false- premise question sets, CoT prompting matching or beating direct answering across model families, with the drop not reproducing. Proposed technique, not yet catalogued: Verify input premises before answering.