FalseCoTQA: Adversarial Multi-Hop QA via Knowledge-Grounded False Chains of Thought
Bibliographic record
Abstract
Multi-hop question answering (QA) models excel at decomposing complex queries into sequential reasoning steps, yet they remain vulnerable to subtly flawed inference chains that appear reasonable but are factually incorrect. To quantify and address this weakness, we present FalseCoTQA, an adversarial benchmark that injects knowledge-grounded false reasoning into retrieval-augmented contexts. Unlike prior methods that merely tweak surface text, FalseCoTQA leverages a domain-agnostic knowledge graph to systematically replace entities to construct semantically coherent yet incorrect chains of thought on top of standard multi-hop datasets (HotpotQA and MuSiQue). By evaluating state-of-the-art language models on this benchmark, we observe dramatic drops in answer accuracy, highlighting their tendency to follow deceptive reasoning without verifying factual consistency. We expect the proposed benchmark to contribute to the evaluation and improvement of the robustness and reliability of language models in multi-hop question answering.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".