Where Did That Come From? Sentence-Level Error-Tolerant Attribution
Bibliographic record
Abstract
Attribution is the process of identifying which parts of the source support a generated output.While attribution can help users verify content and assess faithfulness, existing task definitions typically exclude unsupported or hallucinated content leaving them unattributed, overlooking the potential to increase faithfulness certainty, locate the error, and fix it easier.In this paper, we propose a new definition for sentence-level error-tolerant attribution, which extends attribution to include incorrect or hallucinated content.We introduce a benchmark for this task and evaluate a range of models on it.Our results show that sentence-level error-tolerant attribution improves the quality of both automatic and manual faithfulness evaluations, reducing annotation time by 30% in long-document settings, and facilitates hallucination fixing.We also find that unfaithful outputs are often linked to sentences that appear later in the source or contain non-literal language, pointing to promising avenues for hallucination mitigation.Our approach offers a better user experience along with improved faithfulness evaluation, with better understanding of model behavior.1
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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 | 0.006 | 0.053 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".