Faithful to the Document or to the World? Mitigating Hallucinations via Entity-Linked Knowledge in Abstractive Summarization
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Existing abstractive summarization systems are hampered by content hallucinations in which models generate text that is not directly inferable from the source alone. Annotations from prior work have shown that some of these hallucinations, while being 'unfaithful' to the source, are nonetheless factual. Our analysis in this paper suggests that these factual hallucinations occur as a result of the prevalence of factual yet unfaithful entities in summarization datasets. We find that these entities are not aberrations, but instead examples of additional world knowledge being readily used to latently connect entities and concepts – in this case connecting entities in the source document to those in the target summary. In our analysis and experiments, we demonstrate that connecting entities to an external knowledge base can lend provenance to many of these unfaithful yet factual entities, and further, this knowledge can be used to improve the factuality of summaries without simply making them more extractive.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it