Not Lost After All: How Cross-Encoder Attribution Challenges Position Bias Assumptions in LLM Summarization
Bibliographic record
Abstract
Position bias, the tendency of Large Language Models (LLMs) to select content based on its structural position in a document rather than its semantic relevance, has been viewed as a key limitation in automatic summarization.To measure position bias, prior studies rely heavily on n-gram matching techniques, which fail to capture semantic relationships in abstractive summaries where content is extensively rephrased.To address this limitation, we apply a crossencoder-based alignment method that jointly processes summary-source sentence pairs, enabling more accurate identification of semantic correspondences even when summaries substantially rewrite the source.Experiments with five LLMs across six summarization datasets reveal significantly different position bias patterns than those reported by traditional metrics.Our findings suggest that these patterns primarily reflect rational adaptations to document structure and content rather than true model limitations.Through controlled experiments and analyses across varying document lengths and multi-document settings, we show that LLMs use content from all positions more effectively than previously assumed, challenging common claims about "lost-in-the-middle" behaviour.
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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 | 0.013 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".