Elahe Rahimi

Not Lost After All: How Cross-Encoder Attribution Challenges Position Bias Assumptions in LLM Summarization

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 tec