The performance of Large Language Models (LLMs) is significantly sensitive to the contextual position of information in the input. To investigate the mechanism behind this positional bias, our extensive experiments reveal a consistent phenomenon we term the attention basin: when presented with a sequence of structured items (e.g., retrieved documents or few-shot examples), models systematically assign higher attention to the items at the beginning and end of the sequence, while neglecting those in the middle. Crucially, our analysis further reveals that allocating higher attention to critical information is key to enhancing model performance. Based on these insights, we introduce Attention-Driven Reranking (AttnRank), a two-stage framework that (i) estimates a model’s intrinsic positional attention preferences using a small calibration set, and (ii) reorders retrieved documents or few-shot examples to align the most salient content with these high-attention positions. AttnRank is a model-agnostic, training-free, and plug-and-play method with minimal computational overhead. Experiments on multi-hop QA and few-shot in-context learning tasks demonstrate that AttnRank achieves substantial improvements across 10 large language models of varying architectures and scales, without modifying model parameters or training procedures.

Despite significant progress in abstractive text summarization aimed at generating fluent and informative outputs, how to ensure the factual consistency of generated summaries remains a crucial and challenging issue. In this study, drawing inspiration from advancements in causal inference, we construct causal graphs to analyze the process of abstractive text summarization methods and identify intrinsic causes of factual inconsistency, specifically language bias and irrelevancy bias, and we propose CoFactSum, a novel framework that mitigates the causal effects of these biases through counterfactual estimation for enhancing the factual consistency of the generated content. CoFactSum provides two counterfactual estimation strategies, including Explicit Counterfactual Masking, which employs a dynamic masking approach, and Implicit Counterfactual Training, which utilizes a discriminative cross-attention mechanism. Besides, we propose a Debiasing Degree Adjustment mechanism to dynamically calibrate the level of debiasing at each decoding step. Extensive experiments conducted on two widely used summarization datasets demonstrate the effectiveness and advantages of the proposed CoFactSum in enhancing the factual consistency of generated summaries, outperforming several baseline methods.