Attention Basin: Why Contextual Position Matters in Large Language Models

• URL: http://arxiv.org/abs/2508.05128v1
• Date: Thu, 07 Aug 2025 08:08:08 GMT
• Title: Attention Basin: Why Contextual Position Matters in Large Language Models
• Authors: Zihao Yi, Delong Zeng, Zhenqing Ling, Haohao Luo, Zhe Xu, Wei Liu, Jian Luan, Wanxia Cao, Ying Shen,
• Abstract summary: We show that models systematically assign higher attention to items at the beginning and end of a sequence, while neglecting those in the middle.<n>We introduce Attention-Driven Reranking (AttnRank), a framework that estimates a model's intrinsic positional attention preferences.<n>AttnRank is a model-agnostic, training-free, and plug-and-play method with minimal computational overhead.
• Score: 16.11590856103274
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Mechanistic Interpretability of GPT-like Models on Summarization Tasks [2.4022340214033915]
  This paper presents an interpretability framework for analyzing how GPT-like models adapt to summarization tasks.<n>By identifying specific layers and attention heads that undergo significant transformation, we locate the "summarization circuit" within the model architecture.
  arXiv  Detail & Related papers  (2025-05-20T02:15:11Z)
• Enhancing Unsupervised Graph Few-shot Learning via Set Functions and Optimal Transport [23.36436403062214]
  Recent advancements in graph few-shot learning models have exhibited superior performance across diverse applications.<n>We propose a novel model named STAR, which enhances unsupervised graph few-shot learning.
  arXiv  Detail & Related papers  (2025-01-10T00:42:27Z)
• VSFormer: Value and Shape-Aware Transformer with Prior-Enhanced Self-Attention for Multivariate Time Series Classification [47.92529531621406]
  We propose a novel method, VSFormer, that incorporates both discriminative patterns (shape) and numerical information (value)<n>In addition, we extract class-specific prior information derived from supervised information to enrich the positional encoding.<n>Extensive experiments on all 30 UEA archived datasets demonstrate the superior performance of our method compared to SOTA models.
  arXiv  Detail & Related papers  (2024-12-21T07:31:22Z)
• Corpus Considerations for Annotator Modeling and Scaling [9.263562546969695]
  We show that the commonly used user token model consistently outperforms more complex models. Our findings shed light on the relationship between corpus statistics and annotator modeling performance.
  arXiv  Detail & Related papers  (2024-04-02T22:27:24Z)
• Teaching Smaller Language Models To Generalise To Unseen Compositional Questions [6.9076450524134145]
  We propose a combination of multitask pretraining on up to 93 tasks designed to instill diverse reasoning abilities. We show that performance can be significantly improved by adding retrieval-augmented training datasets.
  arXiv  Detail & Related papers  (2023-08-02T05:00:12Z)
• Do prompt positions really matter? [2.6379212441113213]
  Our findings quantify the substantial impact prompt position has on model performance. We observe that the prompt positions used in prior studies are often sub-optimal, and this observation is consistent even in widely used instruction-tuned models.
  arXiv  Detail & Related papers  (2023-05-23T19:45:45Z)
• Discover, Explanation, Improvement: An Automatic Slice Detection Framework for Natural Language Processing [72.14557106085284]
  slice detection models (SDM) automatically identify underperforming groups of datapoints. This paper proposes a benchmark named "Discover, Explain, improve (DEIM)" for classification NLP tasks. Our evaluation shows that Edisa can accurately select error-prone datapoints with informative semantic features.
  arXiv  Detail & Related papers  (2022-11-08T19:00:00Z)
• Generalization Properties of Retrieval-based Models [50.35325326050263]
  Retrieval-based machine learning methods have enjoyed success on a wide range of problems. Despite growing literature showcasing the promise of these models, the theoretical underpinning for such models remains underexplored. We present a formal treatment of retrieval-based models to characterize their generalization ability.
  arXiv  Detail & Related papers  (2022-10-06T00:33:01Z)
• A Generative Language Model for Few-shot Aspect-Based Sentiment Analysis [90.24921443175514]
  We focus on aspect-based sentiment analysis, which involves extracting aspect term, category, and predicting their corresponding polarities. We propose to reformulate the extraction and prediction tasks into the sequence generation task, using a generative language model with unidirectional attention. Our approach outperforms the previous state-of-the-art (based on BERT) on average performance by a large margins in few-shot and full-shot settings.
  arXiv  Detail & Related papers  (2022-04-11T18:31:53Z)
• RADDLE: An Evaluation Benchmark and Analysis Platform for Robust Task-oriented Dialog Systems [75.87418236410296]
  We introduce the RADDLE benchmark, a collection of corpora and tools for evaluating the performance of models across a diverse set of domains. RADDLE is designed to favor and encourage models with a strong generalization ability. We evaluate recent state-of-the-art systems based on pre-training and fine-tuning, and find that grounded pre-training on heterogeneous dialog corpora performs better than training a separate model per domain.
  arXiv  Detail & Related papers  (2020-12-29T08:58:49Z)
• UniT: Unified Knowledge Transfer for Any-shot Object Detection and Segmentation [52.487469544343305]
  Methods for object detection and segmentation rely on large scale instance-level annotations for training. We propose an intuitive and unified semi-supervised model that is applicable to a range of supervision.
  arXiv  Detail & Related papers  (2020-06-12T22:45:47Z)
• Behind the Scene: Revealing the Secrets of Pre-trained Vision-and-Language Models [65.19308052012858]
  Recent Transformer-based large-scale pre-trained models have revolutionized vision-and-language (V+L) research. We present VALUE, a set of meticulously designed probing tasks to decipher the inner workings of multimodal pre-training. Key observations: Pre-trained models exhibit a propensity for attending over text rather than images during inference.
  arXiv  Detail & Related papers  (2020-05-15T01:06:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.