Uncovering the Role of Initial Saliency in U-Shaped Attention Bias: Scaling Initial Token Weight for Enhanced Long-Text Processing

TL;DR

This paper investigates the U-shaped attention bias in large language models, identifies initial saliency as a contributing factor, and proposes a scaling method to improve long-text processing performance.

Contribution

It introduces the concept of initial saliency affecting attention bias and demonstrates how scaling attention weights enhances long-text understanding in LLMs.

Findings

Scaling attention weights improves long-text task performance by up to 3.6%.

Combining this method with position bias reduction further boosts accuracy.

The approach effectively mitigates the 'lost in the middle' phenomenon in LLMs.

Abstract

Large language models (LLMs) have demonstrated strong performance on a variety of natural language processing (NLP) tasks. However, they often struggle with long-text sequences due to the ``lost in the middle'' phenomenon. This issue has been shown to arise from a U-shaped attention bias, where attention is disproportionately focused on the beginning and end of a text, leaving the middle section underrepresented. While previous studies have attributed this bias to position encoding, our research first identifies an additional factor: initial saliency. It means that in the attention computation for each token, tokens with higher attention weights relative to the initial token tend to receive more attention in the prediction of the next token. We further find that utilizing this property by scaling attention weight between the initial token and others improves the model's ability to process long contexts, achieving a maximum improvement of 3.6\% in MDQA dataset. Moreover, combining this approach with existing methods to reduce position encoding bias further enhances performance, achieving a maximum improvement of 3.4\% in KV-Retrieval tasks.