LLM-based Embeddings: Attention Values Encode Sentence Semantics Better Than Hidden States

• URL: http://arxiv.org/abs/2602.01572v1
• Date: Mon, 02 Feb 2026 03:09:37 GMT
• Title: LLM-based Embeddings: Attention Values Encode Sentence Semantics Better Than Hidden States
• Authors: Yeqin Zhang, Yunfei Wang, Jiaxuan Chen, Ke Qin, Yizheng Zhao, Cam-Tu Nguyen,
• Abstract summary: Sentence representations are foundational to many Natural Language Processing (NLP) applications.<n>This paper introduces a novel perspective, demonstrating that attention value vectors capture sentence semantics more effectively than hidden states.
• Score: 13.418437639290532
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sentence representations are foundational to many Natural Language Processing (NLP) applications. While recent methods leverage Large Language Models (LLMs) to derive sentence representations, most rely on final-layer hidden states, which are optimized for next-token prediction and thus often fail to capture global, sentence-level semantics. This paper introduces a novel perspective, demonstrating that attention value vectors capture sentence semantics more effectively than hidden states. We propose Value Aggregation (VA), a simple method that pools token values across multiple layers and token indices. In a training-free setting, VA outperforms other LLM-based embeddings, even matches or surpasses the ensemble-based MetaEOL. Furthermore, we demonstrate that when paired with suitable prompts, the layer attention outputs can be interpreted as aligned weighted value vectors. Specifically, the attention scores of the last token function as the weights, while the output projection matrix ($W_O$) aligns these weighted value vectors with the common space of the LLM residual stream. This refined method, termed Aligned Weighted VA (AlignedWVA), achieves state-of-the-art performance among training-free LLM-based embeddings, outperforming the high-cost MetaEOL by a substantial margin. Finally, we highlight the potential of obtaining strong LLM embedding models through fine-tuning Value Aggregation.

Related papers

• Learning to Compress: Unlocking the Potential of Large Language Models for Text Representation [34.21806963402883]
  We study the untapped potential of context compression as a pretext task for unsupervised adaptation of large language models (LLMs)<n> Experiments demonstrate that a well-designed compression objective can significantly enhance LLM-based text representations.<n>Further improvements through contrastive learning produce a strong representation model (LLM2Comp)
  arXiv  Detail & Related papers  (2025-11-21T10:45:44Z)
• Spatial Preference Rewarding for MLLMs Spatial Understanding [92.25703021388142]
  Multimodal large language models (MLLMs) have demonstrated promising spatial understanding capabilities.<n>Despite their successes, MLLMs still fall short in fine-grained spatial perception abilities.<n>We propose a Spatial Preference Rewarding(SPR) approach that enhances MLLMs' spatial capabilities.
  arXiv  Detail & Related papers  (2025-10-16T07:16:18Z)
• Where MLLMs Attend and What They Rely On: Explaining Autoregressive Token Generation [59.40886078302025]
  Multimodal large language models (MLLMs) have demonstrated remarkable capabilities in aligning visual inputs with natural language outputs.<n>Yet, the extent to which generated tokens depend on visual modalities remains poorly understood.<n>We present a lightweight black-box framework for explaining autoregressive token generation in MLLMs.
  arXiv  Detail & Related papers  (2025-09-26T15:38:42Z)
• Token Activation Map to Visually Explain Multimodal LLMs [23.774995444587667]
  We propose an estimated causal inference method to mitigate the interference of context to achieve high-quality MLLM explanation.<n>We term this method Token Activation Map (TAM) to highlight the consideration of interactions between tokens.<n>Our TAM method significantly outperforms existing SoTA methods, showcasing high-quality visualization results.
  arXiv  Detail & Related papers  (2025-06-29T14:50:45Z)
• Training Large Recommendation Models via Graph-Language Token Alignment [53.3142545812349]
  We propose a novel framework to train Large Recommendation models via Graph-Language Token Alignment.<n>By aligning item and user nodes from the interaction graph with pretrained LLM tokens, GLTA effectively leverages the reasoning abilities of LLMs.<n> Furthermore, we introduce Graph-Language Logits Matching (GLLM) to optimize token alignment for end-to-end item prediction.
  arXiv  Detail & Related papers  (2025-02-26T02:19:10Z)
• Elevating Visual Perception in Multimodal LLMs with Visual Embedding Distillation [109.5893580175657]
  In recent times, the standard practice for developing MLLMs is to feed features from vision encoder(s) into the LLM and train with natural language supervision.<n>This approach often causes models to lean towards language comprehension and undermine the rich visual perception signals present in the data.<n>We propose VisPer-LM, the first approach that infuses visual perception knowledge from expert vision encoders into the LLM's hidden representations.
  arXiv  Detail & Related papers  (2024-12-12T18:55:18Z)
• Accelerating Multimodal Large Language Models via Dynamic Visual-Token Exit and the Empirical Findings [66.04061083611863]
  Excessive use of visual tokens in existing Multimoal Large Language Models (MLLMs) often exhibits obvious redundancy and brings in prohibitively expensive computation.<n>We propose a simple yet effective method to improve the efficiency of MLLMs, termed dynamic visual-token exit (DyVTE)<n>DyVTE uses lightweight hyper-networks to perceive the text token status and decide the removal of all visual tokens after a certain layer.
  arXiv  Detail & Related papers  (2024-11-29T11:24:23Z)
• IntactKV: Improving Large Language Model Quantization by Keeping Pivot Tokens Intact [46.32830393597601]
  Large language models (LLMs) excel in natural language processing but demand intensive computation. This paper unveils a previously overlooked type of outliers in LLMs. We propose IntactKV to generate the KV cache of pivot tokens losslessly from the full-precision model.
  arXiv  Detail & Related papers  (2024-03-02T16:05:26Z)
• Take One Step at a Time to Know Incremental Utility of Demonstration: An Analysis on Reranking for Few-Shot In-Context Learning [23.932500424117244]
  In-Context Learning (ICL) is an emergent capability of Large Language Models (LLMs) Previous studies have shown that using LLMs' outputs as labels is effective in training models to select demonstrations. This paper presents an analysis on different utility functions by focusing on LLMs' output probability given ground-truth output.
  arXiv  Detail & Related papers  (2023-11-16T07:03:54Z)
• Alleviating Over-smoothing for Unsupervised Sentence Representation [96.19497378628594]
  We present a Simple method named Self-Contrastive Learning (SSCL) to alleviate this issue. Our proposed method is quite simple and can be easily extended to various state-of-the-art models for performance boosting.
  arXiv  Detail & Related papers  (2023-05-09T11:00:02Z)

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.