arXiv:2602.01572v2 Announce Type: replace 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 tha
The paper addresses an ongoing challenge in NLP related to optimizing LLM representations for semantic tasks, proposing a new method for generating more effective sentence embeddings right now.
Improved sentence representations directly enhance the performance of a wide range of NLP applications, from search to summarization, making LLMs more practical and efficient.
The focus for deriving LLM-based sentence representations shifts from final-layer hidden states to attention value vectors, potentially leading to more accurate and generalizable embeddings.
- · NLP researchers
- · Developers of LLM applications
- · Companies relying on semantic search
- · Older embedding methods
Immediate improvements in the precision and recall of information retrieval and text understanding systems using LLMs.
Accelerated development of more sophisticated AI agent architectures that rely on robust semantic understanding capabilities.
Potentially lowers computational costs for achieving high-quality semantic representations across various NLP tasks, broadening access.
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Read at arXiv cs.CL