DySem: Uncovering Dynamic Semantic Components of Large Language Models for Calculating Semantic Textual Similarity
arXiv:2605.29751v2 Announce Type: replace Abstract: Calculating semantic textual similarity is a foundational task in natural language processing. Current large language models (LLMs) based methods typically rely on extracting last-layer hidden states with fixed dimensions to compute similarity for every text pairs. We argue that this paradigm is suffer from two limitations: (i) The last hidden layer encodes more general knowledge rather than just semantic knowledge, making it suboptimal for semantic similarity computation; (ii) The hidden layer dimensions of LLMs are generally very large, whi
The proliferation of Large Language Models (LLMs) and their integration into various applications makes optimizing their core functions, like semantic textual similarity, a current research frontier.
Improving the efficiency and accuracy of semantic understanding in LLMs directly impacts the performance of AI agents, search engines, and automated reasoning systems, which are foundational to many future technologies.
This research suggests a potential shift from generic last-layer hidden states to more specialized, dynamic semantic components for better performing textual similarity, potentially making LLMs more nuanced and efficient in specific tasks.
- · AI researchers
- · NLP developers
- · AI agent developers
- · Data scientists
- · Developers relying on suboptimal, fixed-dimension embedding methods
More accurate and computationally efficient semantic understanding for LLMs.
Improved performance and broader application of AI agents and automated content analysis.
Accelerated development of more sophisticated AI systems capable of complex reasoning and knowledge extraction.
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Read at arXiv cs.CL