Latent Lexical Projection in Large Language Models: A Novel Approach to Implicit Representation Refinement

TL;DR

This paper introduces Latent Lexical Projection (LLP), a novel method that refines lexical representations in large language models to improve semantic coherence, accuracy, and diversity in generated text.

Contribution

The paper presents a new projection technique that enhances internal lexical representations, leading to better alignment with context and improved language model performance.

Findings

Reduced perplexity and higher BLEU scores

Increased lexical diversity and vocabulary variation

Improved long-range dependency retention

Abstract

Generating semantically coherent text requires a robust internal representation of linguistic structures, which traditional embedding techniques often fail to capture adequately. A novel approach, Latent Lexical Projection (LLP), is introduced to refine lexical representations through a structured transformation into a latent space, thereby enhancing the alignment between input embeddings and their contextual meanings. The method integrates an optimized projection mechanism within an existing language model architecture, enabling more accurate token selection while maintaining syntactic integrity. Evaluations across multiple benchmarks indicate a reduction in perplexity and an increase in BLEU scores, suggesting improvements in predictive accuracy and fluency. The analysis of lexical diversity reveals a more varied vocabulary in generated text, addressing common issues of redundancy and repetitive phrase structures. Further assessments of entropy distributions demonstrate a decline in uncertainty during decoding, reflecting enhanced confidence in word selection. Additionally, long-range dependency retention exhibits measurable gains, with increased classification accuracy at extended token distances. Computational efficiency remains within manageable constraints, despite the added projection mechanism, highlighting the practicality of LLP for integration into existing architectures.