# Simple Techniques for Enhancing Sentence Embeddings in Generative Language Models

**Authors:** Bowen Zhang, Kehua Chang, Chunping Li  
**arXiv:** [2404.03921](https://arxiv.org/abs/2404.03921)  
**Date:** 2024-04-05  
**Summary page:** https://www.summarizepaper.com/en/paper/2404.03921v2/

## Key points

- Sentence embedding is crucial in Natural Language Processing applications like search engines, expert systems, and question-and-answer platforms.
- Recent advancements in sentence embedding research are driven by large language models such as LLaMA and Mistral, leading to significant breakthroughs.
- Most developments have focused on fine-tuning scenarios rather than exploring computationally efficient direct inference methods for sentence representation.
- The paper challenges the belief in the necessity of an Explicit One-word Limitation when deriving sentence embeddings from Pre-trained Language Models (PLMs).
- The authors demonstrate that this limitation may not be essential for discriminative models or when fine-tuning generative PLMs, opening up new possibilities for designing manual templates.
- Two innovative prompt engineering techniques – Pretended Chain of Thought and Knowledge Enhancement – are proposed to enhance the expressive power of PLMs' raw embeddings.
- Comprehensive experimentation confirms the effectiveness of these techniques across various PLM types and explores factors contributing to their success.
- The study sheds new light on optimizing sentence embeddings in generative language models and provides valuable insights for researchers and practitioners in Natural Language Processing.

## Summary

In the realm of Natural Language Processing, sentence embedding plays a crucial role in various applications such as search engines, expert systems, and question-and-answer platforms. Recent advancements in research on sentence embedding have been driven by the evolution of large language models like LLaMA and Mistral, leading to significant breakthroughs. However, most of these developments have focused on fine-tuning scenarios while neglecting the exploration of computationally efficient direct inference methods for sentence representation. This paper aims to fill this gap by challenging the conventional belief in the necessity of an Explicit One-word Limitation when deriving sentence embeddings from Pre-trained Language Models (PLMs). The authors demonstrate that while this limitation may be beneficial for generative models in direct inference scenarios, it is not essential for discriminative models or when fine-tuning generative PLMs. This finding opens up new possibilities for designing manual templates in future studies. Building on this insight, the authors propose two innovative prompt engineering techniques – Pretended Chain of Thought and Knowledge Enhancement – to further enhance the expressive power of PLMs' raw embeddings. Through comprehensive experimentation across various PLM types, they confirm the effectiveness of these techniques and provide a detailed exploration of the underlying factors contributing to their success. Overall, this study sheds new light on optimizing sentence embeddings in generative language models and offers valuable insights for researchers and practitioners working in the field of Natural Language Processing.