The paper explores techniques for efficiently processing and analyzing content to derive insights. It focuses on enhancing the quality of sentence embeddings generated by large language models (LLMs) through innovative prompting strategies.
The key highlights are:
The authors investigate the role of the Explicit One-word Limitation (EOL) technique, which was previously proposed to improve sentence embeddings from generative LLMs. They find that EOL is primarily beneficial for direct inference scenarios with generative models, and not as crucial for discriminative models or fine-tuning generative models.
Building on this insight, the authors propose two novel prompting engineering methods: Pretended Chain of Thought (CoT) and Knowledge Enhancement. These techniques involve appending a fixed prefix to the EOL prompt to leverage the contextual learning capabilities of LLMs.
Comprehensive experiments on various LLMs, including OPT, LLaMA, LLaMA2, and Mistral, demonstrate that Pretended CoT and Knowledge Enhancement significantly enhance the quality of raw sentence embeddings, outperforming unsupervised fine-tuning approaches like SimCSE.
The authors analyze the underlying factors contributing to the success of their proposed methods, including improved alignment and uniformity of the sentence embeddings, as well as more focused attention on the core semantic elements of the input sentences.
The authors make their code publicly available, encouraging reproducibility and further research in this direction.
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by Bowen Zhang,... at arxiv.org 04-08-2024
https://arxiv.org/pdf/2404.03921.pdfDeeper Inquiries