Summary of When Text Embedding Meets Large Language Model: a Comprehensive Survey, by Zhijie Nie et al.
When Text Embedding Meets Large Language Model: A Comprehensive Survey
by Zhijie Nie, Zhangchi Feng, Mingxin Li, Cunwang Zhang, Yanzhao Zhang, Dingkun Long, Richong Zhang
First submitted to arxiv on: 12 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The survey categorizes the interplay between large language models (LLMs) and text embeddings into three themes: LLM-augmented text embedding, where traditional methods are enhanced with LLMs; LLMs as text embedders, adapting their capabilities for high-quality embedding; and Text embedding understanding with LLMs, analyzing and interpreting embeddings. This novel overview organizes recent works by interaction patterns rather than specific applications. The survey also highlights unresolved challenges from the pre-LLM era and emerging obstacles brought forth by LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how big language models can be used to improve text embeddings, which are important for many natural language processing tasks. Text embeddings help computers understand the meaning of words and phrases, and this technology has been driving progress in many areas of NLP. The survey looks at three main ways that LLMs interact with text embeddings: by enhancing traditional methods, by using their own capabilities to create high-quality embeddings, and by helping us understand what these embeddings mean. |
Keywords
» Artificial intelligence » Embedding » Natural language processing » Nlp
