GEM: Empowering LLM for both Embedding Generation and Language Understanding

• URL: http://arxiv.org/abs/2506.04344v1
• Date: Wed, 04 Jun 2025 18:02:07 GMT
• Title: GEM: Empowering LLM for both Embedding Generation and Language Understanding
• Authors: Caojin Zhang, Qiang Zhang, Ke Li, Sai Vidyaranya Nuthalapati, Benyu Zhang, Jason Liu, Serena Li, Lizhu Zhang, Xiangjun Fan,
• Abstract summary: We propose Generative Embedding large language Model (GEM) to generate high-quality text embeddings.<n>Our method inserts new special token(s) into a text body, and generates summarization embedding of the text by manipulating the attention mask.<n>Our results indicate that our approach can empower LLMs with state-of-the-art text embedding capabilities while maintaining their original NLP performance.
• Score: 11.081595808236239
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Large decoder-only language models (LLMs) have achieved remarkable success in generation and reasoning tasks, where they generate text responses given instructions. However, many applications, e.g., retrieval augmented generation (RAG), still rely on separate embedding models to generate text embeddings, which can complicate the system and introduce discrepancies in understanding of the query between the embedding model and LLMs. To address this limitation, we propose a simple self-supervised approach, Generative Embedding large language Model (GEM), that enables any large decoder-only LLM to generate high-quality text embeddings while maintaining its original text generation and reasoning capabilities. Our method inserts new special token(s) into a text body, and generates summarization embedding of the text by manipulating the attention mask. This method could be easily integrated into post-training or fine tuning stages of any existing LLMs. We demonstrate the effectiveness of our approach by applying it to two popular LLM families, ranging from 1B to 8B parameters, and evaluating the transformed models on both text embedding benchmarks (MTEB) and NLP benchmarks (MMLU). The results show that our proposed method significantly improves the original LLMs on MTEB while having a minimal impact on MMLU. Our strong results indicate that our approach can empower LLMs with state-of-the-art text embedding capabilities while maintaining their original NLP performance

Related papers

• Ensemble Learning for Large Language Models in Text and Code Generation: A Survey [6.041894045506043]
  We focus on four methods and models that show strong performance and potential for broader applications.<n>These include better representation of diversity, improved output quality, and greater flexibility in applications.
  arXiv  Detail & Related papers  (2025-03-13T18:50:57Z)
• Idiosyncrasies in Large Language Models [54.26923012617675]
  We unveil and study idiosyncrasies in Large Language Models (LLMs)<n>We find that fine-tuning text embedding models on LLM-generated texts yields excellent classification accuracy.<n>We leverage LLM as judges to generate detailed, open-ended descriptions of each model's idiosyncrasies.
  arXiv  Detail & Related papers  (2025-02-17T18:59:02Z)
• LLM-Lasso: A Robust Framework for Domain-Informed Feature Selection and Regularization [59.75242204923353]
  We introduce LLM-Lasso, a framework that leverages large language models (LLMs) to guide feature selection in Lasso regression.<n>LLMs generate penalty factors for each feature, which are converted into weights for the Lasso penalty using a simple, tunable model.<n>Features identified as more relevant by the LLM receive lower penalties, increasing their likelihood of being retained in the final model.
  arXiv  Detail & Related papers  (2025-02-15T02:55:22Z)
• ULLME: A Unified Framework for Large Language Model Embeddings with Generation-Augmented Learning [72.90823351726374]
  We introduce the Unified framework for Large Language Model Embedding (ULLME), a flexible, plug-and-play implementation that enables bidirectional attention across various LLMs. We also propose Generation-augmented Representation Learning (GRL), a novel fine-tuning method to boost LLMs for text embedding tasks. To showcase our framework's flexibility and effectiveness, we release three pre-trained models from ULLME with different backbone architectures.
  arXiv  Detail & Related papers  (2024-08-06T18:53:54Z)
• One Token Can Help! Learning Scalable and Pluggable Virtual Tokens for Retrieval-Augmented Large Language Models [67.49462724595445]
  Retrieval-augmented generation (RAG) is a promising way to improve large language models (LLMs)<n>We propose a novel method that involves learning scalable and pluggable virtual tokens for RAG.
  arXiv  Detail & Related papers  (2024-05-30T03:44:54Z)
• Peering into the Mind of Language Models: An Approach for Attribution in Contextual Question Answering [9.86691461253151]
  We introduce a novel method for attribution in contextual question answering, leveraging the hidden state representations of large language models (LLMs) Our approach bypasses the need for extensive model retraining and retrieval model overhead, offering granular attributions and preserving the quality of generated answers. We present Verifiability-granular, an attribution dataset which has token level annotations for LLM generations in the contextual question answering setup.
  arXiv  Detail & Related papers  (2024-05-28T09:12:44Z)
• ReMoDetect: Reward Models Recognize Aligned LLM's Generations [55.06804460642062]
  Large language models (LLMs) generate human-preferable texts. In this paper, we identify the common characteristics shared by these models. We propose two training schemes to further improve the detection ability of the reward model.
  arXiv  Detail & Related papers  (2024-05-27T17:38:33Z)
• LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders [34.421335513040795]
  Large decoder-only language models (LLMs) are the state-of-the-art models on most of today's NLP tasks and benchmarks. We introduce LLM2Vec, a simple unsupervised approach that can transform any decoder-only LLM into a strong text encoder.
  arXiv  Detail & Related papers  (2024-04-09T02:51:05Z)
• LLM-Pruner: On the Structural Pruning of Large Language Models [65.02607075556742]
  Large language models (LLMs) have shown remarkable capabilities in language understanding and generation. We tackle the compression of LLMs within the bound of two constraints: being task-agnostic and minimizing the reliance on the original training dataset. Our method, named LLM-Pruner, adopts structural pruning that selectively removes non-critical coupled structures.
  arXiv  Detail & Related papers  (2023-05-19T12:10:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.