Embedding黑箱成为历史！这个新框架让模型“先解释，再学Embedding”

Core Insights - The article introduces GRACE, a new explainable generative embedding framework developed by researchers from multiple universities, aimed at addressing the limitations of traditional text embedding models [1][6]. Group 1: Background and Limitations - Text embedding models have evolved from BERT to various newer models, mapping text into vector spaces for tasks like semantic retrieval and clustering [3]. - A common flaw in these models is treating large language models as "mute encoders," which output vectors without explaining the similarity between texts [4]. - This black-box representation becomes a bottleneck in tasks requiring high interpretability and robustness, such as question-answer matching and cross-domain retrieval [5]. Group 2: GRACE Framework Overview - GRACE transforms "contrastive learning" into "reinforcement learning," redefining the meaning of contrastive learning signals [6]. - The framework emphasizes generating explanations (rationales) for text before learning embeddings, allowing the model to produce logical and semantically consistent reasoning [7][25]. - GRACE consists of three key modules: 1. Rationale-Generating Policy, which generates explanatory reasoning chains for input texts [8]. 2. Representation Extraction, which combines input and rationale to compute final embeddings [9]. 3. Contrastive Rewards, which redefines contrastive learning objectives as a reward function for reinforcement learning updates [11]. Group 3: Training Process - GRACE can be trained in both supervised and unsupervised manners, utilizing labeled query-document pairs and self-alignment techniques [12][18]. - In the supervised phase, the model learns semantic relationships from a dataset of 1.5 million samples [13]. - The unsupervised phase generates multiple rationales for each text, encouraging consistent representations across different explanations [17]. Group 4: Experimental Results - GRACE was evaluated across 56 datasets in various tasks, showing significant performance improvements over baseline models in retrieval, pair classification, and clustering [19][20]. - The results indicate that GRACE not only enhances embedding capabilities without sacrificing generative abilities but also provides transparent representations that can be understood by users [25][27]. Group 5: Conclusion - Overall, GRACE represents a paradigm shift in embedding models, moving towards a framework that can explain its understanding process, thus enhancing both performance and interpretability [28].