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README.md ADDED
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+ ---
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+ language:
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+ - en
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+ license: apache-2.0
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+ base_model: Qwen/Qwen2.5-7B
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+ library_name: peft
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+ tags:
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+ - text-embeddings
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+ - sentence-transformers
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+ - contrastive-learning
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+ - generative-embeddings
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+ - mteb
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+ datasets:
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+ - MS-MARCO
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+ - NLI
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+ metrics:
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+ - mteb
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+ pipeline_tag: feature-extraction
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+ ---
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+ # GIRCSE: Generative Iterative Refinement for Contrastive Sentence Embeddings
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+
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+ **GIRCSE** is a novel generative embedding framework that transforms Large Language Models (LLMs) into text encoders by leveraging their autoregressive generative capabilities. Unlike traditional encoder-only embeddings, GIRCSE generates a sequence of **"soft refinement tokens"** and utilizes an **Iterative Contrastive Refinement (ICR)** objective to progressively distill semantics into high-quality embeddings.
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+
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+ ## Model Details
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+
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+ ### Model Description
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+
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+ GIRCSE addresses the limitations of static LLM-based embeddings by treating the representation learning process as an iterative refinement task.
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+
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+ - **Key Innovation:** Instead of a single forward pass, the model generates $k$ auxiliary soft tokens. These tokens capture latent concepts and implicit semantics (e.g., task-specific instructions) that are often missed by standard pooling methods.
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+ - **Iterative Contrastive Refinement (ICR):** A stepwise objective that ensures each additional generated token monotonically improves the embedding quality.
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+ - **Test-time Scaling:** An emergent property where generating more tokens at inference time (e.g., 5 to 20 tokens) leads to better performance on downstream tasks, analogous to "Chain-of-Thought" for embeddings.
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+
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+ - **Developed by:** [Yu-Che (Roy) Tsai](https://github.com/Roytsai27), et al.
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+ - **Model type:** Generative Text Embedding (based on Decoder-only LLM)
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+ - **Language(s) (NLP):** English
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+ - **License:** Apache 2.0
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+ - **Finetuned from model:** [Qwen/Qwen2.5-7B](Qwen/Qwen2.5-7B)
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+
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+ ### Model Sources
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+
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+ - **Repository:** [Roytsai27/GIRCSE](https://github.com/Roytsai27/GIRCSE)
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+ - **Paper:** [Let LLMs Speak Embedding Languages: Generative Text Embeddings via Iterative Contrastive Refinement](https://arxiv.org/abs/2509.24291)
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+
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+ ## Training Details
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+
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+ ### Training Data
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+
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+ The model was trained on a curated mix of contrastive datasets (e.g., MS-MARCO, NLI) totaling approximately 200K samples.
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+
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+ ### Training Procedure
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+
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+ * **Method:** LoRA (Low-Rank Adaptation)
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+ * **Objective:** Iterative Contrastive Refinement (ICR) with Stepwise Contrastive Loss.
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+ * **Steps:** 5 refinement steps were used during training.
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+ * **Framework:** PEFT 0.15.2 + Transformers.
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+
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+
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+ ## Citation
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+
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+ If you find this work helpful, please cite:
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+
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+ **BibTeX:**
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+
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+ ```bibtex
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+ @article{tsai2025gircse,
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+ title={Let LLMs Speak Embedding Languages: Generative Text Embeddings via Iterative Contrastive Refinement},
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+ author={Tsai, Yu-Che and others},
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+ journal={arXiv preprint arXiv:2509.24291},
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+ year={2025}
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+ }
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+
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+ ```
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+
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+ ## Contact
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+
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+ For questions, please open an issue in the [GitHub Repository](https://github.com/Roytsai27/GIRCSE).
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+
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+ ```
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