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Ching-Yu Tsai

2 accepted papers

2026

Let LLMs Speak Embedding Languages: Generative Text Embeddings via Iterative Contrastive Refinement

ICLR 2026poster

Existing large language model (LLM)-based embeddings typically adopt an encoder-only paradigm, treating LLMs as static feature extractors and overlooking their core gener- ative strengths. We introduce GIRCSE (Generative Iterative Refinement for Contrastive Sentence Embeddings), a novel framework th…

Cited by 0SourceScholar
2025

Neuron-Level Differentiation of Memorization and Generalization in Large Language Models

EMNLP 2025

We investigate how Large Language Models (LLMs) distinguish between memorization and generalization at the neuron level. Through carefully designed tasks, we identify distinct neuron subsets responsible for each behavior. Experiments on both a GPT-2 model trained from scratch and a pretrained LLaMA-

Cited by 0SourcePDFScholar