3 papers
cs.CL2026
Focusing Condition: Inference-Time Self-Contrastive Steering Elicits Better Conditional Text Embeddings in LLMs
Zifeng Cheng, Lingyun Qian, Zhiwei Jiang +5
Extracting conditional text embeddings from large language models (LLMs) is a promising paradigm, as it requires neither additional data nor fine-tuning. Existing methods incorpora…
cs.IR2026
ICEGR: An Intent-Coherent End-to-End Generative Retrieval Framework for E-commerce Search
Jiayi Tuo, Hehan Li, Dongjun Fu +10
Generative Retrieval (GR) is promising for e-commerce search, yet existing methods struggle to maintain query-intent consistency throughout the training pipeline. First, semantic I…
cs.CL2026
NE-R1: Enhancing Named Entity Recognition Model via Reinforcement Learning
Meixuan Chen, Hehan Li, Ruizhi Zhao +8
Named Entity Recognition (NER) has achieved substantial progress since the advent of large language models (LLMs). Nevertheless, the recognition of long-tail and domain-specific en…