7 papers
ShadowPEFT: Shadow Network for Parameter-Efficient Fine-Tuning
Xianming Li, Zongxi Li, Tsz-fung Andrew Lee +3
Parameter-efficient fine-tuning (PEFT) reduces the training cost of full-parameter fine-tuning for large language models (LLMs) by training only a small set of task-specific parame…
ProRank: Prompt Warmup via Reinforcement Learning for Small Language Models Reranking
Xianming Li, Aamir Shakir, Rui Huang +4
Reranking is fundamental to information retrieval and retrieval-augmented generation, with recent Large Language Models (LLMs) significantly advancing reranking quality. Most curre…
HI-TransPA: Hearing Impairments Translation Personal Assistant
Zhiming Ma, Shiyu Gan, Junhao Zhao +10
Hearing-impaired individuals often face significant barriers in daily communication due to the inherent challenges of producing clear speech. To address this, we introduce the Omni…
LIR: The First Workshop on Late Interaction and Multi Vector Retrieval @ ECIR 2026
Benjamin Clavié, Xianming Li, Antoine Chaffin +4
Late interaction retrieval methods, pioneered by ColBERT, have emerged as a powerful alternative to single-vector neural IR. By leveraging fine-grained, token-level representations…
OASIS: Order-Augmented Strategy for Improved Code Search
Zuchen Gao, Zizheng Zhan, Xianming Li +6
Code embeddings capture the semantic representations of code and are crucial for various code-related large language model (LLM) applications, such as code search. Previous trainin…
AnglE-optimized Text Embeddings
Xianming Li, Jing Li
High-quality text embedding is pivotal in improving semantic textual similarity (STS) tasks, which are crucial components in Large Language Model (LLM) applications. However, a com…