21 citations · 44 across the 10 of their papers we have counts for
11 papers
ShadowPEFT: Shadow Network for Parameter-Efficient Fine-Tuning
Xianming Li, Zongxi Li, Tsz-fung Andrew Lee +3
Popular low-rank parameter-efficient fine-tuning (PEFT) methods represent adaptation as separate updates to selected backbone weights, without maintaining an explicit task-specific…
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…
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…
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…
BMX: Entropy-weighted Similarity and Semantic-enhanced Lexical Search
Xianming Li, Julius Lipp, Aamir Shakir +2
BM25, a widely-used lexical search algorithm, remains crucial in information retrieval despite the rise of pre-trained and large language models (PLMs/LLMs). However, it neglects q…
2D Matryoshka Sentence Embeddings
Xianming Li, Zongxi Li, Jing Li +2
Common approaches rely on fixed-length embedding vectors from language models as sentence embeddings for downstream tasks such as semantic textual similarity (STS). Such methods ar…
Generative Deduplication For Socia Media Data Selection
Xianming Li, Jing Li
Social media data exhibits severe redundancy caused by its noisy nature. It leads to increased training time and model bias in its processing. To address this issue, we propose a n…
BeLLM: Backward Dependency Enhanced Large Language Model for Sentence Embeddings
Xianming Li, Jing Li
Sentence embeddings are crucial in measuring semantic similarity. Most recent studies employed large language models (LLMs) to learn sentence embeddings. Existing LLMs mainly adopt…
Label Supervised LLaMA Finetuning
Zongxi Li, Xianming Li, Yuzhang Liu +5
The recent success of Large Language Models (LLMs) has gained significant attention in both academia and industry. Substantial efforts have been made to enhance the zero- and few-s…
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…