activity
20202026
most citedDeconfounded Recommendation for Alleviating Bias Amplification

164 citations · 299 across the 6 of their papers we have counts for

collaborators

19 papers

cs.AI2026

CAPSUL: A Comprehensive Human Protein Benchmark for Subcellular Localization

Yicheng Hu, Xinyu Lin, Shulin Li +3

Subcellular localization is a crucial biological task for drug target identification and function annotation. Although it has been biologically realized that subcellular localizati…

cs.IR2025

Navigating Through Paper Flood: Advancing LLM-based Paper Evaluation through Domain-Aware Retrieval and Latent Reasoning

Wuqiang Zheng, Yiyan Xu, Xinyu Lin +3

With the rapid and continuous increase in academic publications, identifying high-quality research has become an increasingly pressing challenge. While recent methods leveraging La…

cs.CL2025

MathOPEval: A Fine-grained Evaluation Benchmark for Visual Operations of MLLMs in Mathematical Reasoning

Xiaoyuan Li, Moxin Li, Wenjie Wang +4

Recent progress in Multi-modal Large Language Models (MLLMs) has enabled step-by-step multi-modal mathematical reasoning by performing visual operations based on the textual instru…

cs.CL2025

SASFT: Sparse Autoencoder-guided Supervised Finetuning to Mitigate Unexpected Code-Switching in LLMs

Boyi Deng, Yu Wan, Baosong Yang +3

Large Language Models (LLMs) have impressive multilingual capabilities, but they suffer from unexpected code-switching, also known as language mixing, which involves switching to u…

q-fin.CP2025

Towards Temporal-Aware Multi-Modal Retrieval Augmented Generation in Finance

Fengbin Zhu, Junfeng Li, Liangming Pan +5

Finance decision-making often relies on in-depth data analysis across various data sources, including financial tables, news articles, stock prices, etc. In this work, we introduce…

cs.IR2025

Personalized Generation In Large Model Era: A Survey

Yiyan Xu, Jinghao Zhang, Alireza Salemi +6

In the era of large models, content generation is gradually shifting to Personalized Generation (PGen), tailoring content to individual preferences and needs. This paper presents t…