activity
20222026
most citedCluster-Level Contrastive Learning for Emotion Recognition in Conversations

83 citations · 107 across the 8 of their papers we have counts for

collaborators

8 papers

cs.AI2026

Artificial Intelligence for Mathematical Reasoning: An Integrated Survey of Language Models, Neuro-symbolic Systems, and Verified Discovery

Syed Rifat Raiyan, Mohsinul Kabir, Hasan Mahmud +2

Mathematical reasoning has long served as a stringent test of machine intelligence; over the past decade, it has moved from a niche problem within NLP to one of the most consequent…

cs.CL2025

MiraMind: Benchmarking Reliable Mental Health Reasoning beyond Answer Accuracy

Mengxi Xiao, Kailai Yang, Pengde Zhao +12

Mental-health reasoning with large language models (LLMs) is an evidence-constrained judgment problem: models must transform limited, subjective, and often ambiguous evidence into…

cs.CL20244 cited

Open-FinLLMs: Open Multimodal Large Language Models for Financial Applications

Jimin Huang, Mengxi Xiao, Dong Li +41

Financial LLMs hold promise for advancing financial tasks and domain-specific applications. However, they are limited by scarce corpora, weak multimodal capabilities, and narrow ev…

cs.CL20233 cited

Back to the Future: Towards Explainable Temporal Reasoning with Large Language Models

Chenhan Yuan, Qianqian Xie, Jimin Huang +1

Temporal reasoning is a crucial NLP task, providing a nuanced understanding of time-sensitive contexts within textual data. Although recent advancements in LLMs have demonstrated t…

cs.IR20232 cited

CitationSum: Citation-aware Graph Contrastive Learning for Scientific Paper Summarization

Zheheng Luo, Qianqian Xie, Sophia Ananiadou

Citation graphs can be helpful in generating high-quality summaries of scientific papers, where references of a scientific paper and their correlations can provide additional knowl…

cs.CL2023

Span-based Named Entity Recognition by Generating and Compressing Information

Nhung T. H. Nguyen, Makoto Miwa, Sophia Ananiadou

The information bottleneck (IB) principle has been proven effective in various NLP applications. The existing work, however, only used either generative or information compression…