Ziliang Pang

most citedMerge, Ensemble, and Cooperate! A Survey on Collaborative Strategies in the Era of Large Language Models

6 citations · 6 across the 3 of their papers we have counts for

collaborators

3 papers

cs.SD2026

EXAM $^{2}$ : $E x t e n d in g$ $A u d i o$ $U n d er s t an d in g$ $in$ $M u l t i l in g u a l$ $an d$ $M u l t im o d a l$ $A na l y s i s$

Jiawen Wang, Xiaoxue Gao, Zi Haur Pang +1

Recent large audio language models (LALMs) have achieved impressive progress in audio understanding. However, existing evaluations remain largely constrained to English and narrow…

cs.SD2026

ERM-MinMaxGAP: Benchmarking and Mitigating Gender Bias in Multilingual Multimodal Speech-LLM Emotion Recognition

Zi Haur Pang, Xiaoxue Gao, Tatsuya Kawahara +1

Speech emotion recognition (SER) systems can exhibit gender-related performance disparities, but how such bias manifests in multilingual speech LLMs across languages and modalities…

cs.CL2024★ 6 cited

Merge, Ensemble, and Cooperate! A Survey on Collaborative Strategies in the Era of Large Language Models

Jinliang Lu, Ziliang Pang, Min Xiao +3

The remarkable success of Large Language Models (LLMs) has ushered natural language processing (NLP) research into a new era. Despite their diverse capabilities, LLMs trained on di…

EXAM2: Ex​tending A​udio Understanding in M​ultilingual and M​ultimodal Analysis

ERM-MinMaxGAP: Benchmarking and Mitigating Gender Bias in Multilingual Multimodal Speech-LLM Emotion Recognition

Merge, Ensemble, and Cooperate! A Survey on Collaborative Strategies in the Era of Large Language Models

EXAM $^{2}$ : $E x t e n d in g$ $A u d i o$ $U n d er s t an d in g$ $in$ $M u l t i l in g u a l$ $an d$ $M u l t im o d a l$ $A na l y s i s$