4 citations · 13 across the 21 of their papers we have counts for
10 papers · 1 filter
MoWE-Audio: Multitask AudioLLMs with Mixture of Weak Encoders
Wenyu Zhang, Shuo Sun, Bin Wang +6
The rapid advancements in large language models (LLMs) have significantly enhanced natural language processing capabilities, facilitating the development of AudioLLMs that process…
In2Core: Leveraging Influence Functions for Coreset Selection in Instruction Finetuning of Large Language Models
Ayrton San Joaquin, Bin Wang, Zhengyuan Liu +4
Despite advancements, fine-tuning Large Language Models (LLMs) remains costly due to the extensive parameter count and substantial data requirements for model generalization. Acces…
AudioBench: A Universal Benchmark for Audio Large Language Models
Bin Wang, Xunlong Zou, Geyu Lin +6
We introduce AudioBench, a universal benchmark designed to evaluate Audio Large Language Models (AudioLLMs). It encompasses 8 distinct tasks and 26 datasets, among which, 7 are new…
DnA-Eval: Enhancing Large Language Model Evaluation through Decomposition and Aggregation
Minzhi Li, Zhengyuan Liu, Shumin Deng +3
The acceleration of Large Language Models (LLMs) research has opened up new possibilities for evaluating generated texts. They serve as scalable and economical evaluators, but the…
CRAFT: Extracting and Tuning Cultural Instructions from the Wild
Bin Wang, Geyu Lin, Zhengyuan Liu +2
Large language models (LLMs) have rapidly evolved as the foundation of various natural language processing (NLP) applications. Despite their wide use cases, their understanding of…
Scaffolding Language Learning via Multi-modal Tutoring Systems with Pedagogical Instructions
Zhengyuan Liu, Stella Xin Yin, Carolyn Lee +1
Intelligent tutoring systems (ITSs) that imitate human tutors and aim to provide immediate and customized instructions or feedback to learners have shown their effectiveness in edu…
CrossIn: An Efficient Instruction Tuning Approach for Cross-Lingual Knowledge Alignment
Geyu Lin, Bin Wang, Zhengyuan Liu +1
Multilingual proficiency presents a significant challenge for large language models (LLMs). English-centric models are usually suboptimal in other languages, particularly those tha…
Resilience of Large Language Models for Noisy Instructions
Bin Wang, Chengwei Wei, Zhengyuan Liu +2
As the rapidly advancing domain of natural language processing (NLP), large language models (LLMs) have emerged as powerful tools for interpreting human commands and generating tex…
Personality-aware Student Simulation for Conversational Intelligent Tutoring Systems
Zhengyuan Liu, Stella Xin Yin, Geyu Lin +1
Intelligent Tutoring Systems (ITSs) can provide personalized and self-paced learning experience. The emergence of large language models (LLMs) further enables better human-machine…
Learning Planning-based Reasoning by Trajectories Collection and Process Reward Synthesizing
Fangkai Jiao, Chengwei Qin, Zhengyuan Liu +2
Large Language Models (LLMs) have demonstrated significant potential in handling complex reasoning tasks through step-by-step rationale generation. However, recent studies have rai…