4 citations · 9 across the 13 of their papers we have counts for
13 papers
A Versatile Multimodal Agent for Multimedia Content Generation
Daoan Zhang, Wenlin Yao, Xiaoyang Wang +3
With the advancement of AIGC (AI-generated content) technologies, an increasing number of generative models are revolutionizing fields such as video editing, music generation, and…
Router-Tuning: A Simple and Effective Approach for Enabling Dynamic-Depth in Transformers
Shwai He, Tao Ge, Guoheng Sun +3
Traditional transformer models often allocate a fixed amount of computational resources to every input token, leading to inefficient and unnecessary computation. To address this, t…
DeFine: Decision-Making with Analogical Reasoning over Factor Profiles
Yebowen Hu, Xiaoyang Wang, Wenlin Yao +5
LLMs are ideal for decision-making thanks to their ability to reason over long contexts. However, challenges arise when processing speech transcripts that describe complex scenario…
DSBench: How Far Are Data Science Agents from Becoming Data Science Experts?
Liqiang Jing, Zhehui Huang, Xiaoyang Wang +6
Large Language Models (LLMs) and Large Vision-Language Models (LVLMs) have demonstrated impressive language/vision reasoning abilities, igniting the recent trend of building agents…
When Reasoning Meets Information Aggregation: A Case Study with Sports Narratives
Yebowen Hu, Kaiqiang Song, Sangwoo Cho +5
Reasoning is most powerful when an LLM accurately aggregates relevant information. We examine the critical role of information aggregation in reasoning by requiring the LLM to anal…
Polarity Calibration for Opinion Summarization
Yuanyuan Lei, Kaiqiang Song, Sangwoo Cho +3
Opinion summarization is automatically generating summaries from a variety of subjective information, such as product reviews or political opinions. The challenge of opinions summa…
Can Large Language Models do Analytical Reasoning?
Yebowen Hu, Kaiqiang Song, Sangwoo Cho +4
This paper explores the cutting-edge Large Language Model with analytical reasoning on sports. Our analytical reasoning embodies the tasks of letting large language models count ho…
SportsMetrics: Blending Text and Numerical Data to Understand Information Fusion in LLMs
Yebowen Hu, Kaiqiang Song, Sangwoo Cho +4
Large language models hold significant potential for integrating various data types, such as text documents and database records, for advanced analytics. However, blending text and…
SPECTRUM: Speaker-Enhanced Pre-Training for Long Dialogue Summarization
Sangwoo Cho, Kaiqiang Song, Chao Zhao +2
Multi-turn dialogues are characterized by their extended length and the presence of turn-taking conversations. Traditional language models often overlook the distinct features of t…
InFoBench: Evaluating Instruction Following Ability in Large Language Models
Yiwei Qin, Kaiqiang Song, Yebowen Hu +7
This paper introduces the Decomposed Requirements Following Ratio (DRFR), a new metric for evaluating Large Language Models' (LLMs) ability to follow instructions. Addressing a gap…
Zebra: Extending Context Window with Layerwise Grouped Local-Global Attention
Kaiqiang Song, Xiaoyang Wang, Sangwoo Cho +2
This paper introduces a novel approach to enhance the capabilities of Large Language Models (LLMs) in processing and understanding extensive text sequences, a critical aspect in ap…
MMC: Advancing Multimodal Chart Understanding with Large-scale Instruction Tuning
Fuxiao Liu, Xiaoyang Wang, Wenlin Yao +5
With the rapid development of large language models (LLMs) and their integration into large multimodal models (LMMs), there has been impressive progress in zero-shot completion of…
From Language Modeling to Instruction Following: Understanding the Behavior Shift in LLMs after Instruction Tuning
Xuansheng Wu, Wenlin Yao, Jianshu Chen +4
Large Language Models (LLMs) have achieved remarkable success, where instruction tuning is the critical step in aligning LLMs with user intentions. In this work, we investigate how…