4 citations · 9 across the 13 of their papers we have counts for
8 papers · 2 filters
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…
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…