2 citations · 2 across the 9 of their papers we have counts for
9 papers
Igniting Creative Writing in Small Language Models: LLM-as-a-Judge versus Multi-Agent Refined Rewards
Xiaolong Wei, Bo Lu, Xingyu Zhang +4
Large Language Models (LLMs) have demonstrated remarkable creative writing capabilities, yet their substantial computational demands hinder widespread use. Enhancing Small Language…
CoLD: Counterfactually-Guided Length Debiasing for Process Reward Models in Mathematical Reasoning
Congmin Zheng, Jiachen Zhu, Jianghao Lin +6
Process Reward Models (PRMs) play a central role in evaluating and guiding multi-step reasoning in large language models (LLMs), especially for mathematical problem solving. Howeve…
Just ASR + LLM? A Study on Speech Large Language Models' Ability to Identify and Understand Speaker in Spoken Dialogue
Junkai Wu, Xulin Fan, Bo-Ru Lu +4
In recent years, we have observed a rapid advancement in speech language models (SpeechLLMs), catching up with humans' listening and reasoning abilities. SpeechLLMs have demonstrat…
CPS-TaskForge: Generating Collaborative Problem Solving Environments for Diverse Communication Tasks
Nikita Haduong, Irene Wang, Bo-Ru Lu +2
Teams can outperform individuals; could adding AI teammates further bolster performance of teams solving problems collaboratively? Collaborative problem solving (CPS) research comm…
Efficient Encoder-Decoder Transformer Decoding for Decomposable Tasks
Bo-Ru Lu, Nikita Haduong, Chien-Yu Lin +3
Transformer-based NLP models are powerful but have high computational costs that limit deployment. Finetuned encoder-decoder models are popular in specialized domains and can outpe…
Does Collaborative Human-LM Dialogue Generation Help Information Extraction from Human Dialogues?
Bo-Ru Lu, Nikita Haduong, Chia-Hsuan Lee +7
The capabilities of pretrained language models have opened opportunities to explore new application areas, but applications involving human-human interaction are limited by the fac…
Unsupervised Learning of Hierarchical Conversation Structure
Bo-Ru Lu, Yushi Hu, Hao Cheng +2
Human conversations can evolve in many different ways, creating challenges for automatic understanding and summarization. Goal-oriented conversations often have meaningful sub-dial…
DIALKI: Knowledge Identification in Conversational Systems through Dialogue-Document Contextualization
Zeqiu Wu, Bo-Ru Lu, Hannaneh Hajishirzi +1
Identifying relevant knowledge to be used in conversational systems that are grounded in long documents is critical to effective response generation. We introduce a knowledge ident…
Order-Preserving Abstractive Summarization for Spoken Content Based on Connectionist Temporal Classification
Bo-Ru Lu, Frank Shyu, Yun-Nung Chen +2
Connectionist temporal classification (CTC) is a powerful approach for sequence-to-sequence learning, and has been popularly used in speech recognition. The central ideas of CTC in…