16 citations · 43 across the 12 of their papers we have counts for
11 papers · 1 filter
ThoughtTrace: Understanding User Thoughts in Real-World LLM Interactions
Chuanyang Jin, Binze Li, Haopeng Xie +6
Conversational AI has now reached billions of users, yet existing datasets capture only what people say, not what they think. We introduce ThoughtTrace, the first large-scale datas…
MIST: Multimodal Interactive Speech-based Tool-calling Conversational Assistants for Smart Homes
Maximillian Chen, Xuanming Zhang, Michael Peng +3
The rise of Internet of Things (IoT) devices in the physical world necessitates voice-based interfaces capable of handling complex user experiences. While modern Large Language Mod…
VarBench: Robust Language Model Benchmarking Through Dynamic Variable Perturbation
Kun Qian, Shunji Wan, Claudia Tang +4
As large language models achieve impressive scores on traditional benchmarks, an increasing number of researchers are becoming concerned about benchmark data leakage during pre-tra…
Learning to Clarify: Multi-turn Conversations with Action-Based Contrastive Self-Training
Maximillian Chen, Ruoxi Sun, Tomas Pfister +1
Large language models (LLMs), optimized through human feedback, have rapidly emerged as a leading paradigm for developing intelligent conversational assistants. However, despite th…
Controllable Mixed-Initiative Dialogue Generation through Prompting
Maximillian Chen, Xiao Yu, Weiyan Shi +2
Mixed-initiative dialogue tasks involve repeated exchanges of information and conversational control. Conversational agents gain control by generating responses that follow particu…
Prompt-Based Monte-Carlo Tree Search for Goal-Oriented Dialogue Policy Planning
Xiao Yu, Maximillian Chen, Zhou Yu
Planning for goal-oriented dialogue often requires simulating future dialogue interactions and estimating task progress. Many approaches thus consider training neural networks to p…
PLACES: Prompting Language Models for Social Conversation Synthesis
Maximillian Chen, Alexandros Papangelis, Chenyang Tao +5
Collecting high quality conversational data can be very expensive for most applications and infeasible for others due to privacy, ethical, or similar concerns. A promising directio…
Pre-Finetuning for Few-Shot Emotional Speech Recognition
Maximillian Chen, Zhou Yu
Speech models have long been known to overfit individual speakers for many classification tasks. This leads to poor generalization in settings where the speakers are out-of-domain…
Weakly Supervised Data Augmentation Through Prompting for Dialogue Understanding
Maximillian Chen, Alexandros Papangelis, Chenyang Tao +5
Dialogue understanding tasks often necessitate abundant annotated data to achieve good performance and that presents challenges in low-resource settings. To alleviate this barrier,…
FastKASSIM: A Fast Tree Kernel-Based Syntactic Similarity Metric
Maximillian Chen, Caitlyn Chen, Xiao Yu +1
Syntax is a fundamental component of language, yet few metrics have been employed to capture syntactic similarity or coherence at the utterance- and document-level. The existing st…
Seamlessly Integrating Factual Information and Social Content with Persuasive Dialogue
Maximillian Chen, Weiyan Shi, Feifan Yan +4
Complex conversation settings such as persuasion involve communicating changes in attitude or behavior, so users' perspectives need to be addressed, even when not directly related…