360 citations · 375 across the 14 of their papers we have counts for
7 papers · 2 filters
Contrastive Learning for Inference in Dialogue
Etsuko Ishii, Yan Xu, Bryan Wilie +4
Inference, especially those derived from inductive processes, is a crucial component in our conversation to complement the information implicitly or explicitly conveyed by a speake…
InstructTODS: Large Language Models for End-to-End Task-Oriented Dialogue Systems
Willy Chung, Samuel Cahyawijaya, Bryan Wilie +2
Large language models (LLMs) have been used for diverse tasks in natural language processing (NLP), yet remain under-explored for task-oriented dialogue systems (TODS), especially…
PICK: Polished & Informed Candidate Scoring for Knowledge-Grounded Dialogue Systems
Bryan Wilie, Yan Xu, Willy Chung +3
Grounding dialogue response generation on external knowledge is proposed to produce informative and engaging responses. However, current knowledge-grounded dialogue (KGD) systems o…
Cross-Lingual Cross-Age Group Adaptation for Low-Resource Elderly Speech Emotion Recognition
Samuel Cahyawijaya, Holy Lovenia, Willy Chung +3
Speech emotion recognition plays a crucial role in human-computer interactions. However, most speech emotion recognition research is biased toward English-speaking adults, which hi…
InstructAlign: High-and-Low Resource Language Alignment via Continual Crosslingual Instruction Tuning
Samuel Cahyawijaya, Holy Lovenia, Tiezheng Yu +2
Large language models (LLMs) that are tuned with instructions have demonstrated remarkable capabilities in various tasks and languages. However, their ability to generalize to unde…
Learn What NOT to Learn: Towards Generative Safety in Chatbots
Leila Khalatbari, Yejin Bang, Dan Su +4
Conversational models that are generative and open-domain are particularly susceptible to generating unsafe content since they are trained on web-based social data. Prior approache…
A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity
Yejin Bang, Samuel Cahyawijaya, Nayeon Lee +10
This paper proposes a framework for quantitatively evaluating interactive LLMs such as ChatGPT using publicly available data sets. We carry out an extensive technical evaluation of…