4.5k citations · 5k across the 20 of their papers we have counts for
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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…
Towards Mitigating Hallucination in Large Language Models via Self-Reflection
Ziwei Ji, Tiezheng Yu, Yan Xu +3
Large language models (LLMs) have shown promise for generative and knowledge-intensive tasks including question-answering (QA) tasks. However, the practical deployment still faces…
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…
Diverse and Faithful Knowledge-Grounded Dialogue Generation via Sequential Posterior Inference
Yan Xu, Deqian Kong, Dehong Xu +4
The capability to generate responses with diversity and faithfulness using factual knowledge is paramount for creating a human-like, trustworthy dialogue system. Common strategies…
KILM: Knowledge Injection into Encoder-Decoder Language Models
Yan Xu, Mahdi Namazifar, Devamanyu Hazarika +3
Large pre-trained language models (PLMs) have been shown to retain implicit knowledge within their parameters. To enhance this implicit knowledge, we propose Knowledge Injection in…
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…