1 citations · 1 across the 5 of their papers we have counts for
6 papers
Fine-Refine: Iterative Fine-grained Refinement for Mitigating Dialogue Hallucination
Xiangyan Chen, Yujian Gan, Matthew Purver
The tendency for hallucination in current large language models (LLMs) negatively impacts dialogue systems. Such hallucinations produce factually incorrect responses that may misle…
FineDialFact: A benchmark for Fine-grained Dialogue Fact Verification
Xiangyan Chen, Yufeng Li, Yujian Gan +2
Large language models are known to produce hallucinations - factually incorrect or fabricated information - which poses significant challenges for many natural language processing…
Improving Factuality for Dialogue Response Generation via Graph-Based Knowledge Augmentation
Xiangyan Chen, Yujian Gan, Yimeng Gu +1
Large Language Models (LLMs) succeed in many natural language processing tasks. However, their tendency to hallucinate - generate plausible but inconsistent or factually incorrect…
ClarQ-LLM: A Benchmark for Models Clarifying and Requesting Information in Task-Oriented Dialog
Yujian Gan, Changling Li, Jinxia Xie +3
We introduce ClarQ-LLM, an evaluation framework consisting of bilingual English-Chinese conversation tasks, conversational agents and evaluation metrics, designed to serve as a str…
Temporal Network Analysis of Email Communication Patterns in a Long Standing Hierarchy
Matthew Russell Barnes, Mladen Karan, Stephen McQuistin +5
An important concept in organisational behaviour is how hierarchy affects the voice of individuals, whereby members of a given organisation exhibit differing power relations based…
Reformulating NLP tasks to Capture Longitudinal Manifestation of Language Disorders in People with Dementia
Dimitris Gkoumas, Matthew Purver, Maria Liakata
Dementia is associated with language disorders which impede communication. Here, we automatically learn linguistic disorder patterns by making use of a moderately-sized pre-trained…