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arXiv 2104.08455cs.CL
Neural Path Hunter: Reducing Hallucination in Dialogue Systems via Path Grounding
University of Alberta（阿尔伯塔大学）；Mila（米拉人工智能研究院）；McGill University（麦吉尔大学）；Canada CIFAR AI Chair（加拿大CIFAR人工智能讲席教授）；The Hong Kong University of Science and Technology（香港科技大学）
Nouha Dziri, Andrea Madotto, Osmar Zaiane, Avishek Joey Bose
Dialogue systems powered by large pre-trained language models (LM) exhibit an innate ability to deliver fluent and natural-looking responses. Despite their impressive generation performance, these models can often generate factually incorrect statements impeding their widespread adoption. In this paper, we focus on the task of improving the faithfulness -- and thus reduce hallucination -- of Neural Dialogue Systems to known facts supplied by a Knowledge Graph (KG). We propose Neural Path Hunter which follows a generate-then-refine strategy whereby a generated response is amended using the k-hop subgraph of a KG. Neural Path Hunter leverages a separate token-level fact critic to identify plausible sources of hallucination followed by a refinement stage consisting of a chain of two neural LM's that retrieves correct entities by crafting a query signal that is propagated over the k-hop subgraph. Our proposed model can easily be applied to any dialogue generated responses without retraining the model. We empirically validate our proposed approach on the OpenDialKG dataset against a suite of metrics and report a relative improvement of faithfulness over dialogue responses by 20.35% based on FeQA (Durmus et al., 2020).
Grounding or Guessing? Visual Signals for Detecting Hallucinations in Sign Language Translation
German Research Center for Artificial Intelligence (DFKI GmbH)（德国人工智能研究中心（DFKI GmbH））；Saarland Informatics Campus（萨尔兰州信息学校园）；Barcelona Supercomputing Center (BSC-CNS)（巴塞罗那超级计算中心（BSC-CNS））
Detecting Clinical Hallucinations in LVLMs via Counterfactual Visual Grounding Uncertainty
School of Intelligent Science and Technology, Nanjing University（南京大学智能科学与技术学院）；National Institute of Healthcare Data Science at Nanjing University（南京大学医疗数据科学国家研究院）；School of Biomedical Engineering, Nanjing University（南京大学生物医学工程学院）
Detecting Contextual Hallucinations in LLMs with Frequency-Aware Attention
Department of Informatics, King's College London, UK（伦敦国王学院信息学院）；Department of Statistics, University of Warwick, UK（沃里克大学统计系）；School of Computer Science, The University of Sheffield, UK（谢菲尔德大学计算机科学学院）；The Alan Turing Institute, UK（艾伦·图灵研究所）
Korea University（高丽大学）；NCSOFT（NCSOFT公司）；Naver Corporation（Naver公司）
To build a conversational agent that interacts fluently with humans, previous studies blend knowledge or personal profile into the pre-trained language model. However, the model that considers knowledge and persona at the same time is still limited, leading to hallucination and a passive way of using personas. We propose an effective dialogue agent that grounds external knowledge and persona simultaneously. The agent selects the proper knowledge and persona to use for generating the answers with our candidate scoring implemented with a poly-encoder. Then, our model generates the utterance with…
P$^2$-DPO: Grounding Hallucination in Perceptual Processing via Calibration Direct Preference Optimization
Guangdong Provincial Key Laboratory of Computational AI Models and Cognitive Intelligence, School of Computer Science & Engineering, South China University of Technology（广东省计算人工智能模型与认知智能重点实验室，计算机科学与工程学院，华南理工大学）；Pazhou Lab, Guangzhou, China（琶洲实验室，广州，中国）；Engineering Research Center of the Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human, Guangzhou, China（教育部健康智能感知与并行数字人工程研究中心，广州，中国）
University of California Santa Barbara（加州大学圣巴巴拉分校）
Data-to-text generation models face challenges in ensuring data fidelity by referring to the correct input source. To inspire studies in this area, Wiseman et al. (2017) introduced the RotoWire corpus on generating NBA game summaries from the box- and line-score tables. However, limited attempts have been made in this direction and the challenges remain. We observe a prominent bottleneck in the corpus where only about 60% of the summary contents can be grounded to the boxscore records. Such information deficiency tends to misguide a conditioned language model to produce unconditioned random fa…
Hallucination as a Feature, not a Defect: Evaluating a multi-agent architecture to transform speculative language-model outputs into testable scientific hypotheses
RAL2M: Retrieval Augmented Learning-To-Match Against Hallucination in Compliance-Guaranteed Service Systems
Hong Kong Polytechnic University（香港理工大学）；New York University Shanghai（纽约大学上海分校）；Hong Kong University of Science and Technology（香港科技大学）；Beijing University of Posts and Telecommunications（北京邮电大学）
