Retrieval Augmentation Reduces Hallucination in Conversation
Despite showing increasingly human-like conversational abilities, state-of-the-art dialogue models often suffer from factual incorrectness and hallucination of knowledge (Roller et al., 2020). In this work we explore the use of neural-retrieval-in-the-loop architectures - recently shown to be effective in open-domain QA (Lewis et al., 2020b; Izacard and Grave, 2020) - for knowledge-grounded dialogue, a task that is arguably more challenging as it requires querying based on complex multi-turn dialogue context and generating conversationally coherent responses. We study various types of architectures with multiple components - retrievers, rankers, and encoder-decoders - with the goal of maximizing knowledgeability while retaining conversational ability. We demonstrate that our best models obtain state-of-the-art performance on two knowledge-grounded conversational tasks. The models exhibit open-domain conversational capabilities, generalize effectively to scenarios not within the training data, and, as verified by human evaluations, substantially reduce the well-known problem of knowledge hallucination in state-of-the-art chatbots.
Code (0)
등록된 구현이 없습니다.
Tasks
HallucinationRetrievalSimilar Papers 제목 키워드 기반
ReGeS: Reciprocal Retrieval-Generation Synergy for Conversational Recommender Systems
Connecting conversation with external domain knowledge is vital for conversational recommender systems (CRS) to correctly understand user preferences. However, existing solutions either require domain-specific engineerin…
Generalizing Conversational Dense Retrieval via LLM-Cognition Data Augmentation
Conversational search utilizes muli-turn natural language contexts to retrieve relevant passages. Existing conversational dense retrieval models mostly view a conversation as a fixed sequence of questions and responses, …
Contrastive LearningConversational SearchData AugmentationRetrievalAlleviating Hallucination in Large Vision-Language Models with Active Retrieval Augmentation
Despite the remarkable ability of large vision-language models (LVLMs) in image comprehension, these models frequently generate plausible yet factually incorrect responses, a phenomenon known as hallucination.Recently, i…
HallucinationImage ComprehensionLanguage ModellingRetrievalWildHallucinations: Evaluating Long-form Factuality in LLMs with Real-World Entity Queries
While hallucinations of large language models (LLMs) prevail as a major challenge, existing evaluation benchmarks on factuality do not cover the diverse domains of knowledge that the real-world users of LLMs seek informa…
ChatbotFormHallucinationRetrievalContrastive Learning Reduces Hallucination in Conversations
Pre-trained language models (LMs) store knowledge in their parameters and can generate informative responses when used in conversational systems. However, LMs suffer from the problem of "hallucination:" they may generate…
Contrastive LearningHallucination