Search Results for author:
Found 7 papers, 3 papers with code
Grounding Language Model with Chunking-Free In-Context Retrieval
These strategies not only improve the efficiency of the retrieval process but also ensure that the fidelity of the generated grounding text evidence is maintained.
Optimizing Factual Accuracy in Text Generation through Dynamic Knowledge Selection
Previous methods use external knowledge as references for text generation to enhance factuality but often struggle with the knowledge mix-up(e. g., entity mismatch) of irrelevant references.
Large Language Models Know Your Contextual Search Intent: A Prompting Framework for Conversational Search
Precisely understanding users' contextual search intent has been an important challenge for conversational search.
Less is More: Learning to Refine Dialogue History for Personalized Dialogue Generation
Existing personalized dialogue systems have tried to extract user profiles from dialogue history to guide personalized response generation.
Learning Implicit User Profiles for Personalized Retrieval-Based Chatbot
To learn a user's personalized language style, we elaborately build language models from shallow to deep using the user's historical responses; To model a user's personalized preferences, we explore the conditional relations underneath each post-response pair of the user.
Speaker or Listener? The Role of a Dialog Agent
However, along with the proactive manner introduced into a dialogue agent, an issue arises that, with too many knowledge facts to express, the agent starts to talks endlessly, and even completely ignores what the other expresses in dialogue sometimes, which greatly harms the interest of the other chatter to continue the conversation.
Pchatbot: A Large-Scale Dataset for Personalized Chatbot
This enables the development of personalized dialogue models that directly learn implicit user personality from the user's dialogue history.
