How to Leverage Personal Textual Knowledge for Personalized Conversational Information Retrieval
Personalized conversational information retrieval (CIR) combines conversational and personalizable elements to satisfy various users' complex information needs through multi-turn interaction based on their backgrounds. The key promise is that the personal textual knowledge base (PTKB) can improve the CIR effectiveness because the retrieval results can be more related to the user's background. However, PTKB is noisy: not every piece of knowledge in PTKB is relevant to the specific query at hand. In this paper, we explore and test several ways to select knowledge from PTKB and use it for query reformulation by using a large language model (LLM). The experimental results show the PTKB might not always improve the search results when used alone, but LLM can help generate a more appropriate personalized query when high-quality guidance is provided.
Code (1)
Tasks
Information RetrievalLanguage ModelingLanguage ModellingLarge Language ModelRetrievalMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
K-PERM: Personalized Response Generation Using Dynamic Knowledge Retrieval and Persona-Adaptive Queries
Personalizing conversational agents can enhance the quality of conversations and increase user engagement. However, they often lack external knowledge to appropriately tend to a user's persona. This is particularly cruci…
NutritionResponse GenerationRetrievalLeveraging Language Models and Bandit Algorithms to Drive Adoption of Battery-Electric Vehicles
Behavior change interventions are important to coordinate societal action across a wide array of important applications, including the adoption of electrified vehicles to reduce emissions. Prior work has demonstrated tha…
A Personalized Conversational Benchmark: Towards Simulating Personalized Conversations
We present PersonaConvBench, a large-scale benchmark for evaluating personalized reasoning and generation in multi-turn conversations with large language models (LLMs). Unlike existing work that focuses on either persona…
SentenceSentence ClassificationSentiment AnalysisSentiment Classification+1CPED: A Large-Scale Chinese Personalized and Emotional Dialogue Dataset for Conversational AI
Human language expression is based on the subjective construal of the situation instead of the objective truth conditions, which means that speakers' personalities and emotions after cognitive processing have an importan…
Chinese Sentiment AnalysisConversational Response GenerationDialog Act ClassificationDialogue Generation+7Bias in Conversational Search: The Double-Edged Sword of the Personalized Knowledge Graph
Conversational AI systems are being used in personal devices, providing users with highly personalized content. Personalized knowledge graphs (PKGs) are one of the recently proposed methods to store users' information in…
Conversational SearchKnowledge Graphs