Search Results for author:
Found 12 papers, 6 papers with code
Simulating Task-Oriented Dialogues with State Transition Graphs and Large Language Models
In our experiments, using graph-guided response simulations leads to significant improvements in intent classification, slot filling and response relevance compared to naive single-prompt simulated conversations.
Conversational Question Answering
Dialogue State Tracking
+8
Planning Ahead in Generative Retrieval: Guiding Autoregressive Generation through Simultaneous Decoding
This paper introduces PAG-a novel optimization and decoding approach that guides autoregressive generation of document identifiers in generative retrieval models through simultaneous decoding.
Towards Unified Multi-Modal Personalization: Large Vision-Language Models for Generative Recommendation and Beyond
Developing a universal model that can effectively harness heterogeneous resources and respond to a wide range of personalized needs has been a longstanding community aspiration.
Scalable and Effective Generative Information Retrieval
This paper represents an important milestone in generative retrieval research by showing, for the first time, that generative retrieval models can be trained to perform effectively on large-scale standard retrieval benchmarks.
Topic Segmentation of Semi-Structured and Unstructured Conversational Datasets using Language Models
However, current works on topic segmentation often focus on segmentation of structured texts.
Language Models As Semantic Indexers
Semantic identifier (ID) is an important concept in information retrieval that aims to preserve the semantics of objects such as documents and items inside their IDs.
Soft Prompt Decoding for Multilingual Dense Retrieval
In this work, we explore a Multilingual Information Retrieval (MLIR) task, where the collection includes documents in multiple languages.
Cross-Lingual Information Retrieval
Knowledge Distillation
+1
A Personalized Dense Retrieval Framework for Unified Information Access
Developing a universal model that can efficiently and effectively respond to a wide range of information access requests -- from retrieval to recommendation to question answering -- has been a long-lasting goal in the information retrieval community.
Curriculum Learning for Dense Retrieval Distillation
Recent work has shown that more effective dense retrieval models can be obtained by distilling ranking knowledge from an existing base re-ranking model.
Understanding the Effectiveness of Reviews in E-commerce Top-N Recommendation
We find that models utilizing only review information can not achieve better performances than vanilla implicit-feedback matrix factorization method.
A Zero Attentive Relevance Matching Networkfor Review Modeling in Recommendation System
User and item reviews are valuable for the construction of recommender systems.
A Hierarchical Self-attentive Convolution Network for Review Modeling in Recommendation Systems
Using reviews to learn user and item representations is important for recommender system.
