Search Results for author:
Found 9 papers, 1 papers with code
Zebra: In-Context and Generative Pretraining for Solving Parametric PDEs
Solving time-dependent parametric partial differential equations (PDEs) is challenging, as models must adapt to variations in parameters such as coefficients, forcing terms, and boundary conditions.
An Evaluation Framework for Attributed Information Retrieval using Large Language Models
With the growing success of Large Language models (LLMs) in information-seeking scenarios, search engines are now adopting generative approaches to provide answers along with in-line citations as attribution.
ACCO: Accumulate while you Communicate, Hiding Communications in Distributed LLM Training
Our method relies on a novel technique to mitigate the one-step delay inherent in parallel execution of gradient computations and communications, eliminating the need for warmup steps and aligning with the training dynamics of standard distributed optimization while converging faster in terms of wall-clock time.
PAQA: Toward ProActive Open-Retrieval Question Answering
Conversational systems have made significant progress in generating natural language responses.
Navigating Uncertainty: Optimizing API Dependency for Hallucination Reduction in Closed-Book Question Answering
Our model directly provides answers for $78. 2\%$ of the known queries and opts to search for $77. 2\%$ of the unknown ones.
Augmenting Ad-Hoc IR Dataset for Interactive Conversational Search
The only two datasets known to us that contain both document relevance judgments and the associated clarification interactions are Qulac and ClariQ.
CIRCLE: Multi-Turn Query Clarifications with Reinforcement Learning
In this paper, we introduce CIRCLE, a generative model for multi-turn query Clarifications wIth ReinforCement LEarning that leverages multi-turn interactions through a user simulation framework.
Interactive Query Clarification and Refinement via User Simulation
When users initiate search sessions, their queries are often unclear or might lack of context; this resulting in inefficient document ranking.
State of the Art of User Simulation approaches for conversational information retrieval
Conversational Information Retrieval (CIR) is an emerging field of Information Retrieval (IR) at the intersection of interactive IR and dialogue systems for open domain information needs.
