Search Results for author:
Found 14 papers, 6 papers with code
GRILLBot: A multi-modal conversational agent for complex real-world tasks
We present GRILLBot, an open-source multi-modal task-oriented voice assistant to help users perform complex tasks, focusing on the domains of cooking and home improvement.
Open Assistant Toolkit -- version 2
We present the second version of the Open Assistant Toolkit (OAT-v2), an open-source task-oriented conversational system for composing generative neural models.
GRILLBot In Practice: Lessons and Tradeoffs Deploying Large Language Models for Adaptable Conversational Task Assistants
We tackle the challenge of building real-world multimodal assistants for complex real-world tasks.
DREQ: Document Re-Ranking Using Entity-based Query Understanding
While entity-oriented neural IR models have advanced significantly, they often overlook a key nuance: the varying degrees of influence individual entities within a document have on its overall relevance.
Adaptive Latent Entity Expansion for Document Retrieval
First, we demonstrate that applying a strong neural re-ranker before sparse or dense PRF can improve the retrieval effectiveness by 5-8%.
GRM: Generative Relevance Modeling Using Relevance-Aware Sample Estimation for Document Retrieval
Recent studies show that Generative Relevance Feedback (GRF), using text generated by Large Language Models (LLMs), can enhance the effectiveness of query expansion.
Generative and Pseudo-Relevant Feedback for Sparse, Dense and Learned Sparse Retrieval
Pseudo-relevance feedback (PRF) is a classical approach to address lexical mismatch by enriching the query using first-pass retrieval.
Generative Relevance Feedback with Large Language Models
Current query expansion models use pseudo-relevance feedback to improve first-pass retrieval effectiveness; however, this fails when the initial results are not relevant.
DocuT5: Seq2seq SQL Generation with Table Documentation
We perform experiments on the Spider family of datasets that contain complex questions that are cross-domain and multi-table.
Query-Specific Knowledge Graphs for Complex Finance Topics
This workshop paper discusses automating the construction of query-specific document and entity knowledge graphs (KGs) for complex research topics.
GRILLBot: An Assistant for Real-World Tasks with Neural Semantic Parsing and Graph-Based Representations
GRILLBot is the winning system in the 2022 Alexa Prize TaskBot Challenge, moving towards the next generation of multimodal task assistants.
VILT: Video Instructions Linking for Complex Tasks
This work addresses challenges in developing conversational assistants that support rich multimodal video interactions to accomplish real-world tasks interactively.
CODEC: Complex Document and Entity Collection
We also show that the manual query reformulations significantly improve document ranking and entity ranking performance.
How Deep is your Learning: the DL-HARD Annotated Deep Learning Dataset
Deep Learning Hard (DL-HARD) is a new annotated dataset designed to more effectively evaluate neural ranking models on complex topics.
