Search Results for author:
Found 20 papers, 8 papers with code
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.
Controllable Chest X-Ray Report Generation from Longitudinal Representations
Previous approaches to automated radiology reporting generally do not provide the prior study as input, precluding comparison which is required for clinical accuracy in some types of scans, and offer only unreliable methods of interpretability.
Finding-Aware Anatomical Tokens for Chest X-Ray Automated Reporting
Automated approaches to radiology reporting require the image to be encoded into a suitable token representation for input to the language model.
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.
Exploiting Simulated User Feedback for Conversational Search: Ranking, Rewriting, and Beyond
To this end, we propose a user simulator-based framework for multi-turn interactions with a variety of mixed-initiative CS systems.
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.
Generate, Transform, Answer: Question Specific Tool Synthesis for Tabular Data
Tabular question answering (TQA) presents a challenging setting for neural systems by requiring joint reasoning of natural language with large amounts of semi-structured data.
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.
Induced Natural Language Rationales and Interleaved Markup Tokens Enable Extrapolation in Large Language Models
Our experimental results show that generating step-by-step rationales and introducing marker tokens are both required for effective extrapolation.
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.
Building and Evaluating Open-Domain Dialogue Corpora with Clarifying Questions
Enabling open-domain dialogue systems to ask clarifying questions when appropriate is an important direction for improving the quality of the system response.
CEQE: Contextualized Embeddings for Query Expansion
We find that CEQE outperforms static embedding-based expansion methods on multiple collections (by up to 18% on Robust and 31% on Deep Learning on average precision) and also improves over proven probabilistic pseudo-relevance feedback (PRF) models.
Relevance Transformer: Generating Concise Code Snippets with Relevance Feedback
Tools capable of automatic code generation have the potential to augment programmer's capabilities.
TREC CAsT 2019: The Conversational Assistance Track Overview
A common theme through the runs is the use of BERT-based neural reranking methods.
