Search Results for author:
Found 13 papers, 7 papers with code
Retrieving Supporting Evidence for Generative Question Answering
After presenting a question to an LLM and receiving a generated answer, we query the corpus with the combination of the question + generated answer.
Generative Question Answering
Open-Domain Question Answering
+1
Retrieving Supporting Evidence for LLMs Generated Answers
After presenting a question to an LLM and receiving a generated answer, we query the corpus with the combination of the question + generated answer.
Query Performance Prediction: From Ad-hoc to Conversational Search
The QPP task is to predict the retrieval quality of a search system for a query without relevance judgments.
Transforming Human-Centered AI Collaboration: Redefining Embodied Agents Capabilities through Interactive Grounded Language Instructions
Human intelligence's adaptability is remarkable, allowing us to adjust to new tasks and multi-modal environments swiftly.
Collecting Interactive Multi-modal Datasets for Grounded Language Understanding
Human intelligence can remarkably adapt quickly to new tasks and environments.
Learning to Solve Voxel Building Embodied Tasks from Pixels and Natural Language Instructions
The adoption of pre-trained language models to generate action plans for embodied agents is a promising research strategy.
Early Stage Sparse Retrieval with Entity Linking
In this work, we propose boosting the performance of sparse retrievers by expanding both the queries and the documents with linked entities in two formats for the entity names: 1) explicit and 2) hashed.
Unsupervised Question Clarity Prediction Through Retrieved Item Coherency
While the research community has paid substantial attention to the problem of predicting query ambiguity in traditional search contexts, researchers have paid relatively little attention to predicting when this ambiguity is sufficient to warrant clarification in the context of conversational systems.
IGLU 2022: Interactive Grounded Language Understanding in a Collaborative Environment at NeurIPS 2022
Starting from a very young age, humans acquire new skills and learn how to solve new tasks either by imitating the behavior of others or by following provided natural language instructions.
PREME: Preference-based Meeting Exploration through an Interactive Questionnaire
The recent increase in the volume of online meetings necessitates automated tools for managing and organizing the material, especially when an attendee has missed the discussion and needs assistance in quickly exploring it.
Making Large Language Models Interactive: A Pioneer Study on Supporting Complex Information-Seeking Tasks with Implicit Constraints
Current interactive systems with natural language interfaces lack the ability to understand a complex information-seeking request which expresses several implicit constraints at once, and there is no prior information about user preferences e. g.,"find hiking trails around San Francisco which are accessible with toddlers and have beautiful scenery in summer", where output is a list of possible suggestions for users to start their exploration.
Predicting Efficiency/Effectiveness Trade-offs for Dense vs. Sparse Retrieval Strategy Selection
These hybrid retrievers leverage low-cost, exact-matching based sparse retrievers along with dense retrievers to bridge the semantic gaps between query and documents.
Shallow pooling for sparse labels
To test this observation, we employed crowdsourced workers to make preference judgments between the top item returned by a modern neural ranking stack and a judged relevant item.
