24 citations · 118 across the 25 of their papers we have counts for
30 papers
Chain-of-Skills: A Configurable Model for Open-domain Question Answering
Kaixin Ma, Hao Cheng, Yu Zhang +3
The retrieval model is an indispensable component for real-world knowledge-intensive tasks, e.g., open-domain question answering (ODQA). As separate retrieval skills are annotated…
InPars-Light: Cost-Effective Unsupervised Training of Efficient Rankers
Leonid Boytsov, Preksha Patel, Vivek Sourabh +4
We carried out a reproducibility study of InPars, which is a method for unsupervised training of neural rankers (Bonifacio et al., 2022). As a by-product, we developed InPars-light…
Knowledge-driven Scene Priors for Semantic Audio-Visual Embodied Navigation
Gyan Tatiya, Jonathan Francis, Luca Bondi +4
Generalisation to unseen contexts remains a challenge for embodied navigation agents. In the context of semantic audio-visual navigation (SAVi) tasks, the notion of generalisation…
Distribution-aware Goal Prediction and Conformant Model-based Planning for Safe Autonomous Driving
Jonathan Francis, Bingqing Chen, Weiran Yao +2
The feasibility of collecting a large amount of expert demonstrations has inspired growing research interests in learning-to-drive settings, where models learn by imitating the dri…
Open-domain Question Answering via Chain of Reasoning over Heterogeneous Knowledge
Kaixin Ma, Hao Cheng, Xiaodong Liu +2
We propose a novel open-domain question answering (ODQA) framework for answering single/multi-hop questions across heterogeneous knowledge sources. The key novelty of our method is…
Coalescing Global and Local Information for Procedural Text Understanding
Kaixin Ma, Filip Ilievski, Jonathan Francis +2
Procedural text understanding is a challenging language reasoning task that requires models to track entity states across the development of a narrative. A complete procedural unde…