23 citations · 58 across the 10 of their papers we have counts for
21 papers · 1 filter
Breakpoint Transformers for Modeling and Tracking Intermediate Beliefs
Kyle Richardson, Ronen Tamari, Oren Sultan +3
Can we teach natural language understanding models to track their beliefs through intermediate points in text? We propose a representation learning framework called breakpoint mode…
Learning to Decompose: Hypothetical Question Decomposition Based on Comparable Texts
Ben Zhou, Kyle Richardson, Xiaodong Yu +1
Explicit decomposition modeling, which involves breaking down complex tasks into more straightforward and often more interpretable sub-tasks, has long been a central theme in devel…
What Makes Instruction Learning Hard? An Investigation and a New Challenge in a Synthetic Environment
Matthew Finlayson, Kyle Richardson, Ashish Sabharwal +1
The instruction learning paradigm -- where a model learns to perform new tasks from task descriptions alone -- has become popular in general-purpose model research. The capabilitie…
Investigating Transfer Learning in Multilingual Pre-trained Language Models through Chinese Natural Language Inference
Hai Hu, He Zhou, Zuoyu Tian +5
Multilingual transformers (XLM, mT5) have been shown to have remarkable transfer skills in zero-shot settings. Most transfer studies, however, rely on automatically translated reso…
Thinking Aloud: Dynamic Context Generation Improves Zero-Shot Reasoning Performance of GPT-2
Gregor Betz, Kyle Richardson, Christian Voigt
Thinking aloud is an effective meta-cognitive strategy human reasoners apply to solve difficult problems. We suggest to improve the reasoning ability of pre-trained neural language…
Think you have Solved Direct-Answer Question Answering? Try ARC-DA, the Direct-Answer AI2 Reasoning Challenge
Sumithra Bhakthavatsalam, Daniel Khashabi, Tushar Khot +6
We present the ARC-DA dataset, a direct-answer ("open response", "freeform") version of the ARC (AI2 Reasoning Challenge) multiple-choice dataset. While ARC has been influential in…