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20152022
most citedLearn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering

215 citations · 297 across the 17 of their papers we have counts for

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Showing 2022Show all

5 papers · 1 filter

cs.CL20221 cited

Just-DREAM-about-it: Figurative Language Understanding with DREAM-FLUTE

Yuling Gu, Yao Fu, Valentina Pyatkin +3

Figurative language (e.g., "he flew like the wind") is challenging to understand, as it is hard to tell what implicit information is being conveyed from the surface form alone. We…

cs.AI20221 cited

Entailer: Answering Questions with Faithful and Truthful Chains of Reasoning

Oyvind Tafjord, Bhavana Dalvi Mishra, Peter Clark

Our goal is a question-answering (QA) system that can show how its answers are implied by its own internal beliefs via a systematic chain of reasoning. Such a capability would allo…

cs.CL2022215 cited

Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering

Pan Lu, Swaroop Mishra, Tony Xia +6

When answering a question, humans utilize the information available across different modalities to synthesize a consistent and complete chain of thought (CoT). This process is norm…

cs.CL2022

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…

cs.CL20226 cited

NumGLUE: A Suite of Fundamental yet Challenging Mathematical Reasoning Tasks

Swaroop Mishra, Arindam Mitra, Neeraj Varshney +4

Given the ubiquitous nature of numbers in text, reasoning with numbers to perform simple calculations is an important skill of AI systems. While many datasets and models have been…