221 citations · 483 across the 30 of their papers we have counts for
7 papers · 1 filter
Interscript: A dataset for interactive learning of scripts through error feedback
Niket Tandon, Aman Madaan, Peter Clark +2
How can an end-user provide feedback if a deployed structured prediction model generates inconsistent output, ignoring the structural complexity of human language? This is an emerg…
Learning to Repair: Repairing model output errors after deployment using a dynamic memory of feedback
Niket Tandon, Aman Madaan, Peter Clark +1
Large language models (LMs), while powerful, are not immune to mistakes, but can be difficult to retrain. Our goal is for an LM to continue to improve after deployment, without ret…
Think about it! Improving defeasible reasoning by first modeling the question scenario
Aman Madaan, Niket Tandon, Dheeraj Rajagopal +3
Defeasible reasoning is the mode of reasoning where conclusions can be overturned by taking into account new evidence. Existing cognitive science literature on defeasible reasoning…
Could you give me a hint? Generating inference graphs for defeasible reasoning
Aman Madaan, Dheeraj Rajagopal, Niket Tandon +2
Defeasible reasoning is the mode of reasoning where conclusions can be overturned by taking into account new evidence. A commonly used method in cognitive science and logic literat…
Improving Neural Model Performance through Natural Language Feedback on Their Explanations
Aman Madaan, Niket Tandon, Dheeraj Rajagopal +4
A class of explainable NLP models for reasoning tasks support their decisions by generating free-form or structured explanations, but what happens when these supporting structures…
CURIE: An Iterative Querying Approach for Reasoning About Situations
Dheeraj Rajagopal, Aman Madaan, Niket Tandon +5
Recently, models have been shown to predict the effects of unexpected situations, e.g., would cloudy skies help or hinder plant growth? Given a context, the goal of such situationa…
The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
Sebastian Gehrmann, Tosin Adewumi, Karmanya Aggarwal +53
We introduce GEM, a living benchmark for natural language Generation (NLG), its Evaluation, and Metrics. Measuring progress in NLG relies on a constantly evolving ecosystem of auto…