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20202024
most citedLanguage Models of Code are Few-Shot Commonsense Learners

5 citations · 9 across the 8 of their papers we have counts for

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cs.CL20241 cited

Towards Implicit Bias Detection and Mitigation in Multi-Agent LLM Interactions

Angana Borah, Rada Mihalcea

As Large Language Models (LLMs) continue to evolve, they are increasingly being employed in numerous studies to simulate societies and execute diverse social tasks. However, LLMs a…

cs.CL20225 cited

Language Models of Code are Few-Shot Commonsense Learners

Aman Madaan, Shuyan Zhou, Uri Alon +2

We address the general task of structured commonsense reasoning: given a natural language input, the goal is to generate a graph such as an event -- or a reasoning-graph. To employ…

cs.CL2021

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…

cs.CL2021

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…

cs.CL20211 cited

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…

cs.CL20212 cited

Meta Back-translation

Hieu Pham, Xinyi Wang, Yiming Yang +1

Back-translation is an effective strategy to improve the performance of Neural Machine Translation~(NMT) by generating pseudo-parallel data. However, several recent works have foun…