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cs.CL2024

Teaching Embodied Reinforcement Learning Agents: Informativeness and Diversity of Language Use

Jiajun Xi, Yinong He, Jianing Yang +2

In real-world scenarios, it is desirable for embodied agents to have the ability to leverage human language to gain explicit or implicit knowledge for learning tasks. Despite recen…

cs.CL20231 cited

MetaReVision: Meta-Learning with Retrieval for Visually Grounded Compositional Concept Acquisition

Guangyue Xu, Parisa Kordjamshidi, Joyce Chai

Humans have the ability to learn novel compositional concepts by recalling and generalizing primitive concepts acquired from past experiences. Inspired by this observation, in this…

cs.CL2023

From Heuristic to Analytic: Cognitively Motivated Strategies for Coherent Physical Commonsense Reasoning

Zheyuan Zhang, Shane Storks, Fengyuan Hu +4

Pre-trained language models (PLMs) have shown impressive performance in various language tasks. However, they are prone to spurious correlations, and often generate illusory inform…

cs.CL2023

NLP Reproducibility For All: Understanding Experiences of Beginners

Shane Storks, Keunwoo Peter Yu, Ziqiao Ma +1

As natural language processing (NLP) has recently seen an unprecedented level of excitement, and more people are eager to enter the field, it is unclear whether current research re…

cs.CL20232 cited

BAD: BiAs Detection for Large Language Models in the context of candidate screening

Nam Ho Koh, Joseph Plata, Joyce Chai

Application Tracking Systems (ATS) have allowed talent managers, recruiters, and college admissions committees to process large volumes of potential candidate applications efficien…

cs.CL2022

Reproducibility Beyond the Research Community: Experience from NLP Beginners

Shane Storks, Keunwoo Peter Yu, Joyce Chai

As NLP research attracts public attention and excitement, it becomes increasingly important for it to be accessible to a broad audience. As the research community works to democrat…