Search Results for author: Shane Storks

Found 11 papers, 8 papers with code

Can Foundation Models Watch, Talk and Guide You Step by Step to Make a Cake?

1 code implementation1 Nov 2023 Yuwei Bao, Keunwoo Peter Yu, Yichi Zhang, Shane Storks, Itamar Bar-Yossef, Alexander De La Iglesia, Megan Su, Xiao Lin Zheng, Joyce Chai

Despite tremendous advances in AI, it remains a significant challenge to develop interactive task guidance systems that can offer situated, personalized guidance and assist humans in various tasks.

Decision Making

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

1 code implementation24 Oct 2023 Zheyuan Zhang, Shane Storks, Fengyuan Hu, Sungryull Sohn, Moontae Lee, Honglak Lee, Joyce Chai

We incorporate these interlinked dual processes in fine-tuning and in-context learning with PLMs, applying them to two language understanding tasks that require coherent physical commonsense reasoning.

In-Context Learning Physical Commonsense Reasoning

In-Context Analogical Reasoning with Pre-Trained Language Models

1 code implementation28 May 2023 Xiaoyang Hu, Shane Storks, Richard L. Lewis, Joyce Chai

Analogical reasoning is a fundamental capacity of human cognition that allows us to reason abstractly about novel situations by relating them to past experiences.

In-Context Learning Relational Reasoning

NLP Reproducibility For All: Understanding Experiences of Beginners

3 code implementations26 May 2023 Shane Storks, Keunwoo Peter Yu, Ziqiao Ma, Joyce Chai

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 reproducibility efforts are sufficient for this group of beginners to apply the latest developments.

Reproducibility Beyond the Research Community: Experience from NLP Beginners

no code implementations4 May 2022 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.

Best of Both Worlds: A Hybrid Approach for Multi-Hop Explanation with Declarative Facts

no code implementations AAAI Workshop CLeaR 2022 Shane Storks, Qiaozi Gao, Aishwarya Reganti, Govind Thattai

Language-enabled AI systems can answer complex, multi-hop questions to high accuracy, but supporting answers with evidence is a more challenging task which is important for the transparency and trustworthiness to users.

Explanation Generation Retrieval

Tiered Reasoning for Intuitive Physics: Toward Verifiable Commonsense Language Understanding

1 code implementation Findings (EMNLP) 2021 Shane Storks, Qiaozi Gao, Yichi Zhang, Joyce Chai

However, evaluations only based on end task performance shed little light on machines' true ability in language understanding and reasoning.

valid

Beyond the Tip of the Iceberg: Assessing Coherence of Text Classifiers

1 code implementation Findings (EMNLP) 2021 Shane Storks, Joyce Chai

As large-scale, pre-trained language models achieve human-level and superhuman accuracy on existing language understanding tasks, statistical bias in benchmark data and probing studies have recently called into question their true capabilities.

text-classification Text Classification

Are We There Yet? Learning to Localize in Embodied Instruction Following

no code implementations9 Jan 2021 Shane Storks, Qiaozi Gao, Govind Thattai, Gokhan Tur

Embodied instruction following is a challenging problem requiring an agent to infer a sequence of primitive actions to achieve a goal environment state from complex language and visual inputs.

Instruction Following object-detection +1

Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and Approaches

2 code implementations2 Apr 2019 Shane Storks, Qiaozi Gao, Joyce Y. Chai

In the NLP community, recent years have seen a surge of research activities that address machines' ability to perform deep language understanding which goes beyond what is explicitly stated in text, rather relying on reasoning and knowledge of the world.

Natural Language Inference

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