activity
20162026
most citedFaith and Fate: Limits of Transformers on Compositionality

71 citations · 177 across the 26 of their papers we have counts for

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Showing 2023Show all

8 papers · 1 filter

cs.CL2023

UNcommonsense Reasoning: Abductive Reasoning about Uncommon Situations

Wenting Zhao, Justin T Chiu, Jena D. Hwang +6

Language technologies that accurately model the dynamics of events must perform commonsense reasoning. Existing work evaluating commonsense reasoning focuses on making inferences a…

cs.AI2023★ 10 cited

The Generative AI Paradox: "What It Can Create, It May Not Understand"

Peter West, Ximing Lu, Nouha Dziri +11

The recent wave of generative AI has sparked unprecedented global attention, with both excitement and concern over potentially superhuman levels of artificial intelligence: models…

cs.CL2023

"You Are An Expert Linguistic Annotator": Limits of LLMs as Analyzers of Abstract Meaning Representation

Allyson Ettinger, Jena D. Hwang, Valentina Pyatkin +2

Large language models (LLMs) show amazing proficiency and fluency in the use of language. Does this mean that they have also acquired insightful linguistic knowledge about the lang…

cs.CL2023★ 31 cited

Value Kaleidoscope: Engaging AI with Pluralistic Human Values, Rights, and Duties

Taylor Sorensen, Liwei Jiang, Jena Hwang +10

Human values are crucial to human decision-making. Value pluralism is the view that multiple correct values may be held in tension with one another (e.g., when considering lying to…

cs.CL2023★ 1 cited

COBRA Frames: Contextual Reasoning about Effects and Harms of Offensive Statements

Xuhui Zhou, Hao Zhu, Akhila Yerukola +4

Warning: This paper contains content that may be offensive or upsetting. Understanding the harms and offensiveness of statements requires reasoning about the social and situational…

cs.CL2023★ 3 cited

PlaSma: Making Small Language Models Better Procedural Knowledge Models for (Counterfactual) Planning

Faeze Brahman, Chandra Bhagavatula, Valentina Pyatkin +7

Procedural planning, which entails decomposing a high-level goal into a sequence of temporally ordered steps, is an important yet intricate task for machines. It involves integrati…

cs.CL2023★ 71 cited

Faith and Fate: Limits of Transformers on Compositionality

Nouha Dziri, Ximing Lu, Melanie Sclar +13

Transformer large language models (LLMs) have sparked admiration for their exceptional performance on tasks that demand intricate multi-step reasoning. Yet, these models simultaneo…

cs.CL2023★ 1 cited

Causal schema induction for knowledge discovery

Michael Regan, Jena D. Hwang, Keisuke Sakaguchi +1

Making sense of familiar yet new situations typically involves making generalizations about causal schemas, stories that help humans reason about event sequences. Reasoning about e…