activity
20192024
most citedFaith and Fate: Limits of Transformers on Compositionality

71 citations · 296 across the 26 of their papers we have counts for

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Showing 2023 · cs.CLShow all

7 papers · 2 filters

cs.CL2023

NovaCOMET: Open Commonsense Foundation Models with Symbolic Knowledge Distillation

Peter West, Ronan Le Bras, Taylor Sorensen +8

We present NovaCOMET, an open commonsense knowledge model, that combines the best aspects of knowledge and general task models. Compared to previous knowledge models, NovaCOMET all…

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

Minding Language Models' (Lack of) Theory of Mind: A Plug-and-Play Multi-Character Belief Tracker

Melanie Sclar, Sachin Kumar, Peter West +3

Theory of Mind (ToM)$\unicode{x2014}$the ability to reason about the mental states of other people$\unicode{x2014}$is a key element of our social intelligence. Yet, despite their e…

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★ 3 cited

Impossible Distillation: from Low-Quality Model to High-Quality Dataset & Model for Summarization and Paraphrasing

Jaehun Jung, Peter West, Liwei Jiang +5

We present Impossible Distillation, a novel framework for paraphrasing and sentence summarization, that distills a high-quality dataset and model from a low-quality teacher that it…

cs.CL2023

Inference-Time Policy Adapters (IPA): Tailoring Extreme-Scale LMs without Fine-tuning

Ximing Lu, Faeze Brahman, Peter West +14

While extreme-scale language models have demonstrated exceptional performance on a variety of language tasks, the degree of control over these language models through pure promptin…

cs.CL2023★ 3 cited

We're Afraid Language Models Aren't Modeling Ambiguity

Alisa Liu, Zhaofeng Wu, Julian Michael +6

Ambiguity is an intrinsic feature of natural language. Managing ambiguity is a key part of human language understanding, allowing us to anticipate misunderstanding as communicators…