1.1k citations · 1.2k across the 10 of their papers we have counts for
14 papers
Can Machines Learn Morality? The Delphi Experiment
Liwei Jiang, Jena D. Hwang, Chandra Bhagavatula +12
As AI systems become increasingly powerful and pervasive, there are growing concerns about machines' morality or a lack thereof. Yet, teaching morality to machines is a formidable…
Is GPT-3 Text Indistinguishable from Human Text? Scarecrow: A Framework for Scrutinizing Machine Text
Yao Dou, Maxwell Forbes, Rik Koncel-Kedziorski +2
Modern neural language models can produce remarkably fluent and grammatical text. So much, in fact, that recent work by Clark et al. (2021) has reported that conventional crowdsour…
CLIPScore: A Reference-free Evaluation Metric for Image Captioning
Jack Hessel, Ari Holtzman, Maxwell Forbes +2
Image captioning has conventionally relied on reference-based automatic evaluations, where machine captions are compared against captions written by humans. This is in contrast to…
MultiTalk: A Highly-Branching Dialog Testbed for Diverse Conversations
Yao Dou, Maxwell Forbes, Ari Holtzman +1
We study conversational dialog in which there are many possible responses to a given history. We present the MultiTalk Dataset, a corpus of over 320,000 sentences of written conver…
Moral Stories: Situated Reasoning about Norms, Intents, Actions, and their Consequences
Denis Emelin, Ronan Le Bras, Jena D. Hwang +2
In social settings, much of human behavior is governed by unspoken rules of conduct. For artificial systems to be fully integrated into social environments, adherence to such norms…
Edited Media Understanding Frames: Reasoning About the Intent and Implications of Visual Misinformation
Jeff Da, Maxwell Forbes, Rowan Zellers +4
Multimodal disinformation, from 'deepfakes' to simple edits that deceive, is an important societal problem. Yet at the same time, the vast majority of media edits are harmless -- s…
Social Chemistry 101: Learning to Reason about Social and Moral Norms
Maxwell Forbes, Jena D. Hwang, Vered Shwartz +2
Social norms -- the unspoken commonsense rules about acceptable social behavior -- are crucial in understanding the underlying causes and intents of people's actions in narratives.…
Paragraph-level Commonsense Transformers with Recurrent Memory
Saadia Gabriel, Chandra Bhagavatula, Vered Shwartz +3
Human understanding of narrative texts requires making commonsense inferences beyond what is stated explicitly in the text. A recent model, COMET, can generate such implicit common…
Neural Naturalist: Generating Fine-Grained Image Comparisons
Maxwell Forbes, Christine Kaeser-Chen, Piyush Sharma +1
We introduce the new Birds-to-Words dataset of 41k sentences describing fine-grained differences between photographs of birds. The language collected is highly detailed, while rema…
Do Neural Language Representations Learn Physical Commonsense?
Maxwell Forbes, Ari Holtzman, Yejin Choi
Humans understand language based on the rich background knowledge about how the physical world works, which in turn allows us to reason about the physical world through language. I…
The Curious Case of Neural Text Degeneration
Ari Holtzman, Jan Buys, Li Du +2
Despite considerable advancements with deep neural language models, the enigma of neural text degeneration persists when these models are tested as text generators. The counter-int…
Balancing Shared Autonomy with Human-Robot Communication
Rosario Scalise, Yonatan Bisk, Maxwell Forbes +3
Robotic agents that share autonomy with a human should leverage human domain knowledge and account for their preferences when completing a task. This extra knowledge can dramatical…
Learning to Write with Cooperative Discriminators
Ari Holtzman, Jan Buys, Maxwell Forbes +3
Recurrent Neural Networks (RNNs) are powerful autoregressive sequence models, but when used to generate natural language their output tends to be overly generic, repetitive, and se…
Verb Physics: Relative Physical Knowledge of Actions and Objects
Maxwell Forbes, Yejin Choi
Learning commonsense knowledge from natural language text is nontrivial due to reporting bias: people rarely state the obvious, e.g., "My house is bigger than me." However, while r…