1.1k citations · 3.7k across the 135 of their papers we have counts for
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MAUVE Scores for Generative Models: Theory and Practice
Krishna Pillutla, Lang Liu, John Thickstun +6
Generative artificial intelligence has made significant strides, producing text indistinguishable from human prose and remarkably photorealistic images. Automatically measuring how…
Detoxifying Text with MaRCo: Controllable Revision with Experts and Anti-Experts
Skyler Hallinan, Alisa Liu, Yejin Choi +1
Text detoxification has the potential to mitigate the harms of toxicity by rephrasing text to remove offensive meaning, but subtle toxicity remains challenging to tackle. We introd…
SODA: Million-scale Dialogue Distillation with Social Commonsense Contextualization
Hyunwoo Kim, Jack Hessel, Liwei Jiang +9
Data scarcity has been a long standing issue in the field of open-domain social dialogue. To quench this thirst, we present SODA: the first publicly available, million-scale high-q…
ClarifyDelphi: Reinforced Clarification Questions with Defeasibility Rewards for Social and Moral Situations
Valentina Pyatkin, Jena D. Hwang, Vivek Srikumar +4
Context is everything, even in commonsense moral reasoning. Changing contexts can flip the moral judgment of an action; "Lying to a friend" is wrong in general, but may be morally…
I2D2: Inductive Knowledge Distillation with NeuroLogic and Self-Imitation
Chandra Bhagavatula, Jena D. Hwang, Doug Downey +7
Commonsense capabilities of pre-trained language models dramatically improve with scale, leading many to believe that scale is the only winning recipe. But is it? Here, we investig…
NaturalAdversaries: Can Naturalistic Adversaries Be as Effective as Artificial Adversaries?
Saadia Gabriel, Hamid Palangi, Yejin Choi
While a substantial body of prior work has explored adversarial example generation for natural language understanding tasks, these examples are often unrealistic and diverge from t…
Generating Sequences by Learning to Self-Correct
Sean Welleck, Ximing Lu, Peter West +4
Sequence generation applications require satisfying semantic constraints, such as ensuring that programs are correct, using certain keywords, or avoiding undesirable content. Langu…
Referee: Reference-Free Sentence Summarization with Sharper Controllability through Symbolic Knowledge Distillation
Melanie Sclar, Peter West, Sachin Kumar +2
We present Referee, a novel framework for sentence summarization that can be trained reference-free (i.e., requiring no gold summaries for supervision), while allowing direct contr…
NeuroCounterfactuals: Beyond Minimal-Edit Counterfactuals for Richer Data Augmentation
Phillip Howard, Gadi Singer, Vasudev Lal +2
While counterfactual data augmentation offers a promising step towards robust generalization in natural language processing, producing a set of counterfactuals that offer valuable…
Rainier: Reinforced Knowledge Introspector for Commonsense Question Answering
Jiacheng Liu, Skyler Hallinan, Ximing Lu +4
Knowledge underpins reasoning. Recent research demonstrates that when relevant knowledge is provided as additional context to commonsense question answering (QA), it can substantia…
Neural Theory-of-Mind? On the Limits of Social Intelligence in Large LMs
Maarten Sap, Ronan LeBras, Daniel Fried +1
Social intelligence and Theory of Mind (ToM), i.e., the ability to reason about the different mental states, intents, and reactions of all people involved, allow humans to effectiv…
REV: Information-Theoretic Evaluation of Free-Text Rationales
Hanjie Chen, Faeze Brahman, Xiang Ren +3
Generating free-text rationales is a promising step towards explainable NLP, yet evaluating such rationales remains a challenge. Existing metrics have mostly focused on measuring t…
Is Reinforcement Learning (Not) for Natural Language Processing: Benchmarks, Baselines, and Building Blocks for Natural Language Policy Optimization
Rajkumar Ramamurthy, Prithviraj Ammanabrolu, Kianté Brantley +5
We tackle the problem of aligning pre-trained large language models (LMs) with human preferences. If we view text generation as a sequential decision-making problem, reinforcement…
Do Androids Laugh at Electric Sheep? Humor "Understanding" Benchmarks from The New Yorker Caption Contest
Jack Hessel, Ana Marasović, Jena D. Hwang +5
Large neural networks can now generate jokes, but do they really "understand" humor? We challenge AI models with three tasks derived from the New Yorker Cartoon Caption Contest: ma…
RealTime QA: What's the Answer Right Now?
Jungo Kasai, Keisuke Sakaguchi, Yoichi Takahashi +7
We introduce REALTIME QA, a dynamic question answering (QA) platform that announces questions and evaluates systems on a regular basis (weekly in this version). REALTIME QA inquire…
Multimodal Knowledge Alignment with Reinforcement Learning
Youngjae Yu, Jiwan Chung, Heeseung Yun +8
Large language models readily adapt to novel settings, even without task-specific training data. Can their zero-shot capacity be extended to multimodal inputs? In this work, we pro…
Aligning to Social Norms and Values in Interactive Narratives
Prithviraj Ammanabrolu, Liwei Jiang, Maarten Sap +2
We focus on creating agents that act in alignment with socially beneficial norms and values in interactive narratives or text-based games -- environments wherein an agent perceives…
Quark: Controllable Text Generation with Reinforced Unlearning
Ximing Lu, Sean Welleck, Jack Hessel +5
Large-scale language models often learn behaviors that are misaligned with user expectations. Generated text may contain offensive or toxic language, contain significant repetition…
NaturalProver: Grounded Mathematical Proof Generation with Language Models
Sean Welleck, Jiacheng Liu, Ximing Lu +2
Theorem proving in natural mathematical language - the mixture of symbolic and natural language used by humans - plays a central role in mathematical advances and education, and te…
ProsocialDialog: A Prosocial Backbone for Conversational Agents
Hyunwoo Kim, Youngjae Yu, Liwei Jiang +5
Most existing dialogue systems fail to respond properly to potentially unsafe user utterances by either ignoring or passively agreeing with them. To address this issue, we introduc…
Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanations
Jaehun Jung, Lianhui Qin, Sean Welleck +4
Despite their impressive capabilities, large pre-trained language models (LMs) struggle with consistent reasoning; recently, prompting LMs to generate explanations that self-guide…
Penguins Don't Fly: Reasoning about Generics through Instantiations and Exceptions
Emily Allaway, Jena D. Hwang, Chandra Bhagavatula +3
Generics express generalizations about the world (e.g., birds can fly) that are not universally true (e.g., newborn birds and penguins cannot fly). Commonsense knowledge bases, use…
Twist Decoding: Diverse Generators Guide Each Other
Jungo Kasai, Keisuke Sakaguchi, Ronan Le Bras +5
Many language generation models are now available for a wide range of generation tasks, including machine translation and summarization. Combining such diverse models may lead to f…
Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks
Yizhong Wang, Swaroop Mishra, Pegah Alipoormolabashi +37
How well can NLP models generalize to a variety of unseen tasks when provided with task instructions? To address this question, we first introduce Super-NaturalInstructions, a benc…