Search Results for author: Chandra Bhagavatula

Found 44 papers, 26 papers with code

proScript: Partially Ordered Scripts Generation

no code implementations Findings (EMNLP) 2021 Keisuke Sakaguchi, Chandra Bhagavatula, Ronan Le Bras, Niket Tandon, Peter Clark, Yejin Choi

Scripts – prototypical event sequences describing everyday activities – have been shown to help understand narratives by providing expectations, resolving ambiguity, and filling in unstated information.

Text Generation

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

1 code implementation2 Sep 2023 Taylor Sorensen, Liwei Jiang, Jena Hwang, Sydney Levine, Valentina Pyatkin, Peter West, Nouha Dziri, Ximing Lu, Kavel Rao, Chandra Bhagavatula, Maarten Sap, John Tasioulas, Yejin Choi

To improve AI systems to better reflect value pluralism, the first-order challenge is to explore the extent to which AI systems can model pluralistic human values, rights, and duties as well as their interaction.

Decision Making

Faith and Fate: Limits of Transformers on Compositionality

no code implementations29 May 2023 Nouha Dziri, Ximing Lu, Melanie Sclar, Xiang Lorraine Li, Liwei Jiang, Bill Yuchen Lin, Peter West, Chandra Bhagavatula, Ronan Le Bras, Jena D. Hwang, Soumya Sanyal, Sean Welleck, Xiang Ren, Allyson Ettinger, Zaid Harchaoui, Yejin Choi

We formulate compositional tasks as computation graphs to systematically quantify the level of complexity, and break down reasoning steps into intermediate sub-procedures.

SwiftSage: A Generative Agent with Fast and Slow Thinking for Complex Interactive Tasks

1 code implementation27 May 2023 Bill Yuchen Lin, Yicheng Fu, Karina Yang, Prithviraj Ammanabrolu, Faeze Brahman, Shiyu Huang, Chandra Bhagavatula, Yejin Choi, Xiang Ren

The Swift module is a small encoder-decoder LM fine-tuned on the oracle agent's action trajectories, while the Sage module employs LLMs such as GPT-4 for subgoal planning and grounding.

I2D2: Inductive Knowledge Distillation with NeuroLogic and Self-Imitation

no code implementations19 Dec 2022 Chandra Bhagavatula, Jena D. Hwang, Doug Downey, Ronan Le Bras, Ximing Lu, Lianhui Qin, Keisuke Sakaguchi, Swabha Swayamdipta, Peter West, Yejin Choi

Here, we investigate an alternative that a priori seems impossible: can smaller language models (e. g., GPT-2) win over models that are orders of magnitude larger and better (e. g., GPT-3), if powered with novel commonsense distillation algorithms?

Imitation Learning Knowledge Distillation

GEMv2: Multilingual NLG Benchmarking in a Single Line of Code

no code implementations22 Jun 2022 Sebastian Gehrmann, Abhik Bhattacharjee, Abinaya Mahendiran, Alex Wang, Alexandros Papangelis, Aman Madaan, Angelina McMillan-Major, Anna Shvets, Ashish Upadhyay, Bingsheng Yao, Bryan Wilie, Chandra Bhagavatula, Chaobin You, Craig Thomson, Cristina Garbacea, Dakuo Wang, Daniel Deutsch, Deyi Xiong, Di Jin, Dimitra Gkatzia, Dragomir Radev, Elizabeth Clark, Esin Durmus, Faisal Ladhak, Filip Ginter, Genta Indra Winata, Hendrik Strobelt, Hiroaki Hayashi, Jekaterina Novikova, Jenna Kanerva, Jenny Chim, Jiawei Zhou, Jordan Clive, Joshua Maynez, João Sedoc, Juraj Juraska, Kaustubh Dhole, Khyathi Raghavi Chandu, Laura Perez-Beltrachini, Leonardo F. R. Ribeiro, Lewis Tunstall, Li Zhang, Mahima Pushkarna, Mathias Creutz, Michael White, Mihir Sanjay Kale, Moussa Kamal Eddine, Nico Daheim, Nishant Subramani, Ondrej Dusek, Paul Pu Liang, Pawan Sasanka Ammanamanchi, Qi Zhu, Ratish Puduppully, Reno Kriz, Rifat Shahriyar, Ronald Cardenas, Saad Mahamood, Salomey Osei, Samuel Cahyawijaya, Sanja Štajner, Sebastien Montella, Shailza, Shailza Jolly, Simon Mille, Tahmid Hasan, Tianhao Shen, Tosin Adewumi, Vikas Raunak, Vipul Raheja, Vitaly Nikolaev, Vivian Tsai, Yacine Jernite, Ying Xu, Yisi Sang, Yixin Liu, Yufang Hou

This problem is especially pertinent in natural language generation which requires ever-improving suites of datasets, metrics, and human evaluation to make definitive claims.

Benchmarking Text Generation

Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanations

no code implementations24 May 2022 JaeHun Jung, Lianhui Qin, Sean Welleck, Faeze Brahman, Chandra Bhagavatula, Ronan Le Bras, Yejin Choi

Despite their impressive capabilities, large pre-trained language models (LMs) struggle with consistent reasoning; recently, prompting LMs to generate explanations that self-guide the inference has emerged as a promising direction to amend this.

Penguins Don't Fly: Reasoning about Generics through Instantiations and Exceptions

no code implementations23 May 2022 Emily Allaway, Jena D. Hwang, Chandra Bhagavatula, Kathleen McKeown, Doug Downey, Yejin Choi

Generics express generalizations about the world (e. g., birds can fly) that are not universally true (e. g., newborn birds and penguins cannot fly).

Natural Language Inference

The Abduction of Sherlock Holmes: A Dataset for Visual Abductive Reasoning

no code implementations10 Feb 2022 Jack Hessel, Jena D. Hwang, Jae Sung Park, Rowan Zellers, Chandra Bhagavatula, Anna Rohrbach, Kate Saenko, Yejin Choi

We present Sherlock, an annotated corpus of 103K images for testing machine capacity for abductive reasoning beyond literal image contents.

Visual Abductive Reasoning Visual Reasoning

CommonsenseQA 2.0: Exposing the Limits of AI through Gamification

no code implementations14 Jan 2022 Alon Talmor, Ori Yoran, Ronan Le Bras, Chandra Bhagavatula, Yoav Goldberg, Yejin Choi, Jonathan Berant

Constructing benchmarks that test the abilities of modern natural language understanding models is difficult - pre-trained language models exploit artifacts in benchmarks to achieve human parity, but still fail on adversarial examples and make errors that demonstrate a lack of common sense.

Common Sense Reasoning Natural Language Understanding

``I'm Not Mad'': Commonsense Implications of Negation and Contradiction

no code implementations NAACL 2021 Liwei Jiang, Antoine Bosselut, Chandra Bhagavatula, Yejin Choi

In this paper, we present the first comprehensive study focusing on commonsense implications of negated statements and contradictions.

Natural Language Inference

proScript: Partially Ordered Scripts Generation via Pre-trained Language Models

no code implementations16 Apr 2021 Keisuke Sakaguchi, Chandra Bhagavatula, Ronan Le Bras, Niket Tandon, Peter Clark, Yejin Choi

Scripts - standardized event sequences describing typical everyday activities - have been shown to help understand narratives by providing expectations, resolving ambiguity, and filling in unstated information.

Text Generation

"I'm Not Mad": Commonsense Implications of Negation and Contradiction

no code implementations13 Apr 2021 Liwei Jiang, Antoine Bosselut, Chandra Bhagavatula, Yejin Choi

In this paper, we present the first comprehensive study focusing on commonsense implications of negated statements and contradictions.

Natural Language Inference

UNICORN on RAINBOW: A Universal Commonsense Reasoning Model on a New Multitask Benchmark

1 code implementation24 Mar 2021 Nicholas Lourie, Ronan Le Bras, Chandra Bhagavatula, Yejin Choi

First, we propose a new multitask benchmark, RAINBOW, to promote research on commonsense models that generalize well over multiple tasks and datasets.

Knowledge Graphs Transfer Learning

On-the-Fly Attention Modulation for Neural Generation

no code implementations Findings (ACL) 2021 Yue Dong, Chandra Bhagavatula, Ximing Lu, Jena D. Hwang, Antoine Bosselut, Jackie Chi Kit Cheung, Yejin Choi

Despite considerable advancements with deep neural language models (LMs), neural text generation still suffers from degeneration: the generated text is repetitive, generic, self-contradictory, and often lacks commonsense.

Language Modelling Text Generation

NeuroLogic Decoding: (Un)supervised Neural Text Generation with Predicate Logic Constraints

no code implementations NAACL 2021 Ximing Lu, Peter West, Rowan Zellers, Ronan Le Bras, Chandra Bhagavatula, Yejin Choi

While the dominant recipe for conditional text generation has been large-scale pretrained language models that are finetuned on the task-specific training data, such models do not learn to follow the underlying constraints reliably, even when supervised with large amounts of task-specific examples.

Conditional Text Generation

Reflective Decoding: Beyond Unidirectional Generation with Off-the-Shelf Language Models

no code implementations ACL 2021 Peter West, Ximing Lu, Ari Holtzman, Chandra Bhagavatula, Jena Hwang, Yejin Choi

In this paper, we present Reflective Decoding, a novel unsupervised algorithm that allows for direct application of unidirectional LMs to non-sequential tasks.

Conditional Text Generation Text Infilling

Natural Language Rationales with Full-Stack Visual Reasoning: From Pixels to Semantic Frames to Commonsense Graphs

1 code implementation Findings of the Association for Computational Linguistics 2020 Ana Marasović, Chandra Bhagavatula, Jae Sung Park, Ronan Le Bras, Noah A. Smith, Yejin Choi

Natural language rationales could provide intuitive, higher-level explanations that are easily understandable by humans, complementing the more broadly studied lower-level explanations based on gradients or attention weights.

Language Modelling Natural Language Inference +4

Back to the Future: Unsupervised Backprop-based Decoding for Counterfactual and Abductive Commonsense Reasoning

1 code implementation EMNLP 2020 Lianhui Qin, Vered Shwartz, Peter West, Chandra Bhagavatula, Jena Hwang, Ronan Le Bras, Antoine Bosselut, Yejin Choi

Abductive and counterfactual reasoning, core abilities of everyday human cognition, require reasoning about what might have happened at time t, while conditioning on multiple contexts from the relative past and future.

Text Infilling

COMET-ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphs

3 code implementations12 Oct 2020 Jena D. Hwang, Chandra Bhagavatula, Ronan Le Bras, Jeff Da, Keisuke Sakaguchi, Antoine Bosselut, Yejin Choi

Next, we show that ATOMIC 2020 is better suited for training knowledge models that can generate accurate, representative knowledge for new, unseen entities and events.

Knowledge Graphs Natural Language Understanding

Paragraph-level Commonsense Transformers with Recurrent Memory

1 code implementation4 Oct 2020 Saadia Gabriel, Chandra Bhagavatula, Vered Shwartz, Ronan Le Bras, Maxwell Forbes, Yejin Choi

Human understanding of narrative texts requires making commonsense inferences beyond what is stated explicitly in the text.

VisualCOMET: Reasoning about the Dynamic Context of a Still Image

no code implementations ECCV 2020 Jae Sung Park, Chandra Bhagavatula, Roozbeh Mottaghi, Ali Farhadi, Yejin Choi

In addition, we provide person-grounding (i. e., co-reference links) between people appearing in the image and people mentioned in the textual commonsense descriptions, allowing for tighter integration between images and text.

Visual Commonsense Reasoning

Adversarial Filters of Dataset Biases

1 code implementation ICML 2020 Ronan Le Bras, Swabha Swayamdipta, Chandra Bhagavatula, Rowan Zellers, Matthew E. Peters, Ashish Sabharwal, Yejin Choi

Large neural models have demonstrated human-level performance on language and vision benchmarks, while their performance degrades considerably on adversarial or out-of-distribution samples.

Natural Language Inference

Commonsense Knowledge Base Completion with Structural and Semantic Context

1 code implementation7 Oct 2019 Chaitanya Malaviya, Chandra Bhagavatula, Antoine Bosselut, Yejin Choi

Our results demonstrate the effectiveness of language model representations in boosting link prediction performance and the advantages of learning from local graph structure (+1. 5 points in MRR for ConceptNet) when training on subgraphs for computational efficiency.

Knowledge Base Completion Knowledge Graphs +3

Counterfactual Story Reasoning and Generation

1 code implementation IJCNLP 2019 Lianhui Qin, Antoine Bosselut, Ari Holtzman, Chandra Bhagavatula, Elizabeth Clark, Yejin Choi

Counterfactual reasoning requires predicting how alternative events, contrary to what actually happened, might have resulted in different outcomes.

Text Generation

Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning

no code implementations IJCNLP 2019 Lifu Huang, Ronan Le Bras, Chandra Bhagavatula, Yejin Choi

In this paper, we introduce Cosmos QA, a large-scale dataset of 35, 600 problems that require commonsense-based reading comprehension, formulated as multiple-choice questions.

Machine Reading Comprehension Multiple-choice

WinoGrande: An Adversarial Winograd Schema Challenge at Scale

3 code implementations24 Jul 2019 Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, Yejin Choi

The key steps of the dataset construction consist of (1) a carefully designed crowdsourcing procedure, followed by (2) systematic bias reduction using a novel AfLite algorithm that generalizes human-detectable word associations to machine-detectable embedding associations.

Transfer Learning

ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning

2 code implementations31 Oct 2018 Maarten Sap, Ronan LeBras, Emily Allaway, Chandra Bhagavatula, Nicholas Lourie, Hannah Rashkin, Brendan Roof, Noah A. Smith, Yejin Choi

We present ATOMIC, an atlas of everyday commonsense reasoning, organized through 877k textual descriptions of inferential knowledge.

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