Papers by Ronan Le Bras
Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning (D19-1)
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| Challenge: | Existing reading comprehension datasets focus on factual and literal understanding of context paragraphs, but our dataset focuses on reading between the lines over a diverse collection of everyday narratives. |
| Approach: | They propose a large-scale dataset that requires commonsense-based reading comprehension, formulated as multiple-choice questions. |
| Outcome: | The proposed architecture improves over the baselines of existing reading comprehension datasets and shows a significant gap between machine (68.4%) and human performance (94%). |
Thinking Like a Skeptic: Defeasible Inference in Natural Language (2020.findings-emnlp)
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Rachel Rudinger, Vered Shwartz, Jena D. Hwang, Chandra Bhagavatula, Maxwell Forbes, Ronan Le Bras, Noah A. Smith, Yejin Choi
| Challenge: | Defeasible inference is a mode of reasoning in which an inference may be weakened or overturned in light of new evidence. |
| Approach: | They propose a dataset for defeasible inference in natural language that includes extensions to existing inference datasets. |
| Outcome: | Defeasible NLI extends existing datasets for defeaasibility inference in natural language . generative models can weaken or strengthen inferences up to 68% of the time, it shows . |
proScript: Partially Ordered Scripts Generation (2021.findings-emnlp)
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| Challenge: | Scripts represent structured commonsense knowledge about prototypical events in everyday situations/scenarios such as bake a cake. |
| Approach: | They collect 6.4k crowdsourced partially ordered scripts and develop models that combine language generation and graph structure prediction to generate scripts. |
| Outcome: | The proposed models perform well on two tasks: edge prediction and script generation. |
Commonsense Knowledge Transfer for Pre-trained Language Models (2023.findings-acl)
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| Challenge: | Recent advances in pre-trained language models have transformed the landscape of natural language processing. |
| Approach: | They propose a framework to transfer commonsense knowledge stored in a neural commonsensing model to a general-purpose pre-trained language model. |
| Outcome: | Empirical results show that the proposed framework improves the model’s performance on downstream tasks that require commonsense reasoning. |
A Call for Clarity in Beam Search: How It Works and When It Stops (2024.lrec-main)
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| Challenge: | Empirical results show that a modified beam decoding implementation improves decoding performance of strong, neural language generation models. |
| Approach: | They propose a modification to a beam decoding implementation that generalizes the stopping criterion and provides flexibility to the depth of search. |
| Outcome: | The proposed method improves decoding performance of strong models on news text summarization and machine translation over diverse language pairs with negligible inference slowdown. |
Twist Decoding: Diverse Generators Guide Each Other (2022.emnlp-main)
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Jungo Kasai, Keisuke Sakaguchi, Ronan Le Bras, Hao Peng, Ximing Lu, Dragomir Radev, Yejin Choi, Noah A. Smith
| Challenge: | Using a variety of language generation models, ensembling models is challenging during inference. |
| Approach: | They propose a method that decodes text models that do not assume a shared vocabulary, tokenization or generation order. |
| Outcome: | The proposed method outperforms models decoded in isolation over various scenarios. |
NeuroLogic Decoding: (Un)supervised Neural Text Generation with Predicate Logic Constraints (2021.naacl-main)
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| Challenge: | Conditional text generation often requires lexical constraints, i.e., which words should or shouldn't be included in the output text. |
| Approach: | They propose an algorithm that enables neural language models to generate fluent text while satisfying complex lexical constraints. |
| Outcome: | The proposed algorithm outperforms existing methods on four benchmarks and shows that it handles any set of lexical constraints expressible under predicate logic while its asymptotic runtime is equivalent to conventional beam search. |
NLPositionality: Characterizing Design Biases of Datasets and Models (2023.acl-long)
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| Challenge: | Design biases in NLP systems often stem from creator’s positionality, i.e., views and lived experiences shaped by identity and background. |
| Approach: | They propose a framework for characterizing design biases and quantifying the positionality of NLP datasets and models. |
| Outcome: | The proposed framework characterizes design biases and quantifies alignment with dataset labels and model predictions. |
Unsupervised Commonsense Question Answering with Self-Talk (2020.emnlp-main)
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| Challenge: | Current systems rely on pre-trained language models or external knowledge bases to incorporate additional relevant knowledge. |
| Approach: | They propose an unsupervised framework based on self-talk to improve commonsense performance by asking language models to ask information seeking questions. |
| Outcome: | Empirical results show that the proposed framework improves on four out of six commonsense benchmarks and competes with models that obtain knowledge from external KBs. |
Generative Data Augmentation for Commonsense Reasoning (2020.findings-emnlp)
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Yiben Yang, Chaitanya Malaviya, Jared Fernandez, Swabha Swayamdipta, Ronan Le Bras, Ji-Ping Wang, Chandra Bhagavatula, Yejin Choi, Doug Downey
| Challenge: | Recent advances in commonsense reasoning depend on large-scale human-authored training data. |
| Approach: | They propose a generative data augmentation technique that augments human-authored training data by using pretrained language models. |
| Outcome: | The proposed technique outperforms existing methods on commonsense reasoning benchmarks and enhances out-of-distribution generalization. |
Bidimensional Leaderboards: Generate and Evaluate Language Hand in Hand (2022.naacl-main)
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Jungo Kasai, Keisuke Sakaguchi, Ronan Le Bras, Lavinia Dunagan, Jacob Morrison, Alexander Fabbri, Yejin Choi, Noah A. Smith
| Challenge: | Recent advances on models and metrics should benefit and inform each other, authors argue . bidimensional leaderboards allow for fast, accurate evaluation of language generation models . |
| Approach: | They propose a bidimensional leaderboard that tracks progress in language generation models and metrics for their evaluation. |
| Outcome: | The proposed leaderboards track progress in language generation models and metrics for their evaluation. |
Social IQa: Commonsense Reasoning about Social Interactions (D19-1)
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| Challenge: | Social IQa contains 38,000 multiple choice questions for probing emotional and social intelligence in a variety of everyday situations. |
| Approach: | They propose a crowdsourcing framework that collects commonsense questions along with correct and incorrect answers about social interactions. |
| Outcome: | The proposed framework mitigates stylistic artifacts in incorrect answers by asking workers to provide the right answer to a different but related question. |
I2D2: Inductive Knowledge Distillation with NeuroLogic and Self-Imitation (2023.acl-long)
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Chandra Bhagavatula, Jena D. Hwang, Doug Downey, Ronan Le Bras, Ximing Lu, Lianhui Qin, Keisuke Sakaguchi, Swabha Swayamdipta, Peter West, Yejin Choi
| Challenge: | Empirical results suggest that scale is not the only way to build commonsense capabilities. |
| Approach: | They propose a commonsense distillation framework that can achieve a competitive level of commonsensing without relying on the benefits of scale. |
| Outcome: | The proposed framework breaks the dependence on the extreme-scale teacher model with two innovations: (1) the novel adaptation of NeuroLogic Decoding to enhance the generation quality of the weak, off-the-shelf language models, and (2) self-imitation learning to iteratively learn from the model’s own enhanced commonsense acquisition capabilities. |
Natural Language Rationales with Full-Stack Visual Reasoning: From Pixels to Semantic Frames to Commonsense Graphs (2020.findings-emnlp)
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| Challenge: | Existing models that use natural language rationales provide intuitive, higher-level explanations that are easily understandable by humans. |
| Approach: | They propose a model that generates free-text rationales by combining pretrained language models with object recognition, grounded visual semantic frames, and visual commonsense graphs. |
| Outcome: | The proposed model generates free-text rationales by combining pretrained language models with object recognition, grounded visual semantic frames, and visual commonsense graphs. |
Transparent Human Evaluation for Image Captioning (2022.naacl-main)
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Jungo Kasai, Keisuke Sakaguchi, Lavinia Dunagan, Jacob Morrison, Ronan Le Bras, Yejin Choi, Noah A. Smith
| Challenge: | Recent work has demonstrated that image captioning is a complex task that requires a large amount of human input. |
| Approach: | They develop a human evaluation protocol for image captioning models based on machine- and human-generated captions on the MSCOCO dataset. |
| Outcome: | The proposed model improves CLIPScore, a recent metric that uses image features, and improves human judgments because it is more sensitive to recall. |
Generated Knowledge Prompting for Commonsense Reasoning (2022.acl-long)
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Jiacheng Liu, Alisa Liu, Ximing Lu, Sean Welleck, Peter West, Ronan Le Bras, Yejin Choi, Hannaneh Hajishirzi
| Challenge: | Existing methods for commonsense reasoning rely on high-quality knowledge, but they are often dominated by large-scale pretrained models that are fine-tuned on a target benchmark. |
| Approach: | They develop generated knowledge prompting which generates knowledge from a language model and provides it as additional input when answering a question. |
| Outcome: | The proposed method improves state-of-the-art models on four commonsense reasoning tasks. |
CLIPScore: A Reference-free Evaluation Metric for Image Captioning (2021.emnlp-main)
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| Challenge: | Image captioning relies on reference-based automatic evaluations, but references are expensive to collect and comparing against multiple human-authored captions is insufficient. |
| Approach: | They propose a reference-free metric that can be used for automatic caption evaluation without references. |
| Outcome: | The proposed model outperforms existing metrics on image-text compatibility and a reference-augmented version achieves even higher correlation with human judgements. |
Moral Stories: Situated Reasoning about Norms, Intents, Actions, and their Consequences (2021.emnlp-main)
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| Challenge: | aaron carroll: in social settings, human behavior is governed by unspoken rules of conduct rooted in societal norms . carroll and colleagues examine whether language generation models can serve as behavioral priors if they are not . they say we examine whether they can generate descriptions of actions that accomplish predefined goals . |
| Approach: | They propose to combine multiple expert models to improve quality of generated actions, consequences, and norms. |
| Outcome: | The proposed models significantly improve the quality of generated actions, consequences, and norms compared to baselines. |
Neural Theory-of-Mind? On the Limits of Social Intelligence in Large LMs (2022.emnlp-main)
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| Challenge: | We show that one of today’s largest language models lacks this kind of social intelligence out-of-the-box, using two tasks: SocialIQa and ToMi. |
| Approach: | They propose to use social intelligence and Theory of Mind to examine whether modern large-scale language models lack this kind of social intelligence out-of-the-box. |
| Outcome: | The proposed model lacks social intelligence out-of-the-box, and has well-below human accuracies on SocialIQa and ToMi, respectively. |
Back to the Future: Unsupervised Backprop-based Decoding for Counterfactual and Abductive Commonsense Reasoning (2020.emnlp-main)
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Lianhui Qin, Vered Shwartz, Peter West, Chandra Bhagavatula, Jena D. Hwang, Ronan Le Bras, Antoine Bosselut, Yejin Choi
| Challenge: | Existing methods for integrating past and future contexts are limited and require manual input. |
| Approach: | They propose an unsupervised decoding algorithm that incorporates past and future contexts using off-the-shelf, left-to-right language models and no supervision. |
| Outcome: | The proposed method outperforms unsupervised methods on abductive and counterfactual reasoning tasks. |
MacGyver: Are Large Language Models Creative Problem Solvers? (2024.naacl-long)
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Yufei Tian, Abhilasha Ravichander, Lianhui Qin, Ronan Le Bras, Raja Marjieh, Nanyun Peng, Yejin Choi, Thomas Griffiths, Faeze Brahman
| Challenge: | a new study examines the creative problem-solving capabilities of modern LLMs . it provides insight into the constrained problem- solving capabilities of both humans and AI . |
| Approach: | They use an automatically generated dataset to compare and contrast LLMs and humans to find out their creative problem-solving abilities. |
| Outcome: | The proposed dataset compares LLMs and humans in a constrained setting . it shows that humans excel in tasks they are familiar with but struggle with domain-specific knowledge . |
From Dogwhistles to Bullhorns: Unveiling Coded Rhetoric with Language Models (2023.acl-long)
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| Challenge: | This work sheds light on the theoretical and applied importance of dogwhistles in both NLP and computational social science. |
| Approach: | They propose a typology of dogwhistles, curate a glossary of over 300 dogwhitles and analyze their usage in historical U.S. politicians’ speeches. |
| Outcome: | The proposed model identifies dogwhistles and their meanings and shows that harmful content containing dogwhitles avoids toxicity detection. |
Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanations (2022.emnlp-main)
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Jaehun Jung, Lianhui Qin, Sean Welleck, Faeze Brahman, Chandra Bhagavatula, Ronan Le Bras, Yejin Choi
| Challenge: | Pre-trained language models struggle with consistent reasoning, and prompting methods are often noisy and inconsistent. |
| Approach: | They propose a few-shot inference method inspired by the Socratic way of conversation that generates a tree of explanations that bear logical relations between each other and frames it as a satisfiability problem. |
| Outcome: | The proposed method achieves 20% better accuracy than state-of-the-art prompting methods and performs competitively with supervised models. |
Modular Transformers: Compressing Transformers into Modularized Layers for Flexible Efficient Inference (2023.findings-acl)
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| Challenge: | Pre-trained sequence-to-sequence models have advanced the state of the art on text generation tasks. |
| Approach: | They introduce a modular encoder-decoder framework for flexible sequence-to-sequence model compression. |
| Outcome: | The proposed framework can achieve flexible compression ratios from 1.1x to 6x with little to moderate relative performance drop. |
Symbolic Knowledge Distillation: from General Language Models to Commonsense Models (2022.naacl-main)
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Peter West, Chandra Bhagavatula, Jack Hessel, Jena Hwang, Liwei Jiang, Ronan Le Bras, Ximing Lu, Sean Welleck, Yejin Choi
| Challenge: | Prior studies suggested pre-trained language models possess limited understanding of commonsense knowledge despite otherwise stellar performance on leaderboards. |
| Approach: | They propose a framework that uses larger models to teach smaller models by distilling knowledge symbolically as text in addition to the neural model. |
| Outcome: | The proposed framework is based on a general language model teacher's commonsense knowledge graphs and a neural commonsensing model surpassing the teacher model's in all three criteria. |
NeuroLogic A*esque Decoding: Constrained Text Generation with Lookahead Heuristics (2022.naacl-main)
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Ximing Lu, Sean Welleck, Peter West, Liwei Jiang, Jungo Kasai, Daniel Khashabi, Ronan Le Bras, Lianhui Qin, Youngjae Yu, Rowan Zellers, Noah A. Smith, Yejin Choi
| Challenge: | Existing paradigms for text generation are left-to-right decoding from autoregressive language models. |
| Approach: | They propose a decoding algorithm that incorporates heuristic estimates of future cost that are efficient for large-scale language models. |
| Outcome: | The proposed method outperforms baselines on five generation tasks and achieves new state-of-the-art performance on table-to-text generation, constrained machine translation, and keyword-constrained generation. |