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Works368 from public data
- HellaSwag: Can a Machine Really Finish Your Sentence?5,095
The construction of HellaSwag, a new challenge dataset, and its resulting difficulty, sheds light on the inner workings of deep pretrained models, and suggests a new path forward for NLP research, in which benchmarks co-evolve with the evolving state-of-the-art in an adversarial way, so as to present ever-harder challenges.
- The Curious Case of Neural Text Degeneration4,651
By sampling text from the dynamic nucleus of the probability distribution, which allows for diversity while effectively truncating the less reliable tail of the distribution, the resulting text better demonstrates the quality of human text, yielding enhanced diversity without sacrificing fluency and coherence.
- PIQA: Reasoning about Physical Commonsense in Natural Language3,673
The task of physical commonsense reasoning and a corresponding benchmark dataset Physical Interaction: Question Answering or PIQA are introduced and analysis about the dimensions of knowledge that existing models lack are provided, which offers significant opportunities for future research.
- CLIPScore: A Reference-free Evaluation Metric for Image Captioning3,343
The surprising empirical finding that CLIP (Radford et al., 2021), a cross-modal model pretrained on 400M image+caption pairs from the web, can be used for robust automatic evaluation of image captioning without the need for references is reported.
- Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks2,301
This paper proposes a new learning method Oscar (Object-Semantics Aligned Pre-training), which uses object tags detected in images as anchor points to significantly ease the learning of alignments.
- RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models1,890
It is found that pretrained LMs can degenerate into toxic text even from seemingly innocuous prompts, and empirically assess several controllable generation methods find that while data- or compute-intensive methods are more effective at steering away from toxicity than simpler solutions, no current method is failsafe against neural toxic degeneration.
- Neural Motifs: Scene Graph Parsing with Global Context1,207
This work analyzes the role of motifs: regularly appearing substructures in scene graphs and introduces Stacked Motif Networks, a new architecture designed to capture higher order motifs in scene graph graphs that improves on the previous state-of-the-art by an average of 3.6% relative improvement across evaluation settings.
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- From Recognition to Cognition: Visual Commonsense Reasoning1,083
To move towards cognition-level understanding, a new reasoning engine is presented, Recognition to Cognition Networks (R2C), that models the necessary layered inferences for grounding, contextualization, and reasoning.
- The Risk of Racial Bias in Hate Speech Detection1,082
This work proposes *dialect* and *race priming* as ways to reduce the racial bias in annotation, showing that when annotators are made explicitly aware of an AAE tweet’s dialect they are significantly less likely to label the tweet as offensive.
- 1,077
- COMET: Commonsense Transformers for Automatic Knowledge Graph Construction1,061
This investigation reveals promising results when implicit knowledge from deep pre-trained language models is transferred to generate explicit knowledge in commonsense knowledge graphs, and suggests that using generative commonsense models for automatic commonsense KB completion could soon be a plausible alternative to extractive methods.
- Truth of Varying Shades: Analyzing Language in Fake News and Political Fact-Checking957
Experiments show that while media fact-checking remains to be an open research question, stylistic cues can help determine the truthfulness of text.
- MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms937
A large-scale dataset of math word problems and an interpretable neural math problem solver by learning to map problems to their operation programs and a new representation language to model operation programs corresponding to each math problem that aim to improve both the performance and the interpretability of the learned models.
- QuAC: Question Answering in Context931
QuAC introduces challenges not found in existing machine comprehension datasets: its questions are often more open-ended, unanswerable, or only meaningful within the dialog context, as it shows in a detailed qualitative evaluation.
- SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference804
This paper introduces the task of grounded commonsense inference, unifying natural language inference and commonsense reasoning, and proposes Adversarial Filtering (AF), a novel procedure that constructs a de-biased dataset by iteratively training an ensemble of stylistic classifiers, and using them to filter the data.
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- Social Bias Frames: Reasoning about Social and Power Implications of Language672
It is found that while state-of-the-art neural models are effective at high-level categorization of whether a given statement projects unwanted social bias, they are not effective at spelling out more detailed explanations in terms of Social Bias Frames.
- Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics648
The results indicate that a shift in focus from quantity to quality of data could lead to robust models and improved out-of-distribution generalization, and a model-based tool to characterize and diagnose datasets.
- OpinionFinder: a system for subjectivity analysis605
OpinionFinder is a system that performs subjectivity analysis, automatically identifying when opinions, sentiments, speculations, and other private states are present in text.
- DExperts: Decoding-Time Controlled Text Generation with Experts and Anti-Experts576
This work highlights the promise of tuning small LMs on text with (un)desirable attributes for efficient decoding-time steering and applies DExperts to language detoxification and sentiment-controlled generation, where it outperform existing controllable generation methods on both automatic and human evaluations.
- Syntactic Stylometry for Deception Detection568
This paper investigates syntactic stylometry for deception detection, adding a somewhat unconventional angle to prior literature and demonstrating that features driven from Context Free Grammar (CFG) parse trees consistently improve the detection performance over several baselines that are based only on shallow lexico-syntactic features.
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- Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning529
This paper introduces Cosmos QA, a large-scale dataset of 35,600 problems that require commonsense-based reading comprehension, formulated as multiple-choice questions, and proposes a new architecture that improves over the competitive baselines.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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