161 citations · 656 across the 43 of their papers we have counts for
51 papers · 1 filter
Coverage-based Example Selection for In-Context Learning
Shivanshu Gupta, Matt Gardner, Sameer Singh
In-context learning (ICL), the ability of large language models to perform novel tasks by conditioning on a prompt with a few task examples, requires these examples to be informati…
Successive Prompting for Decomposing Complex Questions
Dheeru Dua, Shivanshu Gupta, Sameer Singh +1
Answering complex questions that require making latent decisions is a challenging task, especially when limited supervision is available. Recent works leverage the capabilities of…
CONDAQA: A Contrastive Reading Comprehension Dataset for Reasoning about Negation
Abhilasha Ravichander, Matt Gardner, Ana Marasović
The full power of human language-based communication cannot be realized without negation. All human languages have some form of negation. Despite this, negation remains a challengi…
When to Use Multi-Task Learning vs Intermediate Fine-Tuning for Pre-Trained Encoder Transfer Learning
Orion Weller, Kevin Seppi, Matt Gardner
Transfer learning (TL) in natural language processing (NLP) has seen a surge of interest in recent years, as pre-trained models have shown an impressive ability to transfer to nove…
Generating Data to Mitigate Spurious Correlations in Natural Language Inference Datasets
Yuxiang Wu, Matt Gardner, Pontus Stenetorp +1
Natural language processing models often exploit spurious correlations between task-independent features and labels in datasets to perform well only within the distributions they a…
Structurally Diverse Sampling for Sample-Efficient Training and Comprehensive Evaluation
Shivanshu Gupta, Sameer Singh, Matt Gardner
A growing body of research has demonstrated the inability of NLP models to generalize compositionally and has tried to alleviate it through specialized architectures, training sche…
Impact of Pretraining Term Frequencies on Few-Shot Reasoning
Yasaman Razeghi, Robert L. Logan, Matt Gardner +1
Pretrained Language Models (LMs) have demonstrated ability to perform numerical reasoning by extrapolating from a few examples in few-shot settings. However, the extent to which th…
Evidentiality-guided Generation for Knowledge-Intensive NLP Tasks
Akari Asai, Matt Gardner, Hannaneh Hajishirzi
Retrieval-augmented generation models have shown state-of-the-art performance across many knowledge-intensive NLP tasks such as open question answering and fact verification. These…
COVR: A test-bed for Visually Grounded Compositional Generalization with real images
Ben Bogin, Shivanshu Gupta, Matt Gardner +1
While interest in models that generalize at test time to new compositions has risen in recent years, benchmarks in the visually-grounded domain have thus far been restricted to syn…
Enforcing Consistency in Weakly Supervised Semantic Parsing
Nitish Gupta, Sameer Singh, Matt Gardner
The predominant challenge in weakly supervised semantic parsing is that of spurious programs that evaluate to correct answers for the wrong reasons. Prior work uses elaborate searc…
QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehension
Anna Rogers, Matt Gardner, Isabelle Augenstein
Alongside huge volumes of research on deep learning models in NLP in the recent years, there has been also much work on benchmark datasets needed to track modeling progress. Questi…
Tailor: Generating and Perturbing Text with Semantic Controls
Alexis Ross, Tongshuang Wu, Hao Peng +2
Controlled text perturbation is useful for evaluating and improving model generalizability. However, current techniques rely on training a model for every target perturbation, whic…
A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers
Pradeep Dasigi, Kyle Lo, Iz Beltagy +3
Readers of academic research papers often read with the goal of answering specific questions. Question Answering systems that can answer those questions can make consumption of the…
Generative Context Pair Selection for Multi-hop Question Answering
Dheeru Dua, Cicero Nogueira dos Santos, Patrick Ng +4
Compositional reasoning tasks like multi-hop question answering, require making latent decisions to get the final answer, given a question. However, crowdsourced datasets often cap…
Learning with Instance Bundles for Reading Comprehension
Dheeru Dua, Pradeep Dasigi, Sameer Singh +1
When training most modern reading comprehension models, all the questions associated with a context are treated as being independent from each other. However, closely related quest…
Paired Examples as Indirect Supervision in Latent Decision Models
Nitish Gupta, Sameer Singh, Matt Gardner +1
Compositional, structured models are appealing because they explicitly decompose problems and provide interpretable intermediate outputs that give confidence that the model is not…
Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus
Jesse Dodge, Maarten Sap, Ana Marasović +5
Large language models have led to remarkable progress on many NLP tasks, and researchers are turning to ever-larger text corpora to train them. Some of the largest corpora availabl…
Competency Problems: On Finding and Removing Artifacts in Language Data
Matt Gardner, William Merrill, Jesse Dodge +4
Much recent work in NLP has documented dataset artifacts, bias, and spurious correlations between input features and output labels. However, how to tell which features have "spurio…
Mitigating False-Negative Contexts in Multi-document Question Answering with Retrieval Marginalization
Ansong Ni, Matt Gardner, Pradeep Dasigi
Question Answering (QA) tasks requiring information from multiple documents often rely on a retrieval model to identify relevant information for reasoning. The retrieval model is t…
Learning from Task Descriptions
Orion Weller, Nicholas Lourie, Matt Gardner +1
Typically, machine learning systems solve new tasks by training on thousands of examples. In contrast, humans can solve new tasks by reading some instructions, with perhaps an exam…
IIRC: A Dataset of Incomplete Information Reading Comprehension Questions
James Ferguson, Matt Gardner, Hannaneh Hajishirzi +2
Humans often have to read multiple documents to address their information needs. However, most existing reading comprehension (RC) tasks only focus on questions for which the conte…
MOCHA: A Dataset for Training and Evaluating Generative Reading Comprehension Metrics
Anthony Chen, Gabriel Stanovsky, Sameer Singh +1
Posing reading comprehension as a generation problem provides a great deal of flexibility, allowing for open-ended questions with few restrictions on possible answers. However, pro…
Improving Compositional Generalization in Semantic Parsing
Inbar Oren, Jonathan Herzig, Nitish Gupta +2
Generalization of models to out-of-distribution (OOD) data has captured tremendous attention recently. Specifically, compositional generalization, i.e., whether a model generalizes…
Understanding Mention Detector-Linker Interaction in Neural Coreference Resolution
Zhaofeng Wu, Matt Gardner
Despite significant recent progress in coreference resolution, the quality of current state-of-the-art systems still considerably trails behind human-level performance. Using the C…