Papers by Pradeep Dasigi
Generating Data to Mitigate Spurious Correlations in Natural Language Inference Datasets (2022.acl-long)
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| Challenge: | Natural language processing models exploit spurious correlations between features and labels in datasets to perform well only within the distributions they are trained on. |
| Approach: | They propose to generate a debiased version of a dataset and replace it with training data to train a model that is generalised to different task distributions. |
| Outcome: | The proposed method outperforms or performs comparable to state-of-the-art debiasing strategies on a large suite of debiased, out-of distribution, and adversarial test sets. |
Data-Efficient Finetuning Using Cross-Task Nearest Neighbors (2023.findings-acl)
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| Challenge: | Prior work shows training models on multitask data augmented with task descriptions transfers knowledge to new tasks. |
| Approach: | They propose to use unlabeled target-task data to train models on task descriptions . they use only 2% of the data from the P3 pool without labeled target task data . |
| Outcome: | The proposed model outperforms baseline models on 12 out of 14 datasets . it also provides better initialization than single model on target-task data . |
IIRC: A Dataset of Incomplete Information Reading Comprehension Questions (2020.emnlp-main)
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| Challenge: | Existing reading comprehension tasks focus on questions for which the contexts provide all the information required to answer them, thus not evaluating a system’s performance at identifying a potential lack of sufficient information and locating sources for that information. |
| Approach: | They propose to use a dataset with 13K questions over paragraphs from English Wikipedia that provide only partial information to answer them, with the missing information occurring in one or more linked documents. |
| Outcome: | The proposed model achieves 31.1% F1 on the reading comprehension task, while estimated human performance is 88.4%. |
LongEval: Guidelines for Human Evaluation of Faithfulness in Long-form Summarization (2023.eacl-main)
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| Challenge: | Human evaluation is labor-intensive, expensive to scale, and difficult to design. |
| Approach: | They propose a set of guidelines for human evaluation of faithfulness in long-form summaries that address the following challenges: (1) How can we achieve high inter-annotator agreement on faithfulness scores? (2) How can our annotator minimize workload while maintaining accurate faithfulness? |
| Outcome: | The proposed framework reduces inter-annotator variance in faithfulness scores while minimizing annotator workload while maintaining accuracy. |
Mitigating False-Negative Contexts in Multi-document Question Answering with Retrieval Marginalization (2021.emnlp-main)
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| Challenge: | Question Answering models typically use retrieval and reasoning components to identify relevant information for reasoning. |
| Approach: | They propose a retrieval parameterization method that marginalizes unanswerable queries . they show that marginalization allows a model to mitigate false negatives in annotations . |
| Outcome: | The proposed model improves on two multi-document question answering datasets and shows that marginalization improves performance. |
Easy, Reproducible and Quality-Controlled Data Collection with CROWDAQ (2020.emnlp-demos)
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Qiang Ning, Hao Wu, Pradeep Dasigi, Dheeru Dua, Matt Gardner, Robert L. Logan IV, Ana Marasović, Zhen Nie
| Challenge: | Efficient data collection is important for advancing research and building time-sensitive applications. |
| Approach: | They propose an open-source platform that standardizes the data collection pipeline . it includes customizable user interface components, automated annotator qualification, and saved pipelines . |
| Outcome: | The proposed platform simplifies data annotation significantly on diverse datasets . it can be used by researchers and engineers to improve reproducibility and minimize overhead . |
Quoref: A Reading Comprehension Dataset with Questions Requiring Coreferential Reasoning (D19-1)
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| Challenge: | Existing reading comprehension benchmarks do not contain complex coreferential phenomena . obtaining questions focused on such phenomena is difficult because of lexical cues . |
| Approach: | They propose to use a crowdsourced dataset to examine the ability of models to resolve coreference among entities in Wikipedia paragraphs. |
| Outcome: | The proposed model performs significantly worse than humans on the reading comprehension benchmark . paragraphs and other longer texts typically make multiple references to the same entities . |
Scalable Data Ablation Approximations for Language Models through Modular Training and Merging (2024.emnlp-main)
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Clara Na, Ian Magnusson, Ananya Harsh Jha, Tom Sherborne, Emma Strubell, Jesse Dodge, Pradeep Dasigi
| Challenge: | Training data compositions for Large Language Models (LLMs) can significantly affect their downstream performance. |
| Approach: | They propose a method which trains individual models on subsets of a training corpus and reuses them across evaluations of combinations of subset. |
| Outcome: | The proposed method improves training efficiency by scaling only linearly with respect to new data. |
Learning with Instance Bundles for Reading Comprehension (2021.emnlp-main)
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| Challenge: | a study shows that training reading comprehension models assumes that the training instances are independent and identically distributed . however, this assumption can cause the learner to ignore distinguishing cues between related or minimally different questions . |
| Approach: | They propose to normalize question-answer scores across neighborhoods of closely contrasting questions and/or answers by adding a cross entropy loss term to the supervision signal. |
| Outcome: | The proposed methods show up to 9% absolute gains in accuracy on two datasets. |
Evaluating Models’ Local Decision Boundaries via Contrast Sets (2020.findings-emnlp)
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Matt Gardner, Yoav Artzi, Victoria Basmov, Jonathan Berant, Ben Bogin, Sihao Chen, Pradeep Dasigi, Dheeru Dua, Yanai Elazar, Ananth Gottumukkala, Nitish Gupta, Hannaneh Hajishirzi, Gabriel Ilharco, Daniel Khashabi, Kevin Lin, Jiangming Liu, Nelson F. Liu, Phoebe Mulcaire, Qiang Ning, Sameer Singh, Noah A. Smith, Sanjay Subramanian, Reut Tsarfaty, Eric Wallace, Ally Zhang, Ben Zhou
| Challenge: | Standard test sets for supervised learning evaluate in-distribution generalization but are misleading when a dataset has systematic gaps. |
| Approach: | They propose a more rigorous annotation paradigm for NLP that helps to close systematic gaps in the test data. |
| Outcome: | The proposed model performs significantly lower on contrast sets than on the original test sets—up to 25% in some cases. |
Hybrid Preferences: Learning to Route Instances for Human vs. AI Feedback (2025.acl-long)
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Lester James Validad Miranda, Yizhong Wang, Yanai Elazar, Sachin Kumar, Valentina Pyatkin, Faeze Brahman, Noah A. Smith, Hannaneh Hajishirzi, Pradeep Dasigi
| Challenge: | Learning from human feedback has enabled the alignment of language models (LMs) with human preferences. |
| Approach: | They propose a Hybrid Preference routER that defers an annotation to either humans or LMs, achieving better annotation quality while reducing the cost of human-only annotation. |
| Outcome: | The proposed model achieves better annotation quality while reducing the cost of human-only annotation. |
DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs (N19-1)
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| Challenge: | a large body of work has highlighted the brittleness of reading comprehension systems . a crowdsourced, adversarially-created, 55k-question benchmark requires a more comprehensive understanding of paragraphs . |
| Approach: | They propose a reading comprehension benchmark that requires Discrete Reasoning over the content of paragraphs. |
| Outcome: | The proposed benchmarks show that the best systems only achieve 38.4% F1 on the generalized accuracy metric, while human performance is 96%. |
Merge to Learn: Efficiently Adding Skills to Language Models with Model Merging (2024.findings-emnlp)
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| Challenge: | Adapting general-purpose language models to new skills is currently expensive . Adaptation to new skill sets requires repeated training or models forget older skills . |
| Approach: | They propose a parallel-train-then-merge procedure that adds new skills to preexisting models in isolation and later merges with the general model. |
| Outcome: | The proposed method is cheaper than retraining models on updated datasets . it improves model compliance with safe prompts while preserving model's ability to refuse dangerous or harmful prompts. |
Iterative Search for Weakly Supervised Semantic Parsing (N19-1)
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| Challenge: | Recent work has focused on training semantic parsers via weak supervision from denotations alone. |
| Approach: | They propose an iterative training algorithm that alternates between searching for consistent logical forms and maximizing the marginal likelihood of the retrieved ones. |
| Outcome: | The proposed algorithm outperforms the previous best systems on WikiTableQuestions and Cornell Natural Language Visual Reasoning (NLVR) iteratively train models that provide guidance to subsequent models to search for logical forms of increasing complexity, thus dealing with spuriousness. |
A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers (2021.naacl-main)
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| Challenge: | Existing information-seeking question answering datasets do not perform well on answering these questions . existing models that do well on other QA tasks do not do well answering these tasks . |
| Approach: | They present a dataset of 5049 questions over 1585 NLP papers . they use a question-seeking QA model that seeks information in the full text . |
| Outcome: | The proposed dataset underperforms existing models on other QA tasks by 27 F1 points . the focus is on document-grounded, information-seeking QA . |
Neural Semantic Parsing (P18-5)
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| Challenge: | Semantic parsing is the study of translating natural language utterances into machine-executable programs. |
| Approach: | They will describe the various approaches researchers have taken to translate natural language into a formal language . they will also discuss why much recent work has chosen to use standard programming languages instead of more linguistically-motivated representations. |
| Outcome: | This paper will describe the various approaches researchers have taken to translate natural language into a formal language. |
Evaluating In-Context Learning of Libraries for Code Generation (2024.naacl-long)
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| Challenge: | Recent work shows that large proprietary LLMs can learn novel library usage in-context from demonstrations. |
| Approach: | They evaluate large proprietary LLMs to understand library usage in-context . they find they are able to generate code based on library specification presented in-constext - a promising area . |
| Outcome: | The proposed models can learn library usage in-context from demonstrations . the results pave the way for more adaptable and dynamic coding environments. |