Papers by Sarthak Jain
An Empirical Comparison of Instance Attribution Methods for NLP (2021.naacl-main)
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| Challenge: | Influence functions provide machinery for identifying training instances that may have led to a specific prediction, but are computationally expensive and prohibitive in many cases. |
| Approach: | They evaluate the degree to which different potential instance attribution agrees with respect to the importance of training samples. |
| Outcome: | The proposed methods exhibit desirable characteristics similar to more complex methods, but are computationally expensive. |
Learning to Faithfully Rationalize by Construction (2020.acl-main)
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| Challenge: | Neural models dominate NLP but it remains difficult to know why they make specific predictions for sequential text inputs. |
| Approach: | They propose a model to produce faithful rationales for neural text classification by defining independent snippet extraction and prediction modules. |
| Outcome: | The proposed model produces faithful explanations even when the model is complex and complex. |
Does BERT Pretrained on Clinical Notes Reveal Sensitive Data? (2021.naacl-main)
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| Challenge: | Pretraining large (masked) language models over EHR data has yielded consistent performance gains across tasks. |
| Approach: | They propose to use large Transformers to release pretraining models over EHRs . they propose to recover patient names and conditions associated with them . |
| Outcome: | The proposed models recover patient names and conditions associated with patients . the proposed models share the model parameters for use by other researchers . |
Influence Functions for Sequence Tagging Models (2022.findings-emnlp)
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| Challenge: | Named Entity Recognition, Part-of-Speech tagging, and Semantic Role Labeling are standard tasks in NLP, but there has been little work on interpretability methods for sequence taging. |
| Approach: | They propose to extend influence functions to sequence tagging tasks by identifying noisy annotations in NER corpora. |
| Outcome: | The proposed methods are able to identify noisy annotations in NER corpora and are scalable. |
Learning Disentangled Representations of Texts with Application to Biomedical Abstracts (D18-1)
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| Challenge: | a method for learning disentangled representations of texts that encode distinct and complementary aspects is proposed . a classic problem in distributed representation learning is that it is difficult to determine what information individual dimensions encode. |
| Approach: | They propose a method for learning disentangled representations of texts that encode distinct and complementary aspects by a adversarial objective based on the (dis)similarity between triplets of documents with respect to specific aspects. |
| Outcome: | The proposed method can be used to perform aspect-specific retrieval on biomedical abstracts. |
SciREX: A Challenge Dataset for Document-Level Information Extraction (2020.acl-main)
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| Challenge: | Conventional datasets and methods for information extraction focus on within-sentence relations from general Newswire text. |
| Approach: | They propose a document-level IE dataset that integrates automatic and human annotations to annotate entities and document- level N-ary relation identification from scientific articles. |
| Outcome: | The proposed dataset extends state-of-the-art IE models to document-level IE. |
Combining Feature and Instance Attribution to Detect Artifacts (2022.findings-acl)
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| Challenge: | In this paper, we evaluate use of different attribution methods for aiding identification of training data artifacts. |
| Approach: | They propose hybrid methods that combine saliency maps and instance attribution methods to aid in identifying training data artifacts. |
| Outcome: | The proposed methods can be used to efficiently uncover artifacts in training data when a challenging validation set is available. |
Attention is not Explanation (N19-1)
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| Challenge: | Attention mechanisms have seen wide adoption in neural NLP models. |
| Approach: | They perform extensive experiments to assess the degree to which attention weights provide meaningful "explanations" they find that attention weighted inputs are often uncorrelated with gradient-based measures of feature importance . |
| Outcome: | The proposed model is based on a distribution over attended-to input units . the findings show that attention weights are often uncorrelated with features . |
ERASER: A Benchmark to Evaluate Rationalized NLP Models (2020.acl-main)
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Jay DeYoung, Sarthak Jain, Nazneen Fatema Rajani, Eric Lehman, Caiming Xiong, Richard Socher, Byron C. Wallace
| Challenge: | State-of-the-art models in NLP are opaque in terms of how they come to make predictions. |
| Approach: | They propose to release a benchmark to measure the quality of rationales extracted by models and how faithful these rationale are to human annotators. |
| Outcome: | The proposed benchmark will enable researchers to compare models and track progress on interpretable models for NLP. |
Aligning to Constraints for Data-Efficient Language Model Customization (2025.findings-naacl)
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Fei Wang, Chao Shang, Shuai Wang, Sarthak Jain, Qiang Ning, Bonan Min, Vittorio Castelli, Yassine Benajiba, Dan Roth
| Challenge: | General-purpose language models (LMs) are aligned to diverse user intents, but fall short when it comes to specific applications. |
| Approach: | They propose a framework that uses constraints to automatically produce supervision signals for user alignment with constraints. |
| Outcome: | The proposed framework can produce supervision signals for user alignment with constraints. |
How Many and Which Training Points Would Need to be Removed to Flip this Prediction? (2023.eacl-main)
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| Challenge: | Existing methods to find St using brute-force are intractable. |
| Approach: | They propose a fast approximation method to find St based on influence functions . they propose to identify a minimum subset of training data that one would need to remove . |
| Outcome: | The proposed method can find St based on influence functions for simple classification models. |
SUPP.AI: finding evidence for supplement-drug interactions (2020.acl-demos)
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Lucy Lu Wang, Oyvind Tafjord, Arman Cohan, Sarthak Jain, Sam Skjonsberg, Carissa Schoenick, Nick Botner, Waleed Ammar
| Challenge: | Dietary supplements are used by a large portion of the population, but information on their pharmacologic interactions is incomplete. |
| Approach: | They propose an application to search evidence sentences extracted from the literature to identify supplement-drug interactions. |
| Outcome: | The proposed model extracts supplement information and identifies interactions using labeled DDI data. |
Modular Self-Supervision for Document-Level Relation Extraction (2021.emnlp-main)
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| Challenge: | Prior work on information extraction tends to focus on binary relations within sentences . practical applications often require extracting complex relations across large text spans . |
| Approach: | They propose to decompose document-level relation extraction into relation detection and argument resolution, taking inspiration from Davidsonian semantics. |
| Outcome: | The proposed method outperforms state-of-the-art methods in biomedical machine reading for precision oncology by 20 absolute F1 points. |