Anjana Arunkumar


2023

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Real-Time Visual Feedback to Guide Benchmark Creation: A Human-and-Metric-in-the-Loop Workflow
Anjana Arunkumar | Swaroop Mishra | Bhavdeep Singh Sachdeva | Chitta Baral | Chris Bryan
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics

Recent research has shown that language models exploit ‘artifacts’ in benchmarks to solve tasks, rather than truly learning them, leading to inflated model performance. In pursuit of creating better benchmarks, we propose VAIDA, a novel benchmark creation paradigm for NLP, that focuses on guiding crowdworkers, an under-explored facet of addressing benchmark idiosyncrasies. VAIDA facilitates sample correction by providing realtime visual feedback and recommendations to improve sample quality. Our approach is domain, model, task, and metric agnostic, and constitutes a paradigm shift for robust, validated, and dynamic benchmark creation via human-and-metric-in-the-loop workflows. We evaluate via expert review and a user study with NASA TLX. We find that VAIDA decreases effort, frustration, mental, and temporal demands of crowdworkers and analysts, simultaneously increasing the performance of both user groups with a 45.8% decrease in the level of artifacts in created samples. As a by product of our user study, we observe that created samples are adversarial across models, leading to decreases of 31.3% (BERT), 22.5% (RoBERTa), 14.98% (GPT-3 fewshot) in performance.

2022

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Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks
Yizhong Wang | Swaroop Mishra | Pegah Alipoormolabashi | Yeganeh Kordi | Amirreza Mirzaei | Atharva Naik | Arjun Ashok | Arut Selvan Dhanasekaran | Anjana Arunkumar | David Stap | Eshaan Pathak | Giannis Karamanolakis | Haizhi Lai | Ishan Purohit | Ishani Mondal | Jacob Anderson | Kirby Kuznia | Krima Doshi | Kuntal Kumar Pal | Maitreya Patel | Mehrad Moradshahi | Mihir Parmar | Mirali Purohit | Neeraj Varshney | Phani Rohitha Kaza | Pulkit Verma | Ravsehaj Singh Puri | Rushang Karia | Savan Doshi | Shailaja Keyur Sampat | Siddhartha Mishra | Sujan Reddy A | Sumanta Patro | Tanay Dixit | Xudong Shen
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

How well can NLP models generalize to a variety of unseen tasks when provided with task instructions? To address this question, we first introduce Super-NaturalInstructions, a benchmark of 1,616 diverse NLP tasks and their expert-written instructions. Our collection covers 76 distinct task types, including but not limited to classification, extraction, infilling, sequence tagging, text rewriting, and text composition. This large and diverse collection of tasks enables rigorous benchmarking of cross-task generalization under instructions—training models to follow instructions on a subset of tasks and evaluating them on the remaining unseen ones.Furthermore, we build Tk-Instruct, a transformer model trained to follow a variety of in-context instructions (plain language task definitions or k-shot examples). Our experiments show that Tk-Instruct outperforms existing instruction-following models such as InstructGPT by over 9% on our benchmark despite being an order of magnitude smaller. We further analyze generalization as a function of various scaling parameters, such as the number of observed tasks, the number of instances per task, and model sizes. We hope our dataset and model facilitate future progress towards more general-purpose NLP models.