🧑‍🔬 Jason Phang — all works

29 works · ← Jason Phang

1. BLOOM: A 176B-Parameter Open-Access Multilingual Language Model ↗arXiv:2211.05100 · no summary yetBigScience Workshop et al.2022 · 1,606 citations
2. GPT-NeoX-20B: An Open-Source Autoregressive Language ModelBigScience Episode – Workshop on Challenges & Perspectives in Creating Large Language ModelsSidney Black, Stella Biderman, Eric Hallahan, et al.2022 · 831 citations
3. Deep Neural Networks Improve Radiologists’ Performance in Breast Cancer Screening ↗IEEE Transactions on Medical Imaging · no summary yetNan Wu et al.2019 · 703 citations
4. Sentence Encoders on STILTs: Supplementary Training on Intermediate Labeled-data TasksarXiv:1811.01088Jason Phang, Thibault Févry, Samuel R. Bowman2018 · 503 citations
5. BBQ: A hand-built bias benchmark for question answeringFindings of the Association for Computational LinguisticsAlicia Parrish, Angelica Chen, Nikita Nangia, et al.2022 · 497 citations
6. Intermediate-Task Transfer Learning with Pretrained Language Models: When and Why Does It Work?Annual Meeting of the Association for Computational Linguistics (ACL)Yada Pruksachatkun, Jason Phang, Haokun Liu, et al.2020 · 281 citations
7. An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization ↗arXiv:2002.07613 · no summary yetYiqiu Shen et al.2020 · 213 citations
8. Investigating BERT's Knowledge of Language: Five Analysis Methods with NPIsConference on Empirical Methods in Natural Language Processing (EMNLP)Alex Warstadt, Yu Cao, Ioana Grosu, et al.2019 · 179 citations
9. Do Attention Heads in BERT Track Syntactic Dependencies? ↗arXiv (Cornell University) · no summary yetPhu Mon Htut et al.2019 · 175 citations
10. QuALITY: Question Answering with Long Input Texts, Yes!Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)Richard Yuanzhe Pang, Alicia Parrish, Nitish Joshi, et al.2022 · 167 citations
11. Pretraining Language Models with Human PreferencesarXiv:2302.08582Tomasz Korbak, Kejian Shi, Angelica Chen, et al.2023 · 121 citations
12. Lessons from the Trenches on Reproducible Evaluation of Language Models ↗arXiv:2405.14782 · no summary yetStella Biderman et al.2024 · 99 citations
13. jiant: A Software Toolkit for Research on General-Purpose Text Understanding Models ↗Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations · no summary yetYada Pruksachatkun et al.2020 · 89 citations
14. Intermediate-Task Transfer Learning with Pretrained Models for Natural Language Understanding: When and Why Does It Work? ↗arXiv (Cornell University) · no summary yetYada Pruksachatkun et al.2020 · 83 citations
15. What Language Model to Train if You Have One Million GPU Hours? ↗arXiv:2210.15424 · no summary yetTeven Le Scao et al.2022 · 73 citations
16. English Intermediate-Task Training Improves Zero-Shot Cross-Lingual Transfer TooConference of the Asia-Pacific Chapter of the Association for Computational Linguistics (AACL)Jason Phang, Iacer Calixto, Phu Mon Htut, et al.2020 · 70 citations
17. SQuALITY: Building a Long-Document Summarization Dataset the Hard Way ↗Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · no summary yetAlex Wang et al.2022 · 51 citations
18. Comparing Test Sets with Item Response Theory ↗Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · no summary yetClara Vania et al.2021 · 47 citations
19. What Do NLP Researchers Believe? Results of the NLP Community Metasurvey ↗Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetJulian Michael et al.2023 · 40 citations
20. Fine-Tuned Transformers Show Clusters of Similar Representations Across Layers ↗Proceedings of the Fourth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP · no summary yetJason Phang, Haokun Liu, Samuel R. Bowman2021 · 39 citations
21. Globally-Aware Multiple Instance Classifier for Breast Cancer Screening ↗Lecture notes in computer science · no summary yetYiqiu Shen et al.2019 · 38 citations
22. Two Failures of Self-Consistency in the Multi-Step Reasoning of LLMsarXiv:2305.14279 · Transactions on Machine Learning Research (2024)Angelica Chen, Jason Phang, Alicia Parrish, et al.2023 · 25 citations
23. Adversarially Constructed Evaluation Sets Are More Challenging, but May Not Be Fair ↗Proceedings of the First Workshop on Dynamic Adversarial Data Collection · no summary yetJason Phang et al.2022 · 18 citations
24. Single-Turn Debate Does Not Help Humans Answer Hard Reading-Comprehension Questions ↗Proceedings of the First Workshop on Learning with Natural Language Supervision · no summary yetAlicia Parrish et al.2022 · 13 citations
25. Reducing False-Positive Biopsies using Deep Neural Networks that Utilize both Local and Global Image Context of Screening Mammograms ↗Journal of Imaging Informatics in Medicine · no summary yetNan Wu et al.2021 · 12 citations
26. Improving localization-based approaches for breast cancer screening exam classification ↗arXiv (Cornell University) · no summary yetThibault Févry et al.2019 · 10 citations
27. Two-Turn Debate Doesn't Help Humans Answer Hard Reading Comprehension Questions ↗arXiv (Cornell University) · no summary yetAlicia Parrish et al.2022 · 6 citations
28. Reducing false-positive biopsies with deep neural networks that utilize local and global information in screening mammograms ↗arXiv (Cornell University) · no summary yetNan Wu et al.2020 · 3 citations
29. Screening Mammogram Classification with Prior Exams ↗arXiv (Cornell University) · no summary yetJungkyu Park et al.2019

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