VISAGE: A Support Vector Machine Approach to Group Dynamic Analysis. - Abhilasha Ravichander; Supriya Vijay; Varshini Ramaseshan; S. Natarajan (ICMLA, 2015)

Investigating the "Wisdom of Crowds" at Scale. - Alok Shankar Mysore; Vikas S. Yaligar; Imanol Arrieta Ibarra; Camelia Simoiu; Sharad Goel; Ramesh Arvind; Chiraag Sumanth; Arvind Srikantan; Bhargav HS; Mayank Pahadia; Tushar Dobha; Atif Ahmed; et al. (UIST (Adjunct Volume), 2015)

Does the Geometry of Word Embeddings Help Document Classification? A Case Study on Persistent Homology-Based Representations. - Paul Michel; Abhilasha Ravichander; Shruti Rijhwani (Rep4NLP@ACL, 2017)

How Would You Say It? Eliciting Lexically Diverse Dialogue for Supervised Semantic Parsing. - Abhilasha Ravichander; Thomas Manzini; Matthias Grabmair; Graham Neubig; Jonathan Francis; Eric Nyberg (SIGDIAL Conference, 2017)

Preserving Intermediate Objectives: One Simple Trick to Improve Learning for Hierarchical Models. - Abhilasha Ravichander; Shruti Rijhwani; Rajat Kulshreshtha; Chirag Nagpal; Tadas Baltrusaitis; Louis-Philippe Morency (CoRR, 2017)

Stress Test Evaluation for Natural Language Inference. - Aakanksha Naik; Abhilasha Ravichander; Norman M. Sadeh; Carolyn P. Rosé; Graham Neubig (COLING, 2018)

An Empirical Study of Self-Disclosure in Spoken Dialogue Systems. - Abhilasha Ravichander; Alan W. Black (SIGDIAL Conference, 2018)

Exploring Numeracy in Word Embeddings. - Aakanksha Naik; Abhilasha Ravichander; Carolyn P. Rosé; Eduard H. Hovy (ACL (1), 2019)

EQUATE: A Benchmark Evaluation Framework for Quantitative Reasoning in Natural Language Inference. - Abhilasha Ravichander; Aakanksha Naik; Carolyn P. Rosé; Eduard H. Hovy (CoNLL, 2019)

Question Answering for Privacy Policies: Combining Computational and Legal Perspectives. - Abhilasha Ravichander; Alan W. Black; Shomir Wilson; Thomas B. Norton; Norman M. Sadeh (EMNLP/IJCNLP (1), 2019)

MAPS: Scaling Privacy Compliance Analysis to a Million Apps. - Sebastian Zimmeck; Peter Story; Daniel Smullen; Abhilasha Ravichander; Ziqi Wang 007; Joel R. Reidenberg; N. Cameron Russell; Norman M. Sadeh (Proc. Priv. Enhancing Technol., 2019)

Overview and Insights from the Shared Tasks at Scholarly Document Processing 2020: CL-SciSumm, LaySumm and LongSumm. - Muthu Kumar Chandrasekaran; Guy Feigenblat; Eduard H. Hovy; Abhilasha Ravichander; Michal Shmueli-Scheuer; Anita de Waard (SDP@EMNLP, 2020)

On the Systematicity of Probing Contextualized Word Representations: The Case of Hypernymy in BERT. - Abhilasha Ravichander; Eduard H. Hovy; Kaheer Suleman; Adam Trischler; Jackie Chi Kit Cheung (*SEM@COLING, 2020)

EIGEN: Event Influence GENeration using Pre-trained Language Models. - Aman Madaan; Dheeraj Rajagopal; Yiming Yang; Abhilasha Ravichander; Eduard H. Hovy; Shrimai Prabhumoye (CoRR, 2020)

Breaking Down Walls of Text: How Can NLP Benefit Consumer Privacy? - Abhilasha Ravichander; Alan W. Black; Thomas B. Norton; Shomir Wilson; Norman M. Sadeh (ACL/IJCNLP (1), 2021)

Probing the Probing Paradigm: Does Probing Accuracy Entail Task Relevance? - Abhilasha Ravichander; Yonatan Belinkov; Eduard H. Hovy (EACL, 2021)

NoiseQA: Challenge Set Evaluation for User-Centric Question Answering. - Abhilasha Ravichander; Siddharth Dalmia; Maria Ryskina; Florian Metze; Eduard H. Hovy; Alan W. Black (EACL, 2021)

CURIE: An Iterative Querying Approach for Reasoning About Situations. - Dheeraj Rajagopal; Aman Madaan; Niket Tandon; Yiming Yang; Shrimai Prabhumoye; Abhilasha Ravichander; Peter Clark; Eduard H. Hovy (CoRR, 2021)

Measuring and Improving Consistency in Pretrained Language Models. - Yanai Elazar; Nora Kassner; Shauli Ravfogel; Abhilasha Ravichander; Eduard H. Hovy; Hinrich Schütze; Yoav Goldberg (Trans. Assoc. Comput. Linguistics, 2021)

Erratum: Measuring and Improving Consistency in Pretrained Language Models. - Yanai Elazar; Nora Kassner; Shauli Ravfogel; Abhilasha Ravichander; Eduard H. Hovy; Hinrich Schütze; Yoav Goldberg (Trans. Assoc. Comput. Linguistics, 2021)

CONDAQA: A Contrastive Reading Comprehension Dataset for Reasoning about Negation. - Abhilasha Ravichander; Matt Gardner 001; Ana Marasovic (EMNLP, 2022)

A Tale of Two Regulatory Regimes: Creation and Analysis of a Bilingual Privacy Policy Corpus. - Siddhant Arora; Henry Hosseini; Christine Utz; Vinayshekhar Bannihatti Kumar; Tristan Dhellemmes; Abhilasha Ravichander; Peter Story; Jasmine Mangat; Rex Chen; Martin Degeling; Thomas B. Norton; Thomas Hupperich; et al. (LREC, 2022)

Measuring Causal Effects of Data Statistics on Language Model's 'Factual' Predictions. - Yanai Elazar; Nora Kassner; Shauli Ravfogel; Amir Feder; Abhilasha Ravichander; Marius Mosbach; Yonatan Belinkov; Hinrich Schütze; Yoav Goldberg (CoRR, 2022)

Exploring and Improving the Accessibility of Data Privacy-related Information for People Who Are Blind or Low-vision. - Yuanyuan Feng; Abhilasha Ravichander; Yaxing Yao; Shikun Zhang; Norman Sadeh 001 (CoRR, 2022)

Inference-Time Policy Adapters (IPA): Tailoring Extreme-Scale LMs without Fine-tuning. - Ximing Lu; Faeze Brahman; Peter West; Jaehun Jung; Khyathi Raghavi Chandu; Abhilasha Ravichander; Prithviraj Ammanabrolu; Liwei Jiang; Sahana Ramnath; Nouha Dziri; Jillian Fisher; Bill Y. Lin; et al. (EMNLP, 2023)

When and Why Does Bias Mitigation Work? - Abhilasha Ravichander; Joe Stacey; Marek Rei (EMNLP (Findings), 2023)

What's In My Big Data? - Yanai Elazar; Akshita Bhagia; Ian Magnusson; Abhilasha Ravichander; Dustin Schwenk; Alane Suhr; Pete Walsh; Dirk Groeneveld; Luca Soldaini; Sameer Singh 001; Hanna Hajishirzi; Noah A. Smith; et al. (CoRR, 2023)

Lumos: Learning Agents with Unified Data, Modular Design, and Open-Source LLMs. - Da Yin; Faeze Brahman; Abhilasha Ravichander; Khyathi Raghavi Chandu; Kai-Wei Chang; Yejin Choi 001; Bill Yuchen Lin (CoRR, 2023)

Artifacts or Abduction: How Do LLMs Answer Multiple-Choice Questions Without the Question? - Nishant Balepur; Abhilasha Ravichander; Rachel Rudinger (ACL (1), 2024)

OLMo: Accelerating the Science of Language Models. - Dirk Groeneveld; Iz Beltagy; Evan Pete Walsh; Akshita Bhagia; Rodney Kinney; Oyvind Tafjord; Ananya Harsh Jha; Hamish Ivison; Ian Magnusson; Yizhong Wang; Shane Arora; David Atkinson; et al. (ACL (1), 2024)

Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research. - Luca Soldaini; Rodney Kinney; Akshita Bhagia; Dustin Schwenk; David Atkinson; Russell Authur; Ben Bogin; Khyathi Raghavi Chandu; Jennifer Dumas; Yanai Elazar; Valentin Hofmann; Ananya Harsh Jha; et al. (ACL (1), 2024)

Agent Lumos: Unified and Modular Training for Open-Source Language Agents. - Da Yin; Faeze Brahman; Abhilasha Ravichander; Khyathi Raghavi Chandu; Kai-Wei Chang; Yejin Choi 001; Bill Yuchen Lin (ACL (1), 2024)

What's In My Big Data? - Yanai Elazar; Akshita Bhagia; Ian Magnusson; Abhilasha Ravichander; Dustin Schwenk; Alane Suhr; Evan Pete Walsh; Dirk Groeneveld; Luca Soldaini; Sameer Singh 001; Hannaneh Hajishirzi; Noah A. Smith; et al. (ICLR, 2024)

The Unlocking Spell on Base LLMs: Rethinking Alignment via In-Context Learning. - Bill Yuchen Lin; Abhilasha Ravichander; Ximing Lu; Nouha Dziri; Melanie Sclar; Khyathi Raghavi Chandu; Chandra Bhagavatula; Yejin Choi 001 (ICLR, 2024)

The Generative AI Paradox: "What It Can Create, It May Not Understand". - Peter West; Ximing Lu; Nouha Dziri; Faeze Brahman; Linjie Li; Jena D. Hwang; Liwei Jiang; Jillian Fisher; Abhilasha Ravichander; Khyathi Raghavi Chandu; Benjamin Newman; Pang Wei Koh; et al. (ICLR, 2024)

MacGyver: Are Large Language Models Creative Problem Solvers? - Yufei Tian; Abhilasha Ravichander; Lianhui Qin; Ronan Le Bras 001; Raja Marjieh; Nanyun Peng 001; Yejin Choi 001; Thomas L. Griffiths 001; Faeze Brahman (NAACL-HLT, 2024)

The Art of Saying No: Contextual Noncompliance in Language Models. - Faeze Brahman; Sachin Kumar 009; Vidhisha Balachandran; Pradeep Dasigi; Valentina Pyatkin; Abhilasha Ravichander; Sarah Wiegreffe; Nouha Dziri; Khyathi Raghavi Chandu; Jack Hessel; Yulia Tsvetkov; Noah A. Smith; et al. (NeurIPS, 2024)

Understanding How to Inform Blind and Low-Vision Users about Data Privacy through Privacy Question Answering Assistants. - Yuanyuan Feng; Abhilasha Ravichander; Yaxing Yao; Shikun Zhang; Rex Chen; Shomir Wilson; Norman Sadeh 001 (USENIX Security Symposium, 2024)

Incorporating Taxonomic Reasoning and Regulatory Knowledge into Automated Privacy Question Answering. - Abhilasha Ravichander; Ian Yang; Rex Chen; Shomir Wilson; Thomas B. Norton; Norman M. Sadeh (WISE (1), 2024)

OLMo: Accelerating the Science of Language Models. - Dirk Groeneveld; Iz Beltagy; Pete Walsh; Akshita Bhagia; Rodney Kinney; Oyvind Tafjord; Ananya Harsh Jha; Hamish Ivison; Ian Magnusson; Yizhong Wang; Shane Arora; David Atkinson; et al. (CoRR, 2024)

WildBench: Benchmarking LLMs with Challenging Tasks from Real Users in the Wild. - Bill Yuchen Lin; Yuntian Deng; Khyathi Raghavi Chandu; Faeze Brahman; Abhilasha Ravichander; Valentina Pyatkin; Nouha Dziri; Ronan Le Bras 001; Yejin Choi 001 (CoRR, 2024)

WildHallucinations: Evaluating Long-form Factuality in LLMs with Real-World Entity Queries. - Wenting Zhao; Tanya Goyal; Yu Ying Chiu; Liwei Jiang; Benjamin Newman; Abhilasha Ravichander; Khyathi Raghavi Chandu; Ronan Le Bras 001; Claire Cardie; Yuntian Deng; Yejin Choi 001 (CoRR, 2024)

RESTOR: Knowledge Recovery through Machine Unlearning. - Keivan Rezaei; Khyathi Raghavi Chandu; Soheil Feizi; Yejin Choi 001; Faeze Brahman; Abhilasha Ravichander (CoRR, 2024)

HALoGEN: Fantastic LLM Hallucinations and Where to Find Them. - Abhilasha Ravichander; Shrusti Ghela; David Wadden; Yejin Choi 001 (ACL (1), 2025)

WildBench: Benchmarking LLMs with Challenging Tasks from Real Users in the Wild. - Bill Yuchen Lin; Yuntian Deng; Khyathi Raghavi Chandu; Abhilasha Ravichander; Valentina Pyatkin; Nouha Dziri; Ronan Le Bras 001; Yejin Choi 001 (ICLR, 2025)

Reverse Question Answering: Can an LLM Write a Question so Hard (or Bad) that it Can't Answer? - Nishant Balepur; Feng Gu; Abhilasha Ravichander; Shi Feng 005; Jordan Lee Boyd-Graber; Rachel Rudinger (NAACL (Short Papers), 2025)

Information-Guided Identification of Training Data Imprint in (Proprietary) Large Language Models. - Abhilasha Ravichander; Jillian Fisher; Taylor Sorensen; Ximing Lu; Maria Antoniak; Bill Yuchen Lin; Niloofar Mireshghallah; Chandra Bhagavatula; Yejin Choi 001 (NAACL (Long Papers), 2025)

RESTOR: Knowledge Recovery in Machine Unlearning. - Keivan Rezaei; Khyathi Raghavi Chandu; Soheil Feizi; Yejin Choi 001; Faeze Brahman; Abhilasha Ravichander (Trans. Mach. Learn. Res., 2025)

Why and How LLMs Hallucinate: Connecting the Dots with Subsequence Associations. - Yiyou Sun; Yu Gai; Lijie Chen 001; Abhilasha Ravichander; Yejin Choi 001; Dawn Song (CoRR, 2025)

What Has Been Lost with Synthetic Evaluation? - Alexander Gill; Abhilasha Ravichander; Ana Marasovic (CoRR, 2025)

Model State Arithmetic for Machine Unlearning. - Keivan Rezaei; Mehrdad Saberi; Abhilasha Ravichander; Soheil Feizi (CoRR, 2025)

The Curious Case of Factuality Finetuning: Models' Internal Beliefs Can Improve Factuality. - Benjamin Newman; Abhilasha Ravichander; Jaehun Jung; Rui Xin; Hamish Ivison; Yegor Kuznetsov; Pang Wei Koh; Yejin Choi 001 (CoRR, 2025)

The Surprising Effectiveness of Membership Inference with Simple N-Gram Coverage. - Skyler Hallinan; Jaehun Jung; Melanie Sclar; Ximing Lu; Abhilasha Ravichander; Sahana Ramnath; Yejin Choi 001; Sai Praneeth Karimireddy; Niloofar Mireshghallah; Xiang Ren 001 (CoRR, 2025)