I am currently a third-year Ph.D. candidate at the University of Michigan, Ann Arbor, working under the guidance of Prof. Lu Wang. My current work centers on investigating, reducing, and assessing potential scheming and deceptive behaviors in language technologies that can emerge from prevailing alignment methods. Concurrently, I am also exploring how we can generate complex function / api calling datapoints using LLMs that involve complex interdependent long trajectories of api executions with the hope of using these datapoints to improve and evaluating the existing technologies. Previously, I investigated applications of LLMs in education, structured commonsense reasoning, and the evaluation of consistency in their value preferences. Prior to that, I was a Research Associate at Adobe Research Labs, Bangalore where I worked on a wide range of Machine Learning projects in Document intelligence, Natural Language Processing, Information Extraction and Legal Artificial Intelligence. I recieved my Bachelor of Technology (BTech.) in Electrical Engineering with Minor in Computer Science and Engineering from Indian Institute of Technology, Bombay (IIT Bombay) in 2021. In my spare time, I enjoy gaming, reading non-fiction books, and playing sports like badminton and squash.
Research
I am broadly interested in Machine Learning, Natural Language Processing (NLP) and related fields. Currently, I am exploring how LLMs can be employed in effectively extracting the argumentative structure of the text, all with the objective of providing invaluable feedback to students regarding their essay compositions. Currently, I am exploring how potential deceptive and scheming behavior can emerge in current language technologies during the alignment process. During my internship at Bloomberg, I explored the problem of generating complex function / api calling datapoints for improving and evaluating the tool calling abilities of LLMs. At Adobe, I have primarily worked in document intelligence projects involving NLP techniques like semantic / topical structure analysis and information extraction. During my undergraduate studies, I worked with Prof. Suyash Awate to repurpose a generative modelling framework for the task of one-class classification and anomaly detection. Under the guidance of Prof. Shivaram Kalyanakrishnan, I worked in developing a PAC algorithm for mode estimation of discrete distribution with efficient sample complexity.
Preprints
Inderjeet Nair, Lu Wang; Do Language Models Think Consistently? A Study of Value Preferences Across Varying Response Lengths;
Jie Ruan*, Inderjeet Nair*, Shuyang Cao*, Amy Liu, Sheza Munir, Micah Pollens-Dempsey, Tiffany Chiang, Lucy Kates, Nicholas David, Sihan Chen, Ruxin Yang, Yuqian Yang, Jasmine Gump, Tessa Bialek, Vivek Sankaran, Margo Schlanger, Lu Wang; ExpertLongBench: Benchmarking Language Models on Expert-Level Long-Form Generation Tasks with Structured Checklists;
Publications
Inderjeet Nair, Jiaye Tan, Xiaotian Su, Anne Gere, Xu Wang, Lu Wang; Closing the Loop: Learning to Generate Writing Feedback via Language Model Simulated Student Revisions; Accepted to EMNLP 2024 (Oral)
Inderjeet Nair, Lu Wang; MIDGARD: Self-Consistency Using Minimum Description Length for Structured
Commonsense Reasoning; Accepted to ACL 2024 (Oral), Area Chair Award
Inderjeet Nair*, Shwetha S.*, Apoorva Saxena, Koustava Goswami; Drilling Down into the Discourse Structure with LLMs for Long Document Question Answering; Accepted to EMNLP 2023 Findings
Adrian Kochsiek, Apoorv Umang Saxena, Inderjeet Nair, Rainer Gemulla; Friendly Neighbors: Contextualized Sequence-to-Sequence Link Prediction; Accepted to ACL 2023 Workshop RepL4NLP
Inderjeet Nair, Natwar Modani; Exploiting Language Characteristics for Legal Domain-Specific Language Model Pretraining; Accepted to EACL 2023 Findings
Inderjeet Nair, Aparna Garimella, Balaji Vasan Srinivasan, Natwar Modani, Niyati Chhaya, Srikrishna Karanam, Sumit Shekhar; A Neural CRF-based Hierarchical Approach for Linear Text Segmentation; Accepted to EACL 2023 Findings
Aishwarya Agarwal, Anuj Srivastava, Inderjeet Nair, Swasti Shreya Mishra, Vineeth Dorna, Sharmila Reddy Nangi, Balaji Vasan Srinivasan; SketchBuddy: Context-Aware Sketch Enrichment and Enhancement; Accepted to 2023 ACM Multimedia Systems
Shubham Anand Jain, Rohan Shah, Sanit Gupta, Denil Mehta, Inderjeet Nair, Jian Vora, Sushil Khyalia, Sourav Das, Vinay J Ribeiro, Shivaram Kalyanakrishnan; PAC Mode Estimation using PPR Martingale Confidence Sequences; Accepted to the 2022 International Conference on Artificial Intelligence and Statistics
Natwar Modani, Anurag Maurya, Gaurav Verma, Inderjeet Nair, Vaidehi Patil, Anirudh Kanfade; Detecting Document Versions and Their Ordering in a Collection; Accepted to the 2021 International Conference on Web Information Systems Engineering; Best Paper Runners-up Award
Patents
Inderjeet Nair, Aparna Garimella, Balaji Vasan Srinivasan, Natwar Modani, Niyati Chhaya, Srikrishna Karanam, Sumit Shekhar ; Reader-driven Zoom in – Zoom out: Segmentation to enable non-linear navigation of long form documents; US Patent Application to be filed; Adobe Inc.
Inderjeet Nair, Anirudh Phukan, Aravind Veluri, Lakshya J., Mohar Kundu, Akhash Amarnath, Niyati Chhaya, Sumit Shekhar; Reflowing Infographics for Enhanced Cross-Device Consumption; US Patent Application to be filed; Adobe Inc.
Inderjeet Nair, Akshay Singhal, Kumud Lakara, Pritika Ramu, Vikas Balani, Anandhavelu N; Minimally Guided Semantic Extraction; US Patent Application to be filed; Adobe Inc.
Inderjeet Nair, Natwar Modani; Exploiting Legal Domain Characteristics for Legal Language Model Pretraining; US Patent Application to be filed; Adobe Inc.
Inderjeet Nair, Natwar Modani; Integrated Reading Experience for Contracts and their Amendments; US Patent Application 17/954,558; Adobe Inc.
Ayush Maheshwari, Inderjeet Nair, Navita Goyal, Natwar Modani, Ani Nenkova; Assisted Review of Text Content using a Machine Learning Model; US Patent Application 17/549,270; Adobe Inc.
Natwar Modani, Vaidehi Patil, Inderjeet Nair, Gaurav Verma, Anurag Maurya, Anirudh Kanfade; Systems for Generating Indications of Relationships between Electronic Documents; US Patent Application 17/534,744; Adobe Inc.
Work Experience
Bloomberg CTO office, New York
Research Intern - May 2025 - August 2025
University of Michigan, Ann Arbor
Ph.D. Student - September 2023 - Current
Adobe Research Labs, Bangalore
Research Associate - July 2021 - August 2023
Adobe, Research Labs, Bangalore
Research Intern — June 2020 - August 2020
Stamp My Visa, Mumbai
Production Engineer — June 2019 - August 2019
CV
Contact
inderjeetnair1 [at] gmail.com
inair [at] umich.com