Atharva Naik

Intro

I am Atharva Naik a first year PhD (and former MLT) student advised by Prof. Carolyn Rose at the Language Technologies Institute at Carnegie Mellon University. My research interests lie around creating LLM agents that can collaborate with humans for software engineering while facilitating on-the-job learning about code quality, security and efficiency. I'm currently working on a benchmark for version specific best practice recommendation for Python and using code generation capabilities of LLMs for automated phonological reasoning. I recently finished a project where I created CRScore an automated metric for evaluating code review comments in a reference free manner using LLMs and static analysis tools like code smell detectors. In the past, I have published work on data augmentation for code translation, using curriculum learning to improve generalization in code search, and using LLMs to provide support to students for reflection-based learning in collaborative programming activities.

Full CV

Education

Experience

2024

CRScore: Grounding the Evaluation of Code Review Comments in Code Claims and Smells Atharva Naik, Marcus Alenius, Daniel Fried, Carolyn Rosé Arxiv

Can Large Language Models Code Like a Linguist?: A Case Study in Low Resource Sound Law Induction Atharva Naik, Kexun Zhang, Nathaniel Robinson, Aravind Mysore, Clayton Marr, Hong Sng, Rebecca Byrnes, Anna Cai, Kalvin Chang, David Mortensen Arxiv

On the Limitations of Embedding Based Methods for Measuring Functional Correctness for Code Generation Atharva Naik Arxiv

Generating Situated Reflection Triggers about Alternative Solution Paths: A Case Study of Generative AI for Computer-Supported Collaborative Learning Atharva Naik, Jessica Ruhan Yin, Anusha Kamath, Qianou Ma, Sherry Tongshuang Wu, Charles Murray, Christopher Bogart, Majd Sakr, Carolyn P. Rose AIED (2024)

Tricking LLMs into Disobedience: Understanding, Analyzing, and Preventing Jailbreaks Abhinav Rao, Atharva Naik* Sachin Vashistha*, Somak Aditya, Monojit Choudhury LREC-COLLING (2024): 16802-16830 (* - indicates equal contribution)

SkillCLIP: Skill Aware Modality Fusion Visual Question Answering (Student Abstract) Atharva Naik, Yash Parag Butala, Navaneethan Vaikunthan, Raghav Kapoor AAAI Student Abstracts (2024): 23592-23593 (* - indicates equal contribution)

2023

Data Augmentation for Code Translation with Comparable Corpora and Multiple References Yiqing Xie, Atharva Naik, Daniel Fried, Carolyn Rose EMNLP (2023) Findings

SYNC: A Structurally guided Hard Negative Curriculum for Generalizable Neural Code Search Atharva Naik, Soumitra Das, Jyothi Vedurada, Somak Aditya AACL (2023)

Weakly Supervised Explainable Phrasal Reasoning with Neural Fuzzy Logic Zijun Wu, Zi Xuan Zhang, Atharva Naik, Zhijian Mei, Mauajama Firdaus, Lili Mou ICLR (2023)

2022

Super-NaturalInstructions: Generalization via declarative instructions on 1600+ nlp tasks Yizhong Wang, Swaroop Mishra, Pegah Alipoormolabashi, Yeganeh Kordi, Amirreza Mirzaei, Anjana Arunkumar, Arjun Ashok, Arut Selvan Dhanasekaran, Atharva Naik, ... , Chitta Baral, Yejin Choi, Noah A. Smith, Hannaneh Hajishirzi, Daniel Khashabi NAACL (2022): 1718–1734

Representation Learning for Conversational Data using Discourse Mutual Information Maximization Bishal Santra, Sumegh Roychowdhury, Aishik Mandal, Vasu Gurram, Atharva Naik, Manish Gupta, Pawan Goyal NAACL (2022): 1718–1734

Towards Enabling Synchronous Digital Creative Collaboration: Codifying Conflicts in Co-Coloring Suryateja BV, Jeet Patel, Atharva Naik, Yash Parag Butala, Sristy Sharma, Niyati Chhaya CHI Extended Abstracts (2022/4/27): 1-7

2021

Understanding the role of affect dimensions in detecting emotions from tweets: a multi-task approach Rajdeep Mukherjee, Atharva Naik, Sriyash Poddar, Soham Dasgupta, Niloy Ganguly ACM SIGIR (2021): 2303-2307

Email :

arnaik [at] andrew [dot] cmu [dot] edu