Papers by Atharva Naik
Representation Learning for Conversational Data using Discourse Mutual Information Maximization (2022.naacl-main)
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Bishal Santra, Sumegh Roychowdhury, Aishik Mandal, Vasu Gurram, Atharva Naik, Manish Gupta, Pawan Goyal
| Challenge: | Existing language modeling pretraining objectives do not take structural information of conversational text into account. |
| Approach: | They propose a structure-aware Mutual Information based loss-function DMI for training dialog-representation models that captures the inherent uncertainty in response prediction. |
| Outcome: | The proposed model outperforms strong baseline models on nine diverse tasks. |
Programming by Example meets Historical Linguistics: A Large Language Model Based Approach to Sound Law Induction (2025.acl-long)
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Atharva Naik, Darsh Agrawal, Hong Sng, Clayton Marr, Kexun Zhang, Nathaniel Romney Robinson, Kalvin Chang, Rebecca Byrnes, Aravind Mysore, Carolyn Rose, David R. Mortensen
| Challenge: | Historical linguists have written programs that convert reconstructed words into their attested descendants via ordered string rewrite functions. |
| Approach: | They propose to use a model to generate a "similar distribution" for sound law induction . they propose four kinds of methods with varying amounts of inductive bias to investigate best performance . |
| Outcome: | The proposed model shows that it can be fine tuned with training data and evaluation data. |
An Empirical Study on Strong-Weak Model Collaboration for Repo-level Code Generation (2025.emnlp-main)
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| Challenge: | Recent advances in large language models (LLMs) have demonstrated impressive capabilities across complex reasoning and generation tasks. |
| Approach: | They evaluate a broad spectrum of collaboration strategies for repository-level code generation where the weak model handles simpler tasks at lower cost and the most challenging tasks are delegated to the strong model. |
| Outcome: | The proposed model achieves equivalent performance to the strong model while reducing the cost by 40%. |
Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks (2022.emnlp-main)
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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
| Challenge: | a benchmark of 1,616 diverse NLP tasks and their expert-written instructions is used to test generalization of models to unseen tasks . a recent study shows that instruction-following models outperform instruction-based models by over 9% . |
| Approach: | They build a benchmark of 1,616 diverse NLP tasks and their expert-written instructions. |
| Outcome: | The proposed model outperforms existing instruction-following models by over 9% on the benchmark despite being smaller. |
CRScore: Grounding Automated Evaluation of Code Review Comments in Code Claims and Smells (2025.naacl-long)
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| Challenge: | Current review comment evaluation metrics rely on comparisons with a human-written reference for a given code change (also called a diff). |
| Approach: | They propose to use a reference-free metric to measure review quality like conciseness, comprehensiveness, and relevance to compare the quality of code changes with human-written references. |
| Outcome: | The proposed metric can produce fine-grained scores that have the greatest alignment with human judgment and are more sensitive than reference-based metrics. |
PBEBench: A Multi-Step Programming by Examples Reasoning Benchmark inspired by Historical Linguistics (2026.findings-acl)
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Atharva Naik, null Prakam, Yash Mathur, Darsh Agrawal, Manav Nitin Kapadnis, Yuwei An, Clayton Marr, Carolyn Rose, David R. Mortensen
| Challenge: | a benchmark for inductive reasoning is based on sound law induction in historical linguistics . solve rates are below 5% on hard PBEBench instances with long program cascades despite expensive scaling strategies . |
| Approach: | They propose a benchmark for inductive reasoning inspired by sound law induction in historical linguistics. |
| Outcome: | The proposed approach generates problems with controllable difficulty and ordering constraints . solve rates remain below 5% on hard PBEBench instances with long program cascades . |
Data Augmentation for Code Translation with Comparable Corpora and Multiple References (2023.findings-emnlp)
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| Challenge: | Existing methods for translating code between programming languages are limited by parallel training data. |
| Approach: | They propose a data augmentation technique that builds comparable corpora and augments existing parallel data with multiple reference translations. |
| Outcome: | The proposed techniques improve CodeT5 translation between Java, Python, and C++ by an average of 7.5% Computational Accuracy (CA@1) . |