Zoher Kachwala

I am currently pursuing a PhD in Computer Science at Indiana University’s Luddy School of Informatics, Computing and Engineering, under the guidance of Professor Filippo Menczer. I am actively involved in the Observatory on Social Media and the NaN research group. I also have the privilege to collaborate with Professor Jisun An and Professor Haewoon Kwak.

My research specializes in Natural Language Processing (LLMs), AI Alignment, and Reliable Machine Learning. I focus on developing novel steering techniques to improve safety, controllability, and interpretability in Large Language Models and Vision-Language Models.

My current research centers on three key areas: Zero-shot detection of AI-generated images through task-aligned prompting of VLMs, supporting authenticity verification and content trust; Community-aware content moderation, using LLMs to interpret user history and apply nuanced, rule-grounded moderation at scale; and Controlled reasoning in LLMs, analyzing decoding dynamics to guide generation toward faithful, safe, and interpretable outputs.

This work contributes to the broader goals of AI alignment and safety, ensuring that advanced AI systems remain controllable, interpretable, and aligned with human values and community standards.

Recent GitHub Activity

Contribution activity for the past year

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news

May 15, 2025	Excited to share that our paper `Task-Aligned Prompting Improves Detection of AI-Generated Images in VLMs` has been submitted to NeurIPS 2025! Our zero-shot-s² method improves AI-generated image detection by up to 29% without fine-tuning. 🚀
Oct 17, 2024	The results of the first CNetS Chocolate Tasting Workshop are finally live! 🎉 We had a panel of expert taste-testers rate 15 different chocolates on a -5 to 5 scale, and the results are full of surprises. Which chocolate reigned supreme? Which one got the cold shoulder? You’ll have to click through to find out!😏
Jun 06, 2024	My virtual NAACL24 presentation for `Rematch` is now live on YouTube! In this video, I delve into: 🔍 The significance of graphical representations in language, or “local knowledge graphs.” ⚖️ The critical aspects we aim to optimize while keeping computational costs low. 🏆 How our algorithm, REMATCH, outperforms state-of-the-art methods in these areas.
Mar 14, 2024	My paper `Rematch: Robust and Efficient Matching of Local Knowledge Graphs to Improve Structural and Semantic Similarity` was accepted to NAACL24!
Mar 01, 2024	My research was awarded computing resources worth $160,550 by NSF’s Jetstream2 Project!

selected publications

Task-Aligned Prompting Improves Detection of AI-Generated Images in VLMs

Zoher Kachwala , Jisun An , Haewoon Kwak , and 1 more author

In Under Review at NeurIPS 2025 , 2025

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Introduces zero-shot-s², a task-aligned prompting method that improves AI-generated image detection in VLMs by up to 29% without fine-tuning, showing strong generalization across models and datasets.
@inproceedings{kachwala2025task, title = {Task-Aligned Prompting Improves Detection of AI-Generated Images in VLMs}, author = {Kachwala, Zoher and An, Jisun and Kwak, Haewoon and Menczer, Filippo}, booktitle = {Under Review at NeurIPS 2025}, year = {2025}, status = {Under Review}, }
Advanced Heuristics for LLM Decoding Improve Chain-of-Thought Reasoning

Zoher Kachwala , and Filippo Menczer

In In Progress , 2024

Abs Bib

Proposes novel decoding strategies to enhance LLM reasoning, improving explainability, alignment, and reliability in complex generation tasks.
@inproceedings{kachwala2024heuristics, title = {Advanced Heuristics for LLM Decoding Improve Chain-of-Thought Reasoning}, author = {Kachwala, Zoher and Menczer, Filippo}, booktitle = {In Progress}, year = {2024}, status = {In Progress}, }

Hyper-Contextual Steering: Community-Aware Moderation with LLMs

Zoher Kachwala , Jisun An , Haewoon Kwak , and 1 more author

In In Progress , 2024

Abs Bib

@inproceedings{kachwala2024hyper,
  title = {Hyper-Contextual Steering: Community-Aware Moderation with LLMs},
  author = {Kachwala, Zoher and An, Jisun and Kwak, Haewoon and Menczer, Filippo},
  booktitle = {In Progress},
  year = {2024},
  status = {In Progress},
}

Rematch: Robust and Efficient Matching of Local Knowledge Graphs for Improved Structural and Semantic Similarity

Zoher Kachwala , Jisun An , Haewoon Kwak , and 1 more author

In 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics , 2024

Abs Bib PDF Code

Knowledge graphs play a pivotal role in various applications, such as question-answering and fact-checking. Abstract Meaning Representation (AMR) represents text as local knowledge graphs. Evaluating the quality of these graphs involves matching them structurally to each other and semantically to the source text. Existing AMR metrics are inefficient and struggle to capture semantic similarity. We also lack a systematic evaluation benchmark for assessing structural similarity between AMR graphs. To overcome these limitations, we introduce a novel AMR similarity metric, rematch, alongside a new evaluation for structural similarity called RARE. Among state-of-the-art metrics, rematch ranks second in structural similarity; and first in semantic similarity by 1–5 percentage points on the STS-B and SICK-R benchmarks. Rematch is also five times faster than the next most efficient metric.
@inproceedings{kachwala2024rematch, title = {Rematch: Robust and Efficient Matching of Local Knowledge Graphs for Improved Structural and Semantic Similarity}, author = {Kachwala, Zoher and An, Jisun and Kwak, Haewoon and Menczer, Filippo}, booktitle = {2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics}, volume = {Findings of the Association for Computational Linguistics: NAACL 2024}, year = {2024}, url = {https://aclanthology.org/2024.findings-naacl.64}, }
A multi-platform collection of social media posts about the 2022 US midterm elections

Rachith Aiyappa , Matthew R DeVerna , Manita Pote , and 8 more authors

In Proceedings of the International AAAI Conference on Web and Social Media , 2023

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Social media are utilized by millions of citizens to discuss important political issues. Politicians use these platforms to connect with the public and broadcast policy positions. Therefore, data from social media has enabled many studies of political discussion. While most analyses are limited to data from individual platforms, people are embedded in a larger information ecosystem spanning multiple social networks. Here we describe and provide access to the Indiana University 2022 U.S. Midterms Multi-Platform Social Media Dataset (MEIU22), a collection of social media posts from Twitter, Facebook, Instagram, Reddit, and 4chan. MEIU22 links to posts about the midterm elections based on a comprehensive list of keywords and tracks the social media accounts of 1,011 candidates from October 1 to December 25, 2022. We also publish the source code of our pipeline to enable similar multi-platform research projects.
@inproceedings{aiyappa2023multi, title = {A multi-platform collection of social media posts about the 2022 US midterm elections}, author = {Aiyappa, Rachith and DeVerna, Matthew R and Pote, Manita and Truong, Bao Tran and Zhao, Wanying and Axelrod, David and Pessianzadeh, Aria and Kachwala, Zoher and Kim, Munjung and Seckin, Ozgur Can and others}, booktitle = {Proceedings of the International AAAI Conference on Web and Social Media}, volume = {17}, pages = {981--989}, year = {2023}, doi = {10.1609/icwsm.v17i1.22205}, url = {https://ojs.aaai.org/index.php/ICWSM/article/view/22205}, }

The Inexplicable Efficacy of Language Models

Rachith Aiyappa , and Zoher Kachwala

XRDS: Crossroads, The ACM Magazine for Students, Apr 2023

Abs Bib

@article{aiyappa_inexplicable_2023,
  title = {The {Inexplicable} {Efficacy} of {Language} {Models}},
  volume = {29},
  issn = {1528-4972},
  url = {https://dl.acm.org/doi/10.1145/3589654},
  doi = {10.1145/3589654},
  number = {3},
  urldate = {2024-03-18},
  journal = {XRDS: Crossroads, The ACM Magazine for Students},
  author = {Aiyappa, Rachith and Kachwala, Zoher},
  month = apr,
  year = {2023},
  pages = {60--62},
}