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.

I am passionate about bridging the gap between language understanding and structured knowledge. I am particularly intrigued by the potential of local knowledge graphs like Abstract Meaning Representations (AMRs), to ground Large Language Models (LLMs) in trust-sensitive applications like fact-checking and question answering. My current research focuses on developing efficient graph-based methods to leverage and evaluate the semantic richness of AMRs.

news

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!
Jun 02, 2023	Our paper `A multi-platform collection of social media posts about the 2022 US midterm elections` is now published at ICWSM23!
Apr 12, 2023	Our column `The Inexplicable Efficacy of Language Models` is now live in XRDS: Crossroads, The ACM Magazine for Students!

selected publications

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

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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}, year = {2024}, url = {https://openreview.net/forum?id=dBnsZ72qUQ}, eprint = {https://arxiv.org/abs/2404.02126} }
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

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@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},
}