Papers by Eshwar Chandrasekharan
SLM-Mod: Small Language Models Surpass LLMs at Content Moderation (2025.naacl-long)
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| Challenge: | Large language models (LLMs) are expensive to query in real-time and do not allow for a community-specific approach to content moderation. |
| Approach: | They propose to use small language models for community-specific content moderation tasks by fine-tuning and evaluating their performance against larger open- and closed-sourced models. |
| Outcome: | The proposed models outperform zero-shot LLMs in content moderation tasks with 11.5% higher accuracy and 25.7% higher recall across all communities. |
ArgCMV: An Argument Summarization Benchmark for the LLM-era (2025.emnlp-main)
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| Challenge: | Existing methods for key point extraction are limited by the popular ArgKP21 dataset . a novel dataset for long-context online discussions is proposed . |
| Approach: | They propose to use a long-context argument key point extraction dataset to test this method. |
| Outcome: | The proposed dataset exhibits higher complexity, co-referencing arguments, higher presence of subjective discourse units, and a larger range of topics over the existing dataset. |
A Just and Comprehensive Strategy for Using NLP to Address Online Abuse (P19-1)
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| Challenge: | Current methods to detect online abuse focus on a narrow definition of abuse to detriment of victims seeking validation and solutions. |
| Approach: | They argue that the NLP community needs to make three substantive changes to tackle both more subtle and more serious forms of abuse. |
| Outcome: | The proposed approach would address the problem of abuse in a more inclusive and productive way. |
Masking or Mitigating? Deconstructing the Impact of Query Rewriting on Retriever Biases in RAG (2026.findings-acl)
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| Challenge: | Query enhancement techniques are now standard in retrieval-augmented generation systems, yet their impact on these biases remains unexplored. |
| Approach: | They evaluate query enhancement techniques that improve retrieval quality . they find that simple rewriting reduces bias through increased score variance . no technique uniformly addresses all biases, and effects vary substantially across retrievers . |
| Outcome: | The proposed method achieves strongest aggregate reduction, but fails under adversarial conditions where multiple biases combine. |
MoMoE: Mixture of Moderation Experts Framework for AI-Assisted Online Governance (2025.emnlp-main)
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| Challenge: | Existing approaches for content moderation require a separate model for every community and are opaque in their decision-making. |
| Approach: | They propose a modular framework that adds post-hoc explanations to enable scalable content moderation. |
| Outcome: | The proposed framework yields scalable, transparent moderation without fine-tuning across domains. |