Kv Aditya Srivatsa

Also published as: KV Aditya Srivatsa


2024

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Harnessing the Power of Multiple Minds: Lessons Learned from LLM Routing
Kv Aditya Srivatsa | Kaushal Maurya | Ekaterina Kochmar
Proceedings of the Fifth Workshop on Insights from Negative Results in NLP

With the rapid development of LLMs, it is natural to ask how to harness their capabilities efficiently. In this paper, we explore whether it is feasible to direct each input query to a single most suitable LLM. To this end, we propose LLM routing for challenging reasoning tasks. Our extensive experiments suggest that such routing shows promise but is not feasible in all scenarios, so more robust approaches should be investigated to fill this gap.

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What Makes Math Word Problems Challenging for LLMs?
Kv Aditya Srivatsa | Ekaterina Kochmar
Findings of the Association for Computational Linguistics: NAACL 2024

This paper investigates the question of what makes math word problems (MWPs) in English challenging for large language models (LLMs). We conduct an in-depth analysis of the key linguistic and mathematical characteristics of MWPs. In addition, we train feature-based classifiers to better understand the impact of each feature on the overall difficulty of MWPs for prominent LLMs and investigate whether this helps predict how well LLMs fare against specific categories of MWPs.

2022

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Towards Toxic Positivity Detection
Ishan Sanjeev Upadhyay | KV Aditya Srivatsa | Radhika Mamidi
Proceedings of the Tenth International Workshop on Natural Language Processing for Social Media

Over the past few years, there has been a growing concern around toxic positivity on social media which is a phenomenon where positivity is used to minimize one’s emotional experience. In this paper, we create a dataset for toxic positivity classification from Twitter and an inspirational quote website. We then perform benchmarking experiments using various text classification models and show the suitability of these models for the task. We achieved a macro F1 score of 0.71 and a weighted F1 score of 0.85 by using an ensemble model. To the best of our knowledge, our dataset is the first such dataset created.

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Sammaan@LT-EDI-ACL2022: Ensembled Transformers Against Homophobia and Transphobia
Ishan Sanjeev Upadhyay | Kv Aditya Srivatsa | Radhika Mamidi
Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion

Hateful and offensive content on social media platforms can have negative effects on users and can make online communities more hostile towards certain people and hamper equality, diversity and inclusion. In this paper, we describe our approach to classify homophobia and transphobia in social media comments. We used an ensemble of transformer-based models to build our classifier. Our model ranked 2nd for English, 8th for Tamil and 10th for Tamil-English.