publications

2025

1. patent

   Generating alternative examples for content

   Simra Shahid, Nikitha Srikanth, Surgan Jandial, and 1 more author

   May 2025

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2. patent

   Generative artificial intelligence powered response generation, validation, and augmentation

   Niranjan Kumbi, Sreekanth Reddy, Sumit Bhatia, and 5 more authors

   Mar 2025

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2024

1. patent

   Hierarchical topic model with an interpretable topic hierarchy

   Nikitha Srikanth, Simra Shahid, Tanay Anand, and 3 more authors

   Apr 2024

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2. patent

   Digital content analysis

   Yaman Kumar, Somesh Singh, Pranjal Prasoon, and 11 more authors

   Oct 2024

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2023

1. poster

   Contextual Alchemy: A Framework for Enhanced Readability through Cross-Domain Entity Alignment

   Nikitha Srikanth, Simra Shahid, Surgan Jandial, and 1 more author

   In NeurIPS 2023 Workshop on Machine Learning for Creativity and Design, Dec 2023

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   Prior to the development of Large Language Models (LLMs), the pursuit of creative writing or content adjustment mainly focused on tailoring tonality, style, and lexicon to suit reader preferences. In addition, there have been frameworks aimed at simplification like ’Explain it to me like I’m five’ and targeted explanation like ’Explain to me like I’m a scientist’.In this work, we present Contextual Alchemy, a framework that identifies examples and its context in a document and suggests alternate examples for different topic of interest, time, and region. Consider that you are reading a document that mentions Magnavox Odyssey. Such an example does not resonate with all readers and they might lose relevance over time. Our framework aims to retrieve other replacable entities in similar context, for example, in the sports domain Reebok has faced a similar outcome to Magnavox Odyssey. In this manner, our work utilises LLMs to enhance readability by adapting entities and context within a document to align closely with varied reader interests, ensuring reading is more engaging, relatable, and factually consistent for diverse readers.

2. paper

   HyHTM: Hyperbolic Geometry-based Hierarchical Topic Model

   Nikitha Srikanth, Simra Shahid, Tanay Anand, and 3 more authors

   In Findings of the Association for Computational Linguistics: ACL 2023, Jul 2023

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   Hierarchical Topic Models (HTMs) are useful for discovering topic hierarchies in a collection of documents. However, traditional HTMs often produce hierarchies where lower-level topics are unrelated and not specific enough to their higher-level topics. Additionally, these methods can be computationally expensive. We present HyHTM - a Hyperbolic geometry-based Hierarchical Topic Model - that addresses these limitations by incorporating hierarchical information from hyperbolic geometry to explicitly model hierarchies in topic models. Experimental results with four baselines show that HyHTM can better attend to parent-child relationships among topics. HyHTM produces coherent topic hierarchies that specialize in granularity from generic higher-level topics to specific lower-level topics. Further, our model is significantly faster and leaves a much smaller memory footprint than our best-performing baseline. We have made the source code for our algorithm publicly accessible.

2021

1. poster

   Interpretable & Hierarchical Topic Models using Hyperbolic Geometry

   Simra Shahid, Tanay Anand, Nikitha Srikanth, and 3 more authors

   In NeurIPS 2021 WiML Workshop 1, Dec 2021

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   Topic Models (TM) are statistical models to learn latent topics present in a collection of text documents. These topics are usually not independent and represent concepts that are related hierarchically. Flat TMs such as LDA fail to capture this inherent hierarchy. To overcome these limitations, Hierarchical Topic Model (HTM) have been proposed that discover latent topics while preserving the inherent hierarchical structure between different topics (for example, aspect hierarchies in reviews and research topic hierarchies in academic repositories). Despite showing great promise, the state-of-the-art HTMs fail to capture coherent hierarchical structures. Also the number of topics in each level is usually unknown and is determined empirically, wasting a lot of time and resources. Finally, HTMs have very long training time, making them unsuitable for real-time production environments. Thus, there is a need for HTMs that (i) offers good hierarchical structures and more meaningful and interpretable topics and that (ii) can automatically find the number of topics at each level without multiple training iterations. In our work, we address the problems mentioned above by utilizing properties of hyperbolic geometry that has been successfully applied in learning hierarchical structures such as ontologies in Knowledge bases, and latent hierarchy between words. Our initial experiments have yielded promising results where the training time has reduced from weeks to less than an hour, and the quantitative metrics have improved with significantly better hierarchical structures. We have attached an abstract with a brief overview of our approach and results.