most citedEnhancing Talent Employment Insights Through Feature Extraction with LLM Finetuning

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

cs.SI2025

GitHub Stargazers | Building Graph- and Edge-level Prediction Algorithms for Developer Social Networks

Karishma Thakrar, Aniket Chauhan

Analyzing social networks formed by developers provides valuable insights for market segmentation, trend analysis, and community engagement. In this study, we explore the GitHub St…

cs.LG2025

AlgoRxplorers | Precision in Mutation: Enhancing Drug Design with Advanced Protein Stability Prediction Tools

Karishma Thakrar, Jiangqin Ma, Max Diamond +1

Predicting the impact of single-point amino acid mutations on protein stability is essential for understanding disease mechanisms and advancing drug development. Protein stability,…

cs.CL2025

DynaGRAG | Exploring the Topology of Information for Advancing Language Understanding and Generation in Graph Retrieval-Augmented Generation

Karishma Thakrar

Graph Retrieval-Augmented Generation (GRAG or Graph RAG) architectures aim to enhance language understanding and generation by leveraging external knowledge. However, effectively c…

cs.CL2025

StAyaL | Multilingual Style Transfer

Karishma Thakrar, Katrina Lawrence, Kyle Howard

Stylistic text generation plays a vital role in enhancing communication by reflecting the nuances of individual expression. This paper presents a novel approach for generating text…

cs.CL20251 cited

Enhancing Talent Employment Insights Through Feature Extraction with LLM Finetuning

Karishma Thakrar, Nick Young

This paper explores the application of large language models (LLMs) to extract nuanced and complex job features from unstructured job postings. Using a dataset of 1.2 million job p…