1 code implementation • 27 Mar 2024 • Harsh Patel, Dominique Boucher, Emad Fallahzadeh, Ahmed E. Hassan, Bram Adams
This paper investigates the complexities of integrating Large Language Models (LLMs) into software products, with a focus on the challenges encountered for determining their readiness for release.
no code implementations • 13 Oct 2023 • Harsh Patel, Yuan Zhou, Alexander P Lamb, Shu Wang, Jieliang Luo
By leveraging operational data as a foundation for the agent's actions, we enhance the explainability of the agent's actions, foster more robust recommendations, and minimize error.
1 code implementation • 9 Aug 2023 • Shivam Sahni, Harsh Patel
In the paper, we propose several data distillation techniques for multilingual text classification datasets using language-model-based learning methods.
no code implementations • 10 Apr 2023 • Kamal Kishor Rajak, Pavan Pahilani, Harsh Patel, Bhavtosh Kikani, Rucha Desai, Hemant Kumar
The GAgNP's were found to contain crystalline silver through XRD, and the particles were confirmed to be homogeneous and spherical with a size of approximately 5 nm, as evidenced by UV-Visible spectroscopy, XRD, and HR-TEM.
no code implementations • 11 Nov 2022 • Harsh Patel, Nicole Schneider, Hanan Samet
Visualizations such as bar charts, scatter plots, and objects on geographical maps often convey critical information, including exact and relative numeric values, using shapes.
no code implementations • 3 Nov 2022 • Harsh Patel, Shivam Sahni
With the growing use of deep learning methods, particularly graph neural networks, which encode intricate interconnectedness information, for a variety of real tasks, there is a necessity for explainability in such settings.
no code implementations • 19 Aug 2021 • Harsh Patel, Amey Kulkarni, Shivam Sahni, Udit Vyas
Image Inpainting is one of the very popular tasks in the field of image processing with broad applications in computer vision.
no code implementations • 18 Jan 2021 • Harsh Patel
Multi-lingual language models include Indian languages like Hindi, Telugu, Bengali in their training corpus, but they often fail to represent the linguistic features of these languages as they are not the primary language of the study.
1 code implementation • 3 Nov 2020 • Harsh Patel
Results: We introduce BioNerFlair, a method to train models for biomedical named entity recognition using Flair plus GloVe embeddings and Bidirectional LSTM-CRF based sequence tagger.
no code implementations • SEMEVAL 2019 • Harsh Patel
This paper discusses the solution to the problem statement of the SemEval19: EmoContext competition which is {''}Contextual Emotion Detection in Texts{''}.