Analyzing the Robustness of NLP Models to Diverse Prompts
Dr Pankaj Malik
, Palak Khatri , Suhavi Khera , Tanishi Jain , Swati Jha
Robustness of NLP Models , NLP Models, Robustness of NLP
Natural Language Processing (NLP) models have demonstrated impressive capabilities in generating human-like responses, yet their robustness to diverse prompts remains a critical aspect of their performance. This research delves into the nuanced exploration of how NLP models respond to a wide range of prompts, encompassing variations in specificity, context, and style. The study aims to unravel the intricacies of model behavior under diverse inputs, shedding light on potential challenges and proposing strategies to enhance robustness.
The methodology involves the utilization of state-of-the-art NLP models trained on extensive datasets. Diverse prompts are meticulously generated to encompass a spectrum of linguistic variations, ensuring a comprehensive evaluation. Metrics and evaluation criteria are carefully chosen to provide a nuanced understanding of the models' performance under diverse conditions.
Results from the experiments reveal insightful patterns and trends in model behavior, highlighting the influence of prompt diversity on responses. Comparative analyses showcase the differential impact of prompts, allowing for a deeper understanding of how models generalize across varied inputs. The discussion section interprets these findings in the context of existing literature, exploring the factors that contribute to or mitigate robustness in the face of diverse prompts.
Challenges encountered during the study are transparently discussed, acknowledging limitations in experimental design and suggesting areas for improvement. The research concludes with a synthesis of key findings, emphasizing the significance of considering prompt diversity in the development of robust NLP models. The insights gained from this study not only contribute to the theoretical understanding of model behavior but also have practical implications for real-world applications, user interactions, and the ongoing pursuit of advancing NLP technologies.
"Analyzing the Robustness of NLP Models to Diverse Prompts", TIJER - TIJER - INTERNATIONAL RESEARCH JOURNAL (www.TIJER.org), ISSN:2349-9249, Vol.11, Issue 2, page no.a78-a86, February-2024, Available :https://tijer.org/TIJER/papers/TIJER2402009.pdf
Volume 11
Issue 2,
February-2024
Pages : a78-a86
Paper Reg. ID: TIJER_150886
Published Paper Id: TIJER2402009
Downloads: 000292
Research Area: Science and Technology
Country: Indore, Madhya Pradesh, India
ISSN: 2349-9249 | IMPACT FACTOR: 8.57 Calculated By Google Scholar | ESTD YEAR: 2014
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.57 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: TIJER (IJ Publication) Janvi Wave
