Exploring Zero-Shot and Few-Shot Learning Capabilities in LLMS for Complex Query Handling

Authors: Kartheek Kalluri

DOI: https://doi.org/10.5281/zenodo.14535426

Short DOI: https://doi.org/g8wjnn

Country: USA

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Abstract: Currently, advanced natural language processing (NLP) is present in the area that pertains to the reading of complex queries, which have recently emerged due to rapid advancement in LLMs: here, we carry out investigations on the two paradigmatic bases inherent in LLMs-zero-shot learning (ZSL)-and few-shot learning (FSL)-in tackling complex, ambiguous, and multi domain queries. ZSL is good with context, till such time it is with simple reasoning and ambiguities because it fails when it comes to reasoning and ambiguity resolution. In contrast, FSL employs a small number of task-specific input examples and exhibits very high accuracy, coherence, and even more effective contextual alignment in the performance of deeper reasoning and ambiguity resolution.
The study used both qualitative and quantitative analyses for evaluating the performance of both paradigms with an ample variety of question types. Statistical results delivered ZSL as an extraordinarily potent generalizer from abundant pre-training data, although sadly, its resulting answers lacked weight concerning complexity, especially with specialized queries. The FSL paradigm, however, was flexible with better contextualization but was limited by training on narrowly defined examples in situations with few training inputs.
Complex reasoning is limited in both since few, if any, knowledge-based tasks can be performed. Additionally, the major drawbacks which these systems carry are ethics such as biases in training data and interpretability in answers. This research said much more should be done for LLMs to augment their reasoning, context, and ethics.
ZSL and FSL look at the ways in which an effort is made to further develop the understanding and applications of LLMs. Improvement thus made will help these LLMs in a variety of applications-from customer care and academic research to the rule making process. As emphasized herein, one of the primary factors for creating accurate, but contextually appropriate, flexible AI systems is how generalization and adaptation are balanced, thereby encouraging further advancements in NLP.

Keywords: Natural Language Processing (NLP), Large Language Models (LLMs), Zero-Shot Learning (ZSL), Few-Shot Learning (FSL), Complex Queries, Ambiguity Resolution, Ethical AI

Paper Id: 231866

Published On: 2023-11-02

Published In: Volume 11, Issue 6, November-December 2023

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