Does scaling the LLM size (e.g., 7B vs. 70B parameters) mitigate the accuracy loss from adversarial perturbati

Abstract

Abstract: This comprehensive review delves into the pivotal role of prompt engineering in unleashing the capabilities of Large Language Models (LLMs). The development of Artificial Intelligence (AI), from its inception in the 1950s to the emergence of advanced neural networks and deep learning architectures, has made a breakthrough in LLMs, with models such as GPT-4o and Claude-3, and in Vision-Language Models (VLMs), with models such as CLIP and ALIGN. Prompt engineering is the process of structuring inputs, which has emerged as a crucial technique to maximize the utility and accuracy of these models.

Research Question

Does scaling the LLM size (e.g., 7B vs. 70B parameters) mitigate the accuracy loss from adversarial perturbations in multi-hop RAG, compared to single-hop RAG, on the FEVER and NQ subsets of BEIR?

Verification Level

Descriptive public verification status only; aggregate claim counts are public, but individual claim records are not exposed here.

Quality Tier

Descriptive public triage only; this tier does not alter current publication or DOI behavior.

Quality Dimensions

Automated triage signals derived from public fields; not human peer review or independent validation.

Correction Record

Public corrections are additive records. Current status does not claim the synthesis is error-free.

Provenance