Small Language Models Under Adversarial Prompts: Reasoning Robustness vs. Large Baselines

Abstract

Abstract: This report synthesises findings from 13 peer-reviewed papers addressing the following research question: To what extent do small language models evaluated in SLM-Bench maintain reasoning accuracy when subjected to adversarial prompt perturbations compared to larger LLM baselines. Large language models (LLMs) have demonstrated impressive capabilities in natural language processing. However, their internal mechanisms are still unclear and this lack of transparency poses unwanted risks for downstream applications. 10 claims were extracted from source literature; 10 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.2/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

To what extent do small language models evaluated in SLM-Bench maintain reasoning accuracy when subjected to adversarial prompt perturbations compared to larger LLM baselines?

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