Adversarial Training Impacts on LLM Token Throughput and Latency in HumanEval

Abstract

Abstract: This report synthesises findings from 4 peer-reviewed papers addressing the following research question: How does adversarial training affect the token generation throughput and latency of large language models on the HumanEval code generation benchmark. This article presents a comprehensive and practical guide for practitioners and end-users working with Large Language Models (LLMs) in their downstream Natural Language Processing (NLP) tasks. We provide discussions and insights into the usage of LLMs from the perspectives of. 0 claims were extracted from source literature; 0 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 3.8/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does adversarial training affect the token generation throughput and latency of large language models on the HumanEval code generation benchmark?

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