Synthetic Data Quality Effects on Small Language Model Inference Efficiency

Abstract

Abstract: This report synthesises findings from 12 peer-reviewed papers addressing the following research question: What is the impact of synthetic data source quality on the inference efficiency and throughput of small language models trained for natural language inference tasks. The evolution of Generative Pre-trained Transformer (GPT) models has led to significant advancements in various natural language processing applications, particularly in legal textual entailment. We present an analysis of GPT-3.5 (ChatGPT) and GPT-4 performances on COLIEE Task 4. 7 claims were extracted from source literature; 1 was independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 4.3/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

What is the impact of synthetic data source quality on the inference efficiency and throughput of small language models trained for natural language inference tasks?

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