Apr 2, 2026 · Artificial Intelligence

How to Rigorously Test Your Own Trained LLM and Choose the Right Benchmarks

This guide outlines a systematic LLM evaluation framework, covering goal definition, core and code‑oriented benchmarks, agent and safety tests, data‑contamination mitigation, toolchain choices, result reporting, and the inherent structural limits of static benchmarks.

AgentLLMSafety

0 likes · 14 min read

How to Rigorously Test Your Own Trained LLM and Choose the Right Benchmarks

Machine Learning Algorithms & Natural Language Processing

Mar 17, 2026 · Artificial Intelligence

80 Million Records Expose AI‑Generated Data Pollution Undermining Diagnostic Reliability

A large‑scale study of over 800,000 synthetic clinical records shows that self‑training loops of AI‑generated medical text, reports, and images cause severe loss of pathological diversity, vocabulary, and diagnostic confidence, prompting the authors to propose mixed‑real‑data training and quality‑aware filtering as mitigations.

AIDiagnostic reliabilitySelf‑Training

0 likes · 10 min read

80 Million Records Expose AI‑Generated Data Pollution Undermining Diagnostic Reliability

How to Rigorously Test Your Own Trained LLM and Choose the Right Benchmarks

80 Million Records Expose AI‑Generated Data Pollution Undermining Diagnostic Reliability

80 Million Records Expose AI‑Generated Data Pollution Undermining Diagnostic Reliability