Ratnaditya-J/IsItBenchmark

IsItBenchmark addresses the critical 'contamination' problem in LLM evaluation—where models perform deceptively well because they've seen the test questions in their training data. While the problem is high-value, the project's defensibility is minimal. With 0 stars and forks after 240+ days, it represents a stagnant personal research project rather than a living tool. Technically, the approach of matching prompts against a database of known benchmarks (GSM8K, MMLU, etc.) is a standard industry practice. Frontier labs (OpenAI, Anthropic) and specialized evaluation platforms (Giskard, Arize Phoenix, Weights & Biases) have much more robust, private versions of these 'canary' detection systems. The project lacks the necessary 'data gravity' (a massive, proprietary index of benchmark variants) or 'network effect' (community-contributed benchmarks) to survive against established evaluation frameworks or the internal safety pipelines of major AI labs. It is likely to be entirely displaced by standard library functions in major eval suites like 'inspect' or 'lm-evaluation-harness' within months.