pineconedad/undertrained-tokens-0.6B-6B-benchmark
Technical Challenge: Blind Spots of Frontier Models Question 1: Critical Blind Spot, Bias, or Capability Gap While investigating under-trained ("glitch") tokens for my undergraduate thesis, I encountered a mechanistic anomaly that standard LLM benchmarks and existing token-detection papers overlook: "under-trained" tokens are not uniformly unlearned across model families and embedding architectures. Standard benchmarks (like MMLU or GSM8K) only probe… See the full description on the dataset page: https://huggingface.co/datasets/pineconedad/undertrained-tokens-0.6B-6B-benchmark.
| Author / organization | pineconedad |
|---|---|
| License (as declared) | apache-2.0 |
| Downloads | 0 (as reported by Hugging Face) |
| Likes | 0 |
| Created | 21 October 2026 |
| Last updated at source | 21 October 2026 |
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- 21 Oct 2026 Description: Technical Challenge: Blind Spots of Frontier Models Question 1: Critical Blind Spot, Bias, or Capability Gap Drawing directly from undergraduate thesis research into tokenization failure modes and model internals, we identify a critical bli → Technical Challenge: Blind Spots of Frontier Models Question 1: Critical Blind Spot, Bias, or Capability Gap While investigating under-trained ("glitch") tokens for my undergraduate thesis, I encountered a mechanistic anomaly that standard
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