pineconedad/blind-spots-evaluation
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 blind spot in current language models: Magnitude Starvation ("Quietly Learned" Tokens). Standard benchmarks (like MMLU or GSM8K) treat vocabulary representations as uniform and assume low-frequency tokens are completely unlearned… See the full description on the dataset page: https://huggingface.co/datasets/pineconedad/blind-spots-evaluation.
| 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 |
Recent changes
- 21 Oct 2026 License: none → apache-2.0
- 21 Oct 2026 Description: none → 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
- 21 Oct 2026 Topics added representation-engineering, undertrained-tokens, magnitude-starvation, multilingual-equity
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