license: mit
task_categories:
- other
tags:
- mechanistic-interpretability
- robustness
- language-model-evaluation
- perturbation-analysis
Decoding Robustness Results
Mechanistic robustness evaluation results for language models under six input perturbations: character replacement, BPE-token replacement, word replacement, local token shuffle, typographical corruption, and synonym replacement.
The repository is organized by model and perturbation:
models/<model>/<perturbation>/<percentage>/evals.csv
The qwen2.5_1.5b/adversarial directory contains the separate adversarial
evaluation outputs and manifest. Failed or mislabeled Kaggle runs are not
included in the canonical result directories.
Recovered model coverage currently includes complete sweeps for GPT-2,
GPT-2 Medium, GPT-2 XL, Qwen 2.5 0.5B, and Qwen 2.5 1.5B. GPT-2 Large is
represented by a partial legacy run under models/gpt2-large/; it contains
11 char percentages and 2 valid token percentages. An empty GPT-2 Large
token/20 file and an empty legacy GPT-2 XL file were excluded as invalid
results. The GPT-2 XL split-A and split-B outputs are merged under
models/gpt2-xl/.
Source dataset: WikiText-2 raw test split (Salesforce/wikitext,
wikitext-2-raw-v1).