A Practical Review of Mechanistic Interpretability for Transformer-Based Language Models — mentioned by _navierstokes

Thu, 12 Sep 2024 20:57:49 GMT

Skipping some detail: the model applies many high-dimensional functions to the input, and we don't know the reasoning for why these functions solve the problem. Reducing the dimension of the weights to human-readable values is non-trivial, and multiple neurons interact in unpredictable ways. Interpretability research has resulted in many useful results and pretty visualizations[1][2], and there are many efforts to understand Transformers[3][4] but we're far from being able to completely explain the large models currently in use. [1] - https://distill.pub/2018/building-blocks/ [2]…

Paper and discussion history. A mention is not an endorsement.

A Practical Review of Mechanistic Interpretability for Transformer-Based Language Models — HN submission

Sun, 07 Jul 2024 18:10:12 GMT

Review of Mechanistic Interpretability for Transformer-Based Language Models

2 points; 0 discussion replies on HN. Paper and source history. A submission is not an endorsement.

A Practical Review of Mechanistic Interpretability for Transformer-Based Language Models · HackerPapers discussions

A Practical Review of Mechanistic Interpretability for Transformer-Based Language Models — mentioned by _navierstokes

A Practical Review of Mechanistic Interpretability for Transformer-Based Language Models — HN submission