Reddit用户探讨分析Transformer探针强度的理论框架
英文摘要
The user asks how to properly balance probe capacity against the underlying network when analyzing whether a model has learned a feature, referencing an old post that used logistic regression to probe for token position. They question whether there exist theoretical guarantees about overfitting or sufficient sampling for such probes, and whether any work labels example difficulty (e.g., via ensembling) to assess probe reliability. Using a test with Gemini, they show that the model spelled "Google" correctly but still miscounted the number of 'r's, challenging the conclusion that the network truly learns position. They seek grounded theory to move beyond empirical probe comparisons.
中文摘要
该用户询问在分析模型是否学习到特定特征时，如何正确平衡探针容量与底层网络的容量，并引用了一篇用逻辑回归探测token位置的旧文。他们想了解是否存在关于过拟合或充足采样的理论保证，以及相关工作是否通过标注样本难度（如集成方法）评估探针可靠性。通过测试Gemini，他们发现模型正确拼写了“Google”却仍数错字母“r”的数量，质疑网络确实学习到位置的结论，并寻求超越纯经验比较的理论基础。
关键要点
Questions how to balance probe model capacity against the capacity of the underlying network when inferring learned features, pointing to a lack of theoretical grounding.
质疑在推断模型学到的特征时，如何平衡探针模型容量与基础网络容量，指出缺乏理论依据。
Notes that the old post’s logistic regression probe for token position may overestimate performance due to the small vocabulary size making the task artificially easy.
指出旧文章中用于探测token位置的逻辑回归探针可能因词汇量小、任务过于简单而高估性能。
Presents a counterexample: Gemini correctly spells ‘Google’ but miscounts the letter ‘r’, challenging claims that the network reliably learns token decomposition.
给出反例：Gemini正确拼写“Google”却数错字母“r”，挑战了网络可靠学习token分解的断言。
Asks whether existing work labels example difficulty (e.g., via expensive ensembling) to quantify probe reliability and out-of-distribution behavior.
询问现有工作是否通过标注样本难度（如昂贵的集成方法）来量化探针可靠性和分布外表现。