Gzip 作为语言模型的局限性
问题概述
使用 gzip 压缩来评估语言模型续写的方法仅能提供下界，因为搜索空间庞大，且现有方法（如 beamsearch）可能无法找到全局最优的可压缩序列，导致对模型能力的低估或误判。
痛点标签
- 搜索空间过大，仅能得到下界的压缩评估
- 现有启发式搜索（如 beamsearch）可能错失全局最优可压缩序列
- 对压缩驱动的语言模型的能力可能被高估或低估，与大型神经网络模型的对比
待验证问题
- 是否有更高效的搜索算法可以探索更大的压缩空间？
- 用户是否愿意为此类评估工具付费？
- 是否存在更直接的语言模型可解释性指标？
证据
原话
So the result only gives us some lower bound of how well gzip works as a "plausibility tester" of a continuation of a text.
原话
The space of possible sequences is many orders of magnitude larger than what was searched. So there might be sequences in there that compress much better.The text mentions beamsearch, but I don't see a discussion about how well beamsearch performs in finding the global optima when it comes to gzip compressibility of a text?
原话
This is fun, but historically people have gone a bit overboard with saying that models like this, or n-gram language models, are anywhere close to large neural network models.
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