结构化输出与性能透明度的需求
问题概述
用户希望在使用类似 Jev 的小型分类模型时，能够获得清晰的延迟/计算资源对比、错误率及输出格式评估，并明确速度宣称的相对基准，以避免信息模糊和误导。
痛点标签
- 缺乏与 Jev 相同的延迟和计算资源对比
- 缺少错误率及解析格式的评估信息
- 对速度宣称的相对性不明确
待验证问题
- 是否有公开的延迟和计算资源基准数据可供参考？
- 错误率和输出格式是否已有量化评估？
- 速度宣称的相对基准是否明确给出？
证据
原话 · @Anonymous
Beyond the missing latency and compute comparisons that Heaney commenter mentioned, also nothing about its error rate compared to Jev (nor if it even always outputs in a format the app can parse, not sure how solved that is).But then at the end it says it’s parody. Maybe HN title should say it’s a joke.
原话 · @Anonymous
It's fast.If you're comparing with something, you need to state 'fast' in relative terms. Jev is definitely fast, and if this Python takes the same time to get a decision then it's also fast. If it's 100* slower than Jev though, you shouldn't be calling it 'fast', because relatively speaking it's really, really slow.
原话 · @Anonymous
Because of masked attention in LLMs, if you put the options before the body (the email to analyze), the transformer already knows what it needs to look for, and can use more tokens to create state to address that specific task (BERT has no mask in the attention, so tokens attend also to next tokens). You could also do a few examples in the system prompt to improve calibration.Another trick that works is to repeat the question two times: "I'm repeating the task and labels for clarity: ..."
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