Row 8551

Row ID: 8551 | Dataset Entry | Axioma AXP Content Repository

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I always thought it's essentially glorified and structured prompt engineering (very useful still IMO), but it also claims in the docs that it fine-tunes and changes LM weights, and then absolutely refuses to elaborate on this in any of the sections in their docs.

I don't even understand how it can change the actual parameters of the LM, especially if we're using third party API calls for the LMs.

By LM weights, I assume it means the weights of the last layers of the transformer model. When they describe optimizers, they say "DSPy introduces new optimizers, which are LM-driven algorithms that can tune the prompts and/or the weights of your LM calls, given a metric you want to maximize."

Am I misunderstanding what they mean by LM weights?

I'm sorry if this is a stupid question, but I just can't seem to find any information about this. Thanks in advance!

FieldValue
text I always thought it's essentially glorified and structured prompt engineering (very useful still IMO), but it also claims in the docs that it fine-tunes and changes LM weights, and then absolutely refuses to elaborate on this in any of the sections in their docs. I don't even understand how it can change the actual parameters of the LM, especially if we're using third party API calls for the LMs. By LM weights, I assume it means the weights of the last layers of the transformer model. When th…
label r/machinelearning
dataType post
communityName r/MachineLearning
datetime 2024-05-19
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{
  "text": "I always thought it's essentially glorified and structured prompt engineering (very useful still IMO), but it also claims in the docs that it fine-tunes and changes LM weights, and then absolutely refuses to elaborate on this in any of the sections in their docs.\n\nI don't even understand how it can change the actual parameters of the LM, especially if we're using third party API calls for the LMs. \n\nBy LM weights, I assume it means the weights of the last layers of the transformer model. When they describe optimizers, they say \"DSPy introduces new optimizers, which are LM-driven algorithms that can tune the prompts and/or the weights of your LM calls, given a metric you want to maximize.\"\n\nAm I misunderstanding what they mean by LM weights?\n\nI'm sorry if this is a stupid question, but I just can't seem to find any information about this. Thanks in advance!",
  "label": "r/machinelearning",
  "dataType": "post",
  "communityName": "r/MachineLearning",
  "datetime": "2024-05-19",
  "username_encoded": "Z0FBQUFBQm5Lakw0YjJWdEJ6TWI2cmx1R1ZKUVc0dzlEM0VzZjZ1NEpTVTJPakpYcnJWcW5rRE5GbzN0ZW5MS1ZOcmFfUjVoTENLQlJocW4wdVN5cTkwLTVNbmZqUko1YkE9PQ==",
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}
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