Row 5115

Row ID: 5115 | Dataset Entry | Axioma AXP Content Repository

Content Data

This page contains data entry 5115 from the Axioma AXP content repository. The structured data below represents the complete record for this entry.

Everywhere I look for the answer to this question, the responses do little more than anthropomorphize the model. They invariably make claims like:

> *Without examples, the model must infer context and rely on its knowledge to deduce what is expected. This could lead to misunderstandings.*

> *One-shot prompting reduces this cognitive load by offering a specific example, helping to anchor the model's interpretation and focus on a narrower task with clearer expectations.*

> *The example serves as a reference or hint for the model, helping it understand the type of response you are seeking and triggering memories of similar instances during training.*

> *Providing an example allows the model to identify a pattern or structure to replicate. It establishes a cue for the model to align with, reducing the guesswork inherent in zero-shot scenarios.*

These are real excerpts, btw.

But these models don’t “understand” anything. They don’t “deduce”, or “interpret”, or “focus”, or “remember training”, or “make guesses”, or have literal “cognitive load”. They are just statistical token generators. Therefore pop-sci explanations like these are kind of meaningless when seeking a concrete understanding of the exact mechanism by which in-context learning improves accuracy.

Can someone offer an explanation that explains things in terms of the actual model architecture/mechanisms and how the provision of additional context leads to better output? I can “talk the talk”, so spare no technical detail please.

I could make an educated guess - Including examples in the input which use tokens that approximate the kind of output you want leads the attention mechanism and final dense layer to weight more highly tokens which are similar in some way to these examples, increasing the odds that these desired tokens will be sampled at the end of each forward pass; like fundamentally I’d guess it’s a similarity/distance thing, where explicitly exemplifying the output I want increases the odds that the output get will be similar to it - but I’d prefer to hear it from someone else with deep knowledge of these models and mechanisms.

Field	Value
text	Everywhere I look for the answer to this question, the responses do little more than anthropomorphize the model. They invariably make claims like: > Without examples, the model must infer context and rely on its knowledge to deduce what is expected. This could lead to misunderstandings. > One-shot prompting reduces this cognitive load by offering a specific example, helping to anchor the model's interpretation and focus on a narrower task with clearer expectations. > *The example serves a…
label	r/datascience
dataType	post
communityName	r/datascience
datetime	2024-04-26
username_encoded	Z0FBQUFBQm5LakwyTnJUV2RobUVMaXppUEJFWUwzTnItaC1vemxHMDUwWHNXZ0hlaWUxRnFYeXNGSWN0S0wxdEZQUk9SbFY1VHFjTVJfR05mUFZhcmtNNG02NzNyWEItblE9PQ==
url_encoded	Z0FBQUFBQm5Lak9GTURjZDhYUkxQdHRVS3ZKVlJrcncwUlRQWjFTVk5OWlZtOHJkRlZHMWVvakNyN2w1VFhSUW5WVHlwbmppN2pXMDVvV29hRkpUY0hNWmpRNlF6c3VTcmZnUnFuRGp3c1UxdE5ndXhlSF9qSXpKQnR3ZTdlZ0w3cnhKYlJSdTAyYmFpMHNPVEtEYVlNMWNDMzlEdG8zTklFM3Y1OVdxUF9wUG02NTQ2bjNrNlA0RE1mc3ZwZTN5V2FHSFZuWW02Nk1hVnJ5YXdsS3ZlLVlHd0VoN25ELTZsZz09

Raw Record

{
  "text": "Everywhere I look for the answer to this question, the responses do little more than anthropomorphize the model. They invariably make claims like:\n\n> *Without examples, the model must infer context and rely on its knowledge to deduce what is expected. This could lead to misunderstandings.*\n\n> *One-shot prompting reduces this cognitive load by offering a specific example, helping to anchor the model's interpretation and focus on a narrower task with clearer expectations.*\n\n> *The example serves as a reference or hint for the model, helping it understand the type of response you are seeking and triggering memories of similar instances during training.*\n\n> *Providing an example allows the model to identify a pattern or structure to replicate. It establishes a cue for the model to align with, reducing the guesswork inherent in zero-shot scenarios.*\n\nThese are real excerpts, btw.\n\nBut these models don’t “understand” anything. They don’t “deduce”, or “interpret”, or “focus”, or “remember training”, or “make guesses”, or have literal “cognitive load”. They are just statistical token generators. Therefore pop-sci explanations like these are kind of meaningless when seeking a concrete understanding of the exact mechanism by which in-context learning improves accuracy.\n\nCan someone offer an explanation that explains things in terms of the actual model architecture/mechanisms and how the provision of additional context leads to better output? I can “talk the talk”, so spare no technical detail please.\n\nI could make an educated guess - Including examples in the input which use tokens that approximate the kind of output you want leads the attention mechanism and final dense layer to weight more highly tokens which are similar in some way to these examples, increasing the odds that these desired tokens will be sampled at the end of each forward pass; like fundamentally I’d guess it’s a similarity/distance thing, where explicitly exemplifying the output I want increases the odds that the output get will be similar to it - but I’d prefer to hear it from someone else with deep knowledge of these models and mechanisms.",
  "label": "r/datascience",
  "dataType": "post",
  "communityName": "r/datascience",
  "datetime": "2024-04-26",
  "username_encoded": "Z0FBQUFBQm5LakwyTnJUV2RobUVMaXppUEJFWUwzTnItaC1vemxHMDUwWHNXZ0hlaWUxRnFYeXNGSWN0S0wxdEZQUk9SbFY1VHFjTVJfR05mUFZhcmtNNG02NzNyWEItblE9PQ==",
  "url_encoded": "Z0FBQUFBQm5Lak9GTURjZDhYUkxQdHRVS3ZKVlJrcncwUlRQWjFTVk5OWlZtOHJkRlZHMWVvakNyN2w1VFhSUW5WVHlwbmppN2pXMDVvV29hRkpUY0hNWmpRNlF6c3VTcmZnUnFuRGp3c1UxdE5ndXhlSF9qSXpKQnR3ZTdlZ0w3cnhKYlJSdTAyYmFpMHNPVEtEYVlNMWNDMzlEdG8zTklFM3Y1OVdxUF9wUG02NTQ2bjNrNlA0RE1mc3ZwZTN5V2FHSFZuWW02Nk1hVnJ5YXdsS3ZlLVlHd0VoN25ELTZsZz09"
}

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Entry Information

Entry ID: 5115
Repository: Axioma AXP
Dataset: arrmlet/reddit_dataset_36
Total Entries: 100,000