Fusing in-context learning and fine-tuning for language model neural networks

Patent metadata

Field	Value
Publication number	US-2025077895-A1
Application number	US-202418826005-A
Country	US
Kind code	A1
Filing date	Sep 5, 2024
Priority date	Sep 5, 2023
Publication date	Mar 6, 2025
Grant date	—

Abstract

Methods, systems, and apparatuses, including computer programs encoded on computer storage media, for configuring a set of language model neural networks, e.g., a first large language model and a second smaller-sized language model, and performing a machine learning task on new inputs using the set of language model neural networks. Configuring the language model neural networks and performing a machine learning task can include leveraging the ability of a first large language model to follow prompt-engineered instructions and perform chain-of-thought reasoning, while also fine-tuning a second, smaller language model neural network to optimize the machine learning task performance.

First claim

What is claimed is:

1 . A method performed by one or more computers, the method comprising:
receiving a new input for a machine learning task; processing a first input sequence comprising (i) a first prompt for the machine learning task and (ii) the new input using a first language model neural network to generate an intermediate output for the machine learning task; and processing a second input sequence comprising the intermediate output generated by the first language model neural network and the new input using a second language model neural network to generate a final output for the machine learning task.

2 . The method of claim 1 , wherein the first machine learning model has been pre-trained on a second, different machine learning task that is different from the machine learning task.

3 . The method of claim 2 , wherein the second machine learning model has been pre-trained on a third, different machine learning task that is different from the machine learning task and then fine-tuned on the machine learning task.

4 . The method of claim 3 , wherein the second machine learning model has been fine-tuned on the machine learning task through parameter-efficient fine-tuning.

5 . The method of claim 2 , wherein the second machine learning task, the third machine learning task, or both is a language modeling task.

6 . The method of claim 1 , wherein the second input sequence further comprises a second prompt for the machine learning task.

7 . The method of claim 1 , wherein the first prompt is a chain-of-thought prompt for the machine learning task and the intermediate output comprises text describing one or more reasoning steps generated by the first language model neural network.

8 . A method performed by one or more computers and for configuring a first language model neural network and a second language model neural network to perform a machine learning task, the method comprising:
obtaining training data comprising a plurality of training inputs for the machine learning task and a respective target output for the machine learning task for each of the training inputs; obtaining data specifying a first prompt for the machine learning task; for each of the training inputs:
processing a first input sequence comprising the first prompt for the machine learning task and the training input using the first language model neural network to generate an intermediate output for the machine learning task; and
processing a second input sequence comprising the intermediate output generated by the first language model neural network and the training input using the second language model neural network to generate a training output for the machine learning task; and
training the second language model neural network to perform the machine learning task using an objective function that is based on, for each training input, the training output for the training input and the target output for the training input.

9 . The method of claim 8 , wherein the first prompt is a chain-of-thought prompt for the machine learning task and the intermediate output comprises text describing one or more reasoning steps generated by the first language model neural network.

10 . The method of claim 8 , further comprising:
obtaining a second prompt for the machine learning task, wherein the second input sequence comprises the second prompt.

11 . The method of claim 8 , wherein training the second language model neural network to perform the machine learning task comprises:
training the second language model neural network through parameter-efficient fine-tuning.

12 . The method of claim 8 , wherein the second input sequence further comprises the target output for the training input, wherein the training output comprises a respective probability distribution for each position in the target output, and wherein the objective function measures, for each position, a probability assigned by the probability distribution for the position to a token at the position in the target output.

13 . The method of claim 8 , wherein the first language model neural network has more parameters than the second language model neural network.

14 . The method of claim 8 , wherein the first language model neural network is not trained on the machine learning task.

15 . The method of claim 8 , wherein training the second language model neural network comprises:
training the second language model neural network while holding the first language model neural network fixed.

16 . A system comprising:
one or more computers; and one or more storage devices storing instructions that, when executed by the one or more computers, cause the one or more computers to perform operations comprising: receiving a new input for a machine learning task; processing a first input sequence comprising (i) a first prompt for the machine learning task and (ii) the new input using a first language model neural network to generate an intermediate output for the machine learning task; and processing a second input sequence comprising the intermediate output generated by the first language model neural network and the new input using a second language model neural network to generate a final output for the machine learning task.

17 . The system of claim 16 , wherein the second machine learning model has been fine-tuned on the machine learning task through parameter-efficient fine-tuning.

18 . The system of claim 16 , wherein the second machine learning task, the third machine learning task, or both is a language modeling task.

19 . The system of claim 16 , wherein the second input sequence further comprises a second prompt for the machine learning task.

20 . The system of claim 16 , wherein the first prompt is a chain-of-thought prompt for the machine learning task and the intermediate output comprises text describing one or more reasoning steps generated by the first language model neural network.

Assignees

• Google Llc

Inventors

• Wang Xinyi
• Wieting John Frederick
• Clark Jonathan Hudson

Classifications

• G06N3/08

  Learning methods · CPC title

• G06N3/0985 Primary

  Hyperparameter optimisation; Meta-learning; Learning-to-learn · CPC title

• G06N3/045 Primary

  Combinations of networks · CPC title