All-shot training for large language models
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Solution Overview
Problem
Existing techniques for building large language models, such as in-context learning and instruction tuning, face challenges in efficiency, stability, and generalizability, particularly in effectively leveraging multiple examples within a context window.
Innovation Solution
The proposed 'all-shot' training technique employs in-context learning to teach the model to learn 'how to learn in context' by providing a full range of possible examples within a given context window, allowing the model to perform well regardless of the number of examples provided.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If instruction tuning is used to teach the model to follow instructions, then the model's ability to follow human instructions is improved, but the model loses generalizability
Solution Approach 1:
The patent segments the training process into multiple passes, where each pass trains on a subset of examples with different numbers of shots (0-shot, 1-shot, 2-shot, etc.). This segmentation allows the model to learn instruction following at different levels of abstraction without overfitting to any single pattern, thereby maintaining generalizability while improving instruction following capability.
Solution Approach 2:
The patent performs preliminary training with a diverse set of examples covering multiple shot counts before the model is deployed. This preliminary action equips the model with a broad foundation of learning patterns, enabling it to adapt to various instruction types and generalization scenarios without requiring retraining, thus resolving the contradiction between instruction following and generalizability.
2Reliability
If in-context learning is used to provide example prompts and completions, then the model's performance on unseen prompts is improved, but the computational cost increases and stability decreases
Solution Approach 1:
The patent applies partial in-context learning by providing a limited number of examples (shots) in the context window rather than all possible examples. By training on a representative sample of examples with varying shot counts, the model achieves good performance on unseen prompts without the excessive computational cost of processing every possible example, thus balancing performance and efficiency.
Solution Approach 2:
The patent changes the parameter of shot count dynamically during training, exposing the model to different numbers of examples (0-shot, 1-shot, 2-shot, etc.). This parameter variation allows the model to learn flexible patterns that generalize well to unseen prompts while keeping the context window size and computational cost manageable, resolving the contradiction between performance and computational expense.
3Reliability
If prompt engineering is used to ensure in-context learning performance, then the model's performance is improved, but the human effort and experimentation required increases
Solution Approach 1:
The patent enables the model to learn prompt patterns autonomously through self-service training on diverse examples with varying shot counts. By training the model to automatically adapt to different prompt formats and patterns without human intervention in prompt design, the system reduces the need for complex prompt engineering while maintaining high performance, thus resolving the contradiction between performance and human effort.
4Reliability
If more examples are provided in the context window, then the model's learning capability is improved, but the context window capacity is consumed
Solution Approach 1:
The patent makes the number of examples (shots) dynamic rather than fixed, allowing the model to learn from varying quantities of examples in different training passes. This dynamic approach enables the model to maximize its learning capability by utilizing as many examples as fit within the context window capacity, while the variability in shot counts prevents the model from becoming dependent on a fixed number of examples, thus resolving the contradiction between learning capability and context window capacity.
Data Source
AI summary
Embodiments described herein provide systems and techniques for training large language models. In one aspect, a process for performing in-context training of a language model is disclosed. This process may begin by receiving a language model that includes a context window of a predetermined size, as well as receiving a set of in-context prompt/completion pairs prepared for a target task. The process then constructs a first token sequence based on the set of in-context prompt/completion pairs. Next, the process fits the first token sequence into the context window. The process subsequently performs a first in-context training pass using the first token sequence to train the language model to generate a next token in accordance with the target task.