Language Model Parameter Deltas for Fast Alignment Transfer
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Solution Overview
Problem
Existing methods for aligning language models (LMs) are computationally intensive and time-consuming, requiring substantial resources and financial costs due to formal alignment operations like reinforcement learning from human feedback (RLHF) and direct preference optimization (DPO).
Innovation Solution
A method that adjusts neural network (NN) parameters of an LM using delta values derived from a previous alignment operation, allowing the LM to align with desired tones, voices, or safety preferences without performing formal alignment operations, thereby reducing computational demands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If formal alignment operations (RLHF, DPO) are performed to align language models with desired tone, voice, or safety preferences, then alignment performance is improved, but computational resources and time consumption increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing alignment delta values from formal alignment operations during a baseline phase. These pre-computed delta values capture the parameter changes needed to achieve desired alignment. During subsequent inference phases, these pre-computed deltas are applied directly without repeating the costly formal alignment operations, thus reducing real-time computational resource consumption while maintaining alignment performance.
Solution Approach 2:
The patent uses copying by creating a simplified representation of the formal alignment operation through delta value snapshots. Instead of replicating the entire complex RLHF or DPO process, the system copies the essential parameter adjustments (delta values) that achieved alignment in the baseline phase. This copied representation can be applied efficiently during inference, reducing the need for computationally intensive re-execution of formal alignment operations.
2Reliability
If formal alignment operations are performed to ensure safety and ethical standards, then model reliability is improved, but alignment time increases significantly
Solution Approach 1:
The system performs the time-consuming formal alignment operations during an initial baseline phase when time is not constrained. The resulting delta values are pre-computed and stored. During subsequent deployment phases, the system applies these pre-computed deltas instantly, eliminating the need to repeat time-consuming alignment procedures while maintaining safety and ethical standards.
Solution Approach 2:
The patent treats the formal alignment operation as a one-time expensive process that creates a reusable artifact (delta values). The actual alignment adjustment during inference uses these pre-computed, lightweight delta values rather than re-executing the full formal alignment process, significantly reducing operational time while maintaining reliability.
3Productivity
If delta values are applied to adjust NN parameters without formal alignment operations, then computational resources and time are reduced, but alignment accuracy may be compromised
Solution Approach 1:
The system incorporates feedback mechanisms where the baseline phase includes evaluation of alignment quality using formal alignment operations and human feedback. The delta values are computed based on this feedback-driven process. During inference, these feedback-validated delta values are applied to ensure that alignment accuracy is maintained while achieving faster processing speeds through avoidance of repeated formal alignment operations.
Data Source
AI summary
Systems and methods for aligning a language model (LM) are disclosed herein. An example method is performed by one or more processors of a computing system. The example method may include: receiving, over a communications network coupled to the computing system, an LM including a set of neural network parameters; obtaining a set of delta values representative of a difference between a prior LM's neural network parameters before a performance of an alignment operation and the prior LM's neural network parameters after the performance of the alignment operation, the alignment operation performed using alignment data for aligning the prior LM's output with at least one of a tone, voice, or safety preference; and adjusting the LM's neural network parameters based on the set of delta values such that, without undergoing the alignment operation, an expected output of the LM aligns with the at least one tone, voice, or safety preference.