Personalized Language Models for Conversational AI Systems
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Solution Overview
Problem
Large language models (LLMs) face challenges in aligning with diverse user preferences due to the 'alignment tax' from finetuning with reinforcement learning, which reduces diversity and is sensitive to hyperparameters, requiring complex processes and extensive data collection.
Innovation Solution
A system that tunes LLMs using multiple reward models based on different values, such as human feedback and metadata attributes, to generate personalized responses by converting discrete reward values into vector embeddings and combining them with the model input, allowing for more flexible and accurate preference alignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LLMs are finetuned using reinforcement learning with human feedback to align with user preferences, then the accuracy of responses regarding specific tasks is improved, but the diversity of responses is reduced and the process becomes complex with extensive data collection requirements
Solution Approach 1:
The patent segments the alignment process by separating preference modeling from language model finetuning. Instead of directly finetuning the LLM with RLHF, the system first trains a separate preference model on human feedback data, then uses this preference model to guide the LLM through a simplified finetuning process. This segmentation allows the LLM to maintain its diversity while achieving task-specific accuracy through the preference model's guidance.
Solution Approach 2:
The patent introduces a preference model as an intermediary between human feedback and the LLM. This preference model acts as a mediator that translates diverse user preferences into actionable guidance for the LLM, enabling the LLM to adapt to specific tasks without directly processing extensive human feedback data. The intermediary preserves response diversity while improving task-specific accuracy.
2Measurement precision
If LLMs are finetuned using reinforcement learning to align with user preferences, then the accuracy of responses is improved, but the process complexity and hyperparameter sensitivity increase
Solution Approach 1:
The patent divides the complex RLHF process into two distinct stages: (1) training a preference model on human feedback data, and (2) using this preference model to guide LLM finetuning. This segmentation simplifies the overall process by handling preference learning separately, reducing the complexity of the LLM finetuning stage and making hyperparameter tuning more manageable.
Solution Approach 2:
The preference model serves as an intermediary that absorbs much of the complexity from human feedback processing. By pre-training this separate model to capture user preferences, the system reduces the complexity of the subsequent LLM finetuning process, as the LLM only needs to adapt to the preference model's guidance rather than directly processing complex human feedback signals.
3Adaptability or versatility
If multiple separate models are trained to handle different user preferences, then the adaptability to diverse preferences is improved, but the resource consumption and system complexity increase
Solution Approach 1:
The patent implements a single LLM that can handle multiple user preferences through the unified preference model framework. The preference model is designed to capture diverse user preferences across different tasks and domains, allowing one LLM to adapt to various preferences rather than requiring separate specialized models. This universal approach reduces the quantity of models needed while maintaining high adaptability.
Solution Approach 2:
The patent merges multiple preference handling capabilities into a single integrated system. Instead of maintaining separate models for different preferences, the system combines preference learning and language generation into one unified LLM framework guided by a single preference model. This merging reduces system complexity and resource consumption while preserving the ability to adapt to diverse user preferences.
Data Source
AI summary
Disclosed are systems and techniques for training personalized language models. The techniques include applying a plurality of first machine learning models to a first input prompt. Each of the plurality of first machine learning models generates a respective reward value of a first plurality of reward values. The techniques include applying a second machine learning model to the first plurality of reward values to obtain first reward value embeddings; applying a third machine learning model to the first reward value embeddings and the first input prompt to obtain a first output response; calculating a first loss based on a comparison between the first output response and the first input prompt; and causing the second machine learning model to be modified based on the first loss.