Personalized LLM Fine-Tuning With User Context Evaluation
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Solution Overview
Problem
Existing large language models (LLMs) lack personalization due to general-purpose training on vast user-agnostic datasets, making it difficult to tailor responses to individual user contexts and preferences.
Innovation Solution
A personalized response system is developed by finetuning a machine-learned language model using user-specific training examples, including user requests, activities, and satisfaction scores, and applying an evaluation model to generate personalized responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a large language model is trained on vast user-agnostic training datasets for general purposes, then the model achieves a vast and deep knowledge base and excels at answering general questions, but the model lacks personalization and cannot incorporate user context
Solution Approach 1:
The training dataset is segmented into two distinct components: a general training dataset for building foundational knowledge and a user-specific training dataset for personalization. This segmentation allows the model to maintain general knowledge while adapting to individual user contexts through separate training phases.
Solution Approach 2:
User context information is prepared and integrated into the training dataset before model training begins. User profiles, interaction histories, and preference data are pre-processed and incorporated into training examples, enabling the model to learn personalization patterns during the training phase rather than requiring post-training adaptation.
2Adaptability or versatility
If a concierge system attempts to comprehend and meet customers' particular requests through machine learning models, then personalization can be achieved, but it becomes technically difficult to tune LLM to incorporate user context
Solution Approach 1:
The system merges general language model capabilities with user-specific adaptation by combining training on general datasets with training on user-specific datasets. This unified training approach simplifies the tuning process compared to attempting post-training personalization, as personalization is achieved through integrated training rather than complex model modification.
Solution Approach 2:
User context is introduced as an intermediary element that bridges general language understanding and personalized responses. The system uses user profiles and interaction histories as intermediate representations that guide the model's response generation, making personalization achievable without directly modifying the core language model architecture.
3Quantity of substance
If training data includes different data sources without incorporating user context, then comprehensive general knowledge is achieved, but personalization for individual users cannot be provided
Solution Approach 1:
The training data is segmented into general knowledge sources and user-specific sources. This segmentation enables the system to process vast amounts of general training data while separately integrating user context information, allowing both comprehensive knowledge and personalization to coexist in the training process.
Solution Approach 2:
User context is incorporated as an additional dimension in the training data structure. Rather than replacing general training data, the system adds user-specific information as a complementary dimension, enabling the model to process both general knowledge and personalization requirements simultaneously during training.
Data Source
AI summary
A computer system finetunes a machine-learned language model to generate a personalized response to a user request. The system may generate a user representation for each of a plurality of users by applying a transformer model to a sequence of tokens representing a sequence of activities of the user. The system may train an evaluation model coupled to receive a user representation and a response to a user request and generate an estimated evaluation score indicating a level of personalization of the response to the user. The system may finetune a first machine-learned language model to generate a second machine-learned language model. The finetuned machine-learned language model is configured to provide personalized responses for customer services at an online concierge system.