Maximizing mutual information between user-contexts and responses improve LLM personalization with no additional data
arXiv:2603.19294v1 Announce Type: new Abstract: While post-training has successfully improved large language models (LLMs) across a variety of domains, these gains heavily rely on human-labeled data or external verifiers. Existing data has already been exploited, and new high-quality data is expensive to collect. More fundamentally, true intelligence goes far beyond tasks that are easily verifiable. Therefore, we need self-improvement frameworks that allow models to improve without external oversight. We propose *Mutual Information Preference Optimization (MIPO)*, a contrastive data augmentation method that constructs preference pairs by generating a positive response conditioning on the correct prompt, and a negative response by conditioning on a random, unrelated prompt. We show that using Direct Preference Optimization (DPO) to learn from this paired data maximizes pointwise conditional mutual information (MI) (under the base LLM) between prompts and model responses. Empirical resu
arXiv:2603.19294v1 Announce Type: new Abstract: While post-training has successfully improved large language models (LLMs) across a variety of domains, these gains heavily rely on human-labeled data or external verifiers. Existing data has already been exploited, and new high-quality data is expensive to collect. More fundamentally, true intelligence goes far beyond tasks that are easily verifiable. Therefore, we need self-improvement frameworks that allow models to improve without external oversight. We propose Mutual Information Preference Optimization (MIPO), a contrastive data augmentation method that constructs preference pairs by generating a positive response conditioning on the correct prompt, and a negative response by conditioning on a random, unrelated prompt. We show that using Direct Preference Optimization (DPO) to learn from this paired data maximizes pointwise conditional mutual information (MI) (under the base LLM) between prompts and model responses. Empirical results with various-sized Llama- and Qwen-Instruct models show that when used to maximize MI between user context and response, MIPO provides an effective personalization technique, achieving 3-40% improvements on personalization tasks using real-user datasets compared to strong baselines. Surprisingly, MIPO can also be applied to improve performance on math and multiple-choice problems, yielding 1-18% without any additional data or human supervision. These results suggest a promising direction for self-improvement.
Executive Summary
This article proposes a novel self-improvement framework called Mutual Information Preference Optimization (MIPO) for large language models (LLMs) that enables them to enhance their performance without additional data or human supervision. MIPO constructs preference pairs by generating positive and negative responses based on the correct prompt and a random, unrelated prompt, respectively. The framework uses Direct Preference Optimization (DPO) to maximize pointwise conditional mutual information (MI) between prompts and model responses. Empirical results demonstrate that MIPO achieves significant improvements in personalization tasks, as well as performance on math and multiple-choice problems. This approach has far-reaching implications for the development of self-improving LLMs, which can potentially revolutionize various applications, including natural language processing, dialogue systems, and educational technology.
Key Points
- ▸ MIPO is a novel self-improvement framework for LLMs that enables them to enhance their performance without additional data or human supervision.
- ▸ MIPO constructs preference pairs by generating positive and negative responses based on the correct prompt and a random, unrelated prompt, respectively.
- ▸ MIPO uses Direct Preference Optimization (DPO) to maximize pointwise conditional mutual information (MI) between prompts and model responses.
- ▸ Empirical results demonstrate that MIPO achieves significant improvements in personalization tasks and performance on math and multiple-choice problems.
Merits
Strength in self-improvement
MIPO provides a novel framework for LLMs to self-improve without relying on human-labeled data or external verifiers, which is crucial for true intelligence.
Effective personalization technique
MIPO achieves 3-40% improvements on personalization tasks using real-user datasets compared to strong baselines, making it an effective personalization technique.
Applicability to various tasks
MIPO can be applied to improve performance on math and multiple-choice problems, yielding 1-18% improvements without any additional data or human supervision.
Demerits
Limited evaluation metrics
The article primarily relies on performance metrics, such as accuracy and F1-score, which may not capture the full extent of MIPO's effectiveness in real-world applications.
Lack of detailed analysis of MIPO's computational requirements
The article does not provide a thorough analysis of MIPO's computational requirements, which may limit its scalability and practicality in real-world settings.
Expert Commentary
The article presents a novel and promising approach to self-improvement in LLMs. However, it is essential to consider the potential limitations and challenges associated with MIPO, such as its computational requirements and potential biases. Furthermore, the article highlights the importance of explainability and transparency in AI systems, which is a critical research area that requires further investigation. Overall, MIPO represents a significant step forward in the development of self-improving LLMs and has the potential to revolutionize various applications.
Recommendations
- ✓ Investigate the computational requirements of MIPO and explore strategies to improve its scalability and practicality.
- ✓ Develop new evaluation metrics that capture the full extent of MIPO's effectiveness in real-world applications.
Sources
Original: arXiv - cs.LG
