On the Way to LLM Personalization: Learning to Remember User Conversations (2411.13405v1)

Abstract: LLMs have quickly become an invaluable assistant for a variety of tasks. However, their effectiveness is constrained by their ability to tailor responses to human preferences and behaviors via personalization. Prior work in LLM personalization has largely focused on style transfer or incorporating small factoids about the user, as knowledge injection remains an open challenge. In this paper, we explore injecting knowledge of prior conversations into LLMs to enable future work on less redundant, personalized conversations. We identify two real-world constraints: (1) conversations are sequential in time and must be treated as such during training, and (2) per-user personalization is only viable in parameter-efficient settings. To this aim, we propose PLUM, a pipeline performing data augmentation for up-sampling conversations as question-answer pairs, that are then used to finetune a low-rank adaptation adapter with a weighted cross entropy loss. Even in this first exploration of the problem, we perform competitively with baselines such as RAG, attaining an accuracy of 81.5% across 100 conversations.

Overview of "On the Way to LLM Personalization: Learning to Remember User Conversations"

The paper "On the Way to LLM Personalization: Learning to Remember User Conversations" introduces an exploration into enhancing personalization in LLMs by addressing the challenge of retaining and utilizing prior user conversations. The authors, Lucie Charlotte Magister et al., propose a novel pipeline named PLUM (Pipeline for Learning User Conversations in LLMs) that aims to efficiently inject this conversational knowledge into LLMs. This approach is particularly designed within a parameter-efficient framework, acknowledging the constraints of scalability and storage.

Key Contributions

The paper primarily contributes by recognizing the limitations of current personalization efforts, which have mostly revolved around style transfer or the incorporation of isolated facts about users. Unlike existing Retrieval Augmented Generation (RAG) methods, which rely on maintaining external storage with potential degradation in performance due to context window size, PLUM seeks to augment LLMs directly. This approach facilitates user-specific personalization without the need for external repositories.

PLUM's methodology involves two stages: data augmentation and parameter-efficient finetuning. Initially, user conversations are transformed into question-answer (QA) pairs, which are then employed for finetuning the LLM. This is achieved with the help of Low-Rank Adaptation (LoRA) adapters and a tailored weighted cross-entropy loss, aiming for a balance between positive and negative QA samples. Impressively, PLUM achieves an accuracy of 81.5% across 100 conversations, which remains competitive with established baselines like RAG.

Implications

From a theoretical standpoint, the research underscores the importance of sequentially ordered data in conversation memory and constraints surrounding efficient parameter usage. Practically, this work heralds potential advancements in creating more personalized user experiences through LLMs, allowing models to engage in less repetitive and more contextually coherent interactions over time.

Speculation on Future Developments

Looking ahead, the integration of PLUM into LLMs could contribute to a new paradigm in AI personalization. This methodology might pave the way for more adaptive models that evolve with user interactions, potentially extending their application to broader AI systems beyond mere conversation recall. As LLMs continue to embed conversations more seamlessly, we can anticipate deeper personalization, improved user satisfaction, and enhanced task performance across diversified applications. Further research may focus on refining the PLUM process, tailoring it to handle multilingual data or dynamically adjusting training epochs based on conversation complexity.

In conclusion, "On the Way to LLM Personalization: Learning to Remember User Conversations" presents an innovative step towards more personalized LLMs, challenging existing techniques, and opening avenues for advanced AI systems capable of meaningful and memory-driven user interactions.