Join us at @emnlpmeeting.bsky.social for:

"Tailoring AI: Exploring Active and Passive LLM Personalization" 🎯🧠

To answer, when should LLMs personalize? What role do users play in LLM-personalization?

📅 Deadline Aug. 1
📝 Details in thread 🧵👇
#EMNLP2025 #LLM #AI #personalization

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4/🧵Find more details and our results at: arxiv.org/abs/2411.13405

3/🧵PEFT: We use these QA pairs to finetune a LoRA adapter conversation by conversation. We find that weighting the loss on the QA tokens focuses the model on relevant content, rather than structure. Iterating on each conversation for 10 epochs gives the best results.

2/🧵Data Augmentation: We up-sample conversations as positive and negative QA pairs. Positive pairs contain questions about the conversation content, while negative pairs contain questions about related topics not discussed. The negative pairs allow to draw a knowledge boundary.

1/🧵PLUM is a 2 stage pipeline performing data augmentation for up-sampling conversations as QA pairs, that we use to finetune a LoRA adapter with a weighted CE loss. We perform competitively with baselines such as RAG, achieving 81.5% accuracy across 100 conversations.

Looking for an alternative to RAG for personalization?

With PLUM, a pipeline for teaching LLMs to remember prior user conversations, we aim to enable your future personalization research! Joint work with @maartjeterhoeve.bsky.social, Katherine Metcalf and Yizhe Zhang from my internship at Apple.

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