Papers with personalized generation
A Survey on Personalized Alignment—The Missing Piece for Large Language Models in Real-World Applications (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) have demonstrated remarkable capabilities, yet their transition to real-world applications reveals a critical limitation: the inability to adapt to individual preferences while maintaining alignment with universal human values. |
| Approach: | They propose a framework that enables LLMs to adapt their behavior within ethical boundaries based on individual preferences. |
| Outcome: | The proposed framework analyzes implementation approaches and evaluates their effectiveness across various scenarios. |
Personalized Text Generation with Contrastive Activation Steering (2025.acl-long)
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| Challenge: | Existing approaches to personalized text generation rely on retrieval-augmented generation and parameter-efficient fine-tuning. |
| Approach: | They propose a training-free framework that disentangles and represents personalized writing style as a vector in LLM’s activation-space. |
| Outcome: | The proposed framework achieves 8% relative improvement in personalized generation while reducing storage requirements by 1700 over PEFT method. |
Perspective Taking through Generating Responses to Conflict Situations (2024.findings-acl)
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| Challenge: | Language models struggle to understand and explain the beliefs of others, despite improving performance on a wide variety of tasks. |
| Approach: | They propose to modify the social-chem-101 corpus to allow for perspective-taking, the process of conceptualizing the point of view of another person. |
| Outcome: | The proposed models outperform the recent models conditioned on self-disclosures with high similarity to the conflict situation. |
ClusterRAG: Cluster-Based Collaborative Filtering for Personalized Retrieval-Augmented Generation (2026.acl-long)
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| Challenge: | Existing approaches to Personalized Retrieval-Augmented Generation (RAG) ignore long-term user information and inter-user relationships when constructing retrieval contexts, limiting personalization and the ability to leverage analogous users' knowledge for improved generation quality. |
| Approach: | They propose a Cluster-Based Collaborative Filtering for Personalized Retrieval-Augmented Generation that organizes users into semantically coherent clusters and performs retrieval at both the cluster and document levels via cluster-level similarity and fine-grained ranking. |
| Outcome: | Extensive experiments on the LaMP benchmark show that ClusterRAG integrates seamlessly with different dense retrievers and rankers, and remains effective when paired with both fine-tuned and zero-shot language models. |
I-AM-G: Interest Augmented Multimodal Generator for Item Personalization (2024.emnlp-main)
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| Challenge: | e-commerce and recommender systems lack a framework for personalized generation . a new framework extracts tags from multimodal information of items that the user has interacted with . |
| Approach: | They propose a framework that extracts tags from multimodal information and rewrites item description . they then use a decoupled text-to-text and image-to image retriever to search for similar item text . |
| Outcome: | The proposed framework can generate results aligned with user preferences . it can be used in e-commerce and recommender systems to win over diverse user base . |