Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-15T21:02:03.287322Z
Paper Citation Record · LEDGER
As of 16 August 2026, this Paper Citation Record lists 62 of 62 outbound references and 3 inbound Pith citation observations for arXiv:2505.17051.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-15T21:02:03.287322Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-06-26T00:27:41.609691Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z
62 of 62 outbound references displayed
External citation measurements
0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Observation d30b283d-b186-4fb3-98b2-dac9dbfcc71d · outbound
Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models Pens: A dataset and generic framework for personalized news headline generation
Reference 1
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Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models Transparent, scrutable and explainable user models for personalized recommendation
Reference 2
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Observation c0502cf9-4f71-4eb9-93ef-29ec90b6542d · outbound
Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models Persona: A reproducible testbed for pluralistic alignment
Reference 3
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Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models MaxMin-RLHF: Alignment with Diverse Human Preferences
Reference 4
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Observation 79939c40-b339-4f7c-a549-0936bb219024 · outbound
Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models Direct preference optimization with unobserved preference heterogeneity
Reference 5
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Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models Personalized audiobook recommendations at spotify through graph neural networks
Reference 6
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Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models User Embedding Model for Personalized Language Prompting
Reference 7
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Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models KTO: Model Alignment as Prospect Theoretic Optimization
Reference 8
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Observation 1349b55e-f109-4831-914d-7724764e4968 · outbound
Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models Stylept: Personalized neural text style transfer via prompt tuning.proceedings of the 37th aaai conference on artificial intelligence (aaai 2023), pages 9135–9143, 2023
Reference 9
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Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models Generalized User Representations for Transfer Learning
Reference 10
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Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models In-context Autoencoder for Context Compression in a Large Language Model
Reference 11
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Observation da52ba7f-3ea0-4e81-a321-82af256bbd3a · outbound
Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models The Llama 3 Herd of Models
Reference 12
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Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models Transformer with memory as personalized gpt for dialogue agents
Reference 13
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Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models Neural collaborative filtering
Reference 14
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Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models PERSOMA: PERsonalized SOft ProMpt Adapter Architecture for Personalized Language Prompting
Reference 15
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Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models Free energy analyses of cell-penetrating peptides using the weighted ensemble method
Reference 16
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Observation 1a11ee79-98c4-43c8-affc-e2ce6cbc1414 · outbound
Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models Lora: Low-rank adaptation of large language models
Reference 17
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Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models Lapdog: Learning retrieval augmentation for personalized dialogue generation
Reference 18
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Observation a57eb3a4-9875-43fc-8baa-aeb7657e2648 · outbound
Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models Aligning Language Models to User Opinions
Reference 19
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Observation 99647fb3-f4e3-4aa5-9b04-dc02c3addc5f · outbound
Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models Personalized Soups: Personalized Large Language Model Alignment via Post-hoc Parameter Merging
Reference 20
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Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models Personalized language models via privacy- preserving evolutionary model merging
Reference 21
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Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models Adam: A Method for Stochastic Optimization
Reference 22
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Observation f9e7eee9-d520-4cef-b15c-ac5bdad06977 · outbound
Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models ComPO: Community Preferences for Language Model Personalization
Reference 23
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Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models P5: Plug-and-play persona prompting for personal- ized response selection
Reference 24
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Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models The power of scale for parameter-efficient prompt tuning
Reference 25
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Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models
Reference 26
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Observation ad4aaaa5-6a1f-4249-93b2-689bed80cdcf · outbound
Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models Prefix-Tuning: Optimizing Continuous Prompts for Generation
Reference 27
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Observation 47d7bfc9-e4e0-437f-9c02-f7b2eb2937e0 · outbound
Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models Personalized item embeddings in federated multimodal recommendation
Reference 28
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Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models 500xCompressor: Generalized Prompt Compression for Large Language Models
Reference 29
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Observation b988c9ea-74ef-4884-88d3-88c79604a219 · outbound
Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models Rouge: A package for automatic evaluation of summaries
Reference 30
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Observation b16d1249-156e-4dd5-924a-a3076b1a3c1c · outbound
Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models Visual instruction tuning.Advances in neural information processing systems, 36:34892–34916, 2023
Reference 31
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Observation 5eb5a081-3769-4c29-969c-149cb8697929 · outbound
Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models A survey of personalized large language models
Reference 32
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Observation 3d54abe7-9366-43f8-a156-e078b7d1d785 · outbound
Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models Llms + persona-plug = personalized llms
Reference 33
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Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models Xrec: Large language models for explainable recommendation
Reference 34
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Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models Personalizing dialogue agents via meta-learning
Reference 35
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Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models Personalized paraphrasing: Controlling the formality and style of text
Reference 36
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Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models User-LLM: Efficient LLM Contextualization with User Embeddings
Reference 37
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Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models Recommender systems with generative retrieval
Reference 38
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Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models LaMP: When Large Language Models Meet Personalization
Reference 39
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Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models Whose opinions do language models reflect? In International Conference on Machine Learning, pages 29971–30004
Reference 40
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Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models LMFusion: Adapting Pretrained Language Models for Multimodal Generation
Reference 41
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Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models Better generalization with semantic ids: A case study in ranking for recommendations
Reference 42
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Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models A multi-stage approach for persona-aware response generation
Reference 43
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Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models Persona-DB: Efficient Large Language Model Personalization for Response Prediction with Collaborative Data Refinement
Reference 44
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Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models Democratizing Large Language Models via Personalized Parameter-Efficient Fine-tuning
Reference 45
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Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models Step-Back Profiling: Distilling User History for Personalized Scientific Writing
Reference 46
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Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models Demystifying Embedding Spaces using Large Language Models
Reference 47
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Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models Modeling users’ personality for response generation in conversational agent
Reference 48
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Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models Large Language Model as a Universal Clinical Multi-task Decoder
Reference 49
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Observation 600be5a3-76c4-468a-b722-5628f6600c28 · outbound
Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models Building your own chatbot with customized persona
Reference 50
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Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models Personalized LLM Response Generation with Parameterized Memory Injection
Reference 51
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Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models Llama-adapter: Efficient fine-tuning of large language models with zero-initialized attention
Reference 52
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Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models Unresolved cited work
Reference 53
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Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models Personalize your llm: Fake it then align it
Reference 54
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Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models Less is more: Learning to refine dialogue history for personalized dialogue generation
Reference 55
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Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models Useradapter: Few-shot user learning in sentiment analysis
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Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models DiffLM: Controllable Synthetic Data Generation via Diffusion Language Models
Reference 57
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Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models HYDRA: Model Factorization Framework for Black-Box LLM Personalization
Reference 58
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Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models We start from the “cleaned” HuggingFace edition (version dated 2024-03-01, 122,499 dialogues)
Reference 59
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Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models We use the official v7 TSV dump distributed on Kaggle and respect its train/valid/personalized_test partition
Reference 60
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Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models This internal dataset consists of 300,000 user queries and generated playlists
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Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models AlekseyKorshuk/persona-chat
Reference 62
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Cloud-native and Distributed Systems for Efficient and Scalable Large Language Models -- A Research Agenda Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models
Reference 149
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Observation b4df45f5-88bd-43cf-995d-37e2667e516c · inbound
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Reference 71
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Reference 49
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