Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy | Paper Citation Record

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This paper cites Ai-based digital assistants: Opportunities, threats, and research perspectives.

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Ai-based digital assistants: Opportunities, threats, and research perspectives

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This paper cites A Comprehensive Survey of Direct Preference Optimization: Datasets, Theories, Variants, and Applications.

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy A Comprehensive Survey of Direct Preference Optimization: Datasets, Theories, Variants, and Applications

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This paper cites Survey on virtual assistant: Google assistant, siri, cortana, alexa.

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Survey on virtual assistant: Google assistant, siri, cortana, alexa

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Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy On the security and privacy challenges of virtual assistants

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This paper cites Design and evaluation of AI copilots -- case studies of retail copilot templates.

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Design and evaluation of AI copilots -- case studies of retail copilot templates

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Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Computing, cognition and the future of knowing

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Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Foundations of augmented cognition

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Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Joint cognitive systems: Foundations of cognitive systems engineering

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This paper cites Experi- mental evidence of effective human–ai collaboration in medical decision-making.

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Experi- mental evidence of effective human–ai collaboration in medical decision-making

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This paper cites The future of human-AI collaboration: a taxonomy of design knowledge for hybrid intelligence systems.

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy The future of human-AI collaboration: a taxonomy of design knowledge for hybrid intelligence systems

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Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Human–ai collaboration enables more empathic conversations in text-based peer-to-peer mental health support

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Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Anatomy of a digital assistant

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Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Classifying smart personal assistants: An empirical cluster analysis

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Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy what can i help you with?

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Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy A literature survey of recent advances in chatbots

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This paper cites A survey on privacy issues and solutions for voice-controlled digital assistants.

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy A survey on privacy issues and solutions for voice-controlled digital assistants

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Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Manifestation of virtual assistants and robots into daily life: Vision and challenges

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This paper cites V oices in and of the machine: Source orientation toward mobile virtual assistants.Computers in Human Behavior, 90:343–350, 2019.

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy V oices in and of the machine: Source orientation toward mobile virtual assistants.Computers in Human Behavior, 90:343–350, 2019

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This paper cites Survey on intelligent chatbots: State-of-the-art and future research directions.

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Survey on intelligent chatbots: State-of-the-art and future research directions

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Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Review of state-of-the-art design techniques for chatbots

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This paper cites A survey on conversational agents/chatbots classification and design techniques.

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy A survey on conversational agents/chatbots classification and design techniques

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Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Chatbots: History, technology, and applications

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Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Improving the domain adaptation of retrieval augmented generation (rag) models for open domain question answering

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Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Medical expert systems survey

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Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Expert system methodologies and applications—a decade review from 1995 to 2004

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Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy A survey on expert system in agriculture

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Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Expert systems: Principles and programming (fouth edition)

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Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy A survey of belief rule-base expert system

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This paper cites A multimodal generative ai copilot for human pathology.

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy A multimodal generative ai copilot for human pathology

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Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy When to show a suggestion? integrating human feedback in ai-assisted programming

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Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Human+ machine: Reimagining work in the age of AI

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Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy The rise of the ai co-pilot: Lessons for design from aviation and beyond

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Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Angelopoulos, Tianle Li, Dacheng Li, Banghua Zhu, Hao Zhang, Michael I

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Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Exploring the potential of generative ai for augmenting choice-based preference elicitation in recommender systems

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This paper cites Explicit or implicit feedback? engagement or satisfaction? In Proceedings of the 12th ACM Conference on Recommender Systems (RecSys ’18), pages 24–32, 2018.

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Explicit or implicit feedback? engagement or satisfaction? In Proceedings of the 12th ACM Conference on Recommender Systems (RecSys ’18), pages 24–32, 2018

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Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Automatic personalization based on web usage mining

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Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Exploring gaze-based prediction strategies for preference detection in videos

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This paper cites Tucker, Kiante Brantley, Adam Cahall, and Thorsten Joachims.

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Tucker, Kiante Brantley, Adam Cahall, and Thorsten Joachims

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Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Rlhf from heterogeneous feedback via personalization and preference aggregation

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Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy What are you known for? learning user topical profiles with implicit and explicit footprints

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Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Self-exploring language models: Active preference elicitation for online alignment

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Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Bayesian optimization with llm-based acquisition functions for natural language preference elicitation

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Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Unresolved cited work

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Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Active preference inference using language models and probabilistic reasoning

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Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Evaluating large language models as generative user simulators for conversational recommendation

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Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Guided profile generation improves personalization with llms

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This paper cites Aligning language models with preferences through f-divergence minimization.

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Aligning language models with preferences through f-divergence minimization

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Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Aligning llms with individual preferences via interaction

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This paper cites Heimdall: A privacy-respecting implicit preference collection framework.

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Heimdall: A privacy-respecting implicit preference collection framework

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This paper cites Coached conversational preference elicitation: A case study in understanding movie preferences.

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Coached conversational preference elicitation: A case study in understanding movie preferences

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This paper cites Do llms recognize your preferences? evaluating personalized preference following in llms.

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Do llms recognize your preferences? evaluating personalized preference following in llms

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This paper cites A survey of user profiling: State-of-the-art, challenges, and solutions.

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy A survey of user profiling: State-of-the-art, challenges, and solutions

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This paper cites User modeling and user profiling: A comprehensive survey.

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy User modeling and user profiling: A comprehensive survey

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This paper cites Preference learning with gaussian processes.

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Preference learning with gaussian processes

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This paper cites User persona identification and new service adaptation recommendation.

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy User persona identification and new service adaptation recommendation

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 08f3735b-0d59-48a4-8539-ee45e1a855fe · outbound

This paper cites Collaborative filtering to capture ai user’s preferences as norms.

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Collaborative filtering to capture ai user’s preferences as norms

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:38.842810Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3f11bc10-a138-441d-9fb2-bbf62e48b735 · outbound

This paper cites A survey on accuracy-oriented neural recommendation: From collaborative filtering to information-rich recommendation.

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy A survey on accuracy-oriented neural recommendation: From collaborative filtering to information-rich recommendation

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:38.670098Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 80050039-83f4-4fb7-ad0f-59b80a38dd12 · outbound

This paper cites Neural collaborative filtering for user preference discovery from biased implicit feedback.

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Neural collaborative filtering for user preference discovery from biased implicit feedback

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:38.439200Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 57633d0c-6673-4684-a6b7-deffd57d66f1 · outbound

This paper cites Paed- zero-shot persona attribute extraction in dialogues.

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Paed- zero-shot persona attribute extraction in dialogues

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:38.238976Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3d934f44-67af-41c3-b656-c66cb11d4cdb · outbound

This paper cites Enhancing emotional support conversations a framework for dynamic.

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Enhancing emotional support conversations a framework for dynamic

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:38.078235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 6b5bad74-3dca-4f20-9150-c87bb463404b · outbound

This paper cites Towards personalized human-ai interaction: Adapting the behavior of ai agents using neural signatures of subjective interest.

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Towards personalized human-ai interaction: Adapting the behavior of ai agents using neural signatures of subjective interest

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:37.819670Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d4cc112e-96ce-474d-b92b-c3b0ce0dabc9 · outbound

This paper cites Active preference learning for large language models.

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Active preference learning for large language models

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:37.650865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 6f2b79b3-38a9-47f5-aad3-6d9adb6414f8 · outbound

This paper cites When to show a suggestion? integrating human feedback in ai-assisted programming.

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy When to show a suggestion? integrating human feedback in ai-assisted programming

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:37.475371Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 8441b629-23a7-4aff-9126-39a51c4d9082 · outbound

This paper cites Afspp: An agent framework for shaping preference and personality with llms.

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Afspp: An agent framework for shaping preference and personality with llms

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:37.288977Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 12d7bf12-b491-4066-b97d-9b2ebab7b4e9 · outbound

This paper cites Preferences in ai.

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Preferences in ai

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:37.104003Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7bee41ed-0803-4160-b813-93ffd90a5b3b · outbound

This paper cites Learning Retrieval Augmentation for Personalized Dialogue Generation.

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Learning Retrieval Augmentation for Personalized Dialogue Generation

Reference 67

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:24:33.296148Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2a35fff0-3b36-400d-baab-da659b931d4b · outbound

This paper cites Persobench: Benchmarking personalized response generation in large language models.

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Persobench: Benchmarking personalized response generation in large language models

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-07T13:24:28.260645Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 88d99919-ac11-4bef-b803-fb14b9a99ff9 · outbound

This paper cites Recent Trends in Personalized Dialogue Generation: A Review of Datasets, Methodologies, and Evaluations.

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Recent Trends in Personalized Dialogue Generation: A Review of Datasets, Methodologies, and Evaluations

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-07T13:24:28.339748Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 641e948b-5502-478f-95ed-13d7f8a69207 · outbound

This paper cites Cross-graph knowledge exchange for personalized response generation in dialogue systems.

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Cross-graph knowledge exchange for personalized response generation in dialogue systems

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:36.923544Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 01582498-2d3c-41a3-87e3-8da252f3dd1b · outbound

This paper cites Context aggregation with topic-focused summarization for personalized medical dialogue generation.

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Context aggregation with topic-focused summarization for personalized medical dialogue generation

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:36.697544Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e82da79d-4fba-4933-ae1d-d7f0c954335d · outbound

This paper cites Will I Sound Like Me? Improving Persona Consistency in Dialogues through Pragmatic Self-Consciousness.

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Will I Sound Like Me? Improving Persona Consistency in Dialogues through Pragmatic Self-Consciousness

Reference 72

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:24:32.931496Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2d42b270-0cc3-4ee2-8e13-4ef95e937bcc · outbound

This paper cites Pk-icr: Persona-knowledge interactive multi-context retrieval for grounded dialogue.

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Pk-icr: Persona-knowledge interactive multi-context retrieval for grounded dialogue

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:36.519758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 08e0c0bd-98c0-40a9-b159-83910448e981 · outbound

This paper cites Selective Prompting Tuning for Personalized Conversations with LLMs.

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Selective Prompting Tuning for Personalized Conversations with LLMs

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-07T13:24:28.648037Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6bdb900e-be23-4cff-a405-77e66d5bf9e0 · outbound

This paper cites Talk to your brain: Artificial personalized intelligence for emotionally adaptive ai interactions.

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Talk to your brain: Artificial personalized intelligence for emotionally adaptive ai interactions

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:36.317052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation edaf098e-b23e-470c-8faa-2b17e608d39f · outbound

This paper cites A cue adaptive decoder for controllable neural response generation.

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy A cue adaptive decoder for controllable neural response generation

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:36.138404Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 21a0ae83-797d-4d2e-9a9e-e3aebb7dd05b · outbound

This paper cites TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents.

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-07T13:24:28.853597Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b69a1478-daab-47de-a5a9-14af1886e408 · outbound

This paper cites A Model-Agnostic Data Manipulation Method for Persona-based Dialogue Generation.

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy A Model-Agnostic Data Manipulation Method for Persona-based Dialogue Generation

Reference 78

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:24:32.657218Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 9a3e0829-3c3e-42fd-80dc-5e3229005efb · outbound

This paper cites PersonaPKT: Building Personalized Dialogue Agents via Parameter-efficient Knowledge Transfer.

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy PersonaPKT: Building Personalized Dialogue Agents via Parameter-efficient Knowledge Transfer

Reference 79

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:24:32.438949Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c8ffa51a-c336-4206-99e3-6e47b21566f1 · outbound

This paper cites Beyond candidates: adaptive dialogue agent utilizing persona and knowledge.

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Beyond candidates: adaptive dialogue agent utilizing persona and knowledge

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:35.983903Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a0252f89-dd57-46da-b8cd-de8f61143cce · outbound

This paper cites PLATO: Pre-trained Dialogue Generation Model with Discrete Latent Variable.

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy PLATO: Pre-trained Dialogue Generation Model with Discrete Latent Variable

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-07T13:24:29.267513Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a93738a4-c3ba-4163-b8e6-de55cd591c64 · outbound

This paper cites Personalized response generation via generative split memory network.

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Personalized response generation via generative split memory network

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:35.767300Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 8fab62ec-7545-427f-9d00-c2b165716d86 · outbound

This paper cites Towards Persona-Based Empathetic Conversational Models.

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Towards Persona-Based Empathetic Conversational Models

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-07T13:24:29.497969Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cd781854-3cdb-40fd-a9bc-577a77f26058 · outbound

This paper cites Learning to improve persona consistency in multi-party dialogue generation via text knowledge enhancement.

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Learning to improve persona consistency in multi-party dialogue generation via text knowledge enhancement

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:35.522931Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 33964d5a-ff70-4e1f-b5c2-c8d38dde34a1 · outbound

This paper cites Personalized dialogue generation with persona-adaptive attention.

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Personalized dialogue generation with persona-adaptive attention

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:35.274147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation bd2b6b6e-f58d-4bde-86b2-e03a18bfb890 · outbound

This paper cites Persona-aware multi-party conversation response generation.

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Persona-aware multi-party conversation response generation

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:35.092125Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 6dc1ce23-e30d-4b35-8c77-833a5ecce85a · outbound

This paper cites Personalized Pieces: Efficient Personalized Large Language Models through Collaborative Efforts.

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Personalized Pieces: Efficient Personalized Large Language Models through Collaborative Efforts

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-07T13:24:29.738328Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9d6c4ac6-b464-461b-9cf3-8bfb55266fa9 · outbound

This paper cites Training language models to follow instructions with human feedback.

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Training language models to follow instructions with human feedback

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-07T13:24:29.743517Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 365cac23-fd65-4f7b-92ac-7e913f471b9c · outbound

This paper cites Direct preference optimization: Your language model is secretly a reward model.

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Direct preference optimization: Your language model is secretly a reward model

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-07T13:24:29.747785Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2b48c89d-06c4-4169-a26a-2204eab6db34 · outbound

This paper cites Fine-Tuning Language Models from Human Preferences.

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Fine-Tuning Language Models from Human Preferences

Reference 90

Resolution: unresolved; no resolver link, observed 2026-08-07T13:24:29.752590Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 660c9214-e1e5-4f5c-a64d-cf2c19dd1383 · outbound

This paper cites Learning to summarize with human feedback.

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Learning to summarize with human feedback

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-07T13:24:29.761435Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1cc8a3b3-bfc9-4bce-b9ad-56cadf354ff5 · outbound

This paper cites Fine-grained human feedback gives better rewards for language model training.

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Fine-grained human feedback gives better rewards for language model training

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:34.867143Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f0950b69-1412-4337-8094-aad210ce9b44 · outbound

This paper cites Fine-Tuning Language Models with Reward Learning on Policy.

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Fine-Tuning Language Models with Reward Learning on Policy

Reference 93

Resolution: unresolved; no resolver link, observed 2026-08-07T13:24:29.885002Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7edda160-2dfc-4367-a029-edd0badfddbd · outbound

This paper cites Pretraining language models with human preferences.

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Pretraining language models with human preferences

Reference 94

Resolution: unresolved; no resolver link, observed 2026-08-07T13:24:29.933042Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a2769944-a99f-44ea-93e0-1557a5aae3ed · outbound

This paper cites trlx: A framework for large scale reinforcement learning from human feedback.

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy trlx: A framework for large scale reinforcement learning from human feedback

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:34.740613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0f396de3-948b-4652-8ee5-55ff1588566f · outbound

This paper cites Deep reinforcement learning from human preferences.

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Deep reinforcement learning from human preferences

Reference 96

Resolution: unresolved; no resolver link, observed 2026-08-07T13:24:30.066231Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bb59dc01-c5f7-4551-b9de-a78f7d70b003 · outbound

This paper cites Improving Reinforcement Learning from Human Feedback with Efficient Reward Model Ensemble.

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Improving Reinforcement Learning from Human Feedback with Efficient Reward Model Ensemble

Reference 97

Resolution: unresolved; no resolver link, observed 2026-08-07T13:24:30.152840Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 04b3437d-e8a2-4c38-90df-e92982a2163d · outbound

This paper cites Dense Reward for Free in Reinforcement Learning from Human Feedback.

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Dense Reward for Free in Reinforcement Learning from Human Feedback

Reference 98

Resolution: unresolved; no resolver link, observed 2026-08-07T13:24:30.201304Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 377015c3-fe7f-40db-849b-341f6af6eb34 · outbound

This paper cites Adaptive preference scaling for reinforcement learning with human feedback.

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Adaptive preference scaling for reinforcement learning with human feedback

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:34.606304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b6b24e2f-2f2f-40e5-8d64-81a0dae43c40 · outbound

This paper cites Confronting Reward Model Overoptimization with Constrained RLHF.

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Confronting Reward Model Overoptimization with Constrained RLHF

Reference 100

Resolution: unresolved; no resolver link, observed 2026-08-07T13:24:30.335635Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ac711189-9f6d-43d7-9184-727b8818fe3f · outbound

This paper cites ArCHer: Training Language Model Agents via Hierarchical Multi-Turn RL.

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy ArCHer: Training Language Model Agents via Hierarchical Multi-Turn RL

Reference 101

Resolution: unresolved; no resolver link, observed 2026-08-07T13:24:30.408981Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers