Personalized Assistants

Source papers 43

The Arxiv papers behind this sub-topic. Links may take you off-site to arxiv.org.

• A Comprehensive Review of AI-based Intelligent Tutoring Systems: Applications and Challenges
  AI-based Intelligent Tutoring Systems (ITS) have significant potential to transform teaching and learning. As efforts continue to design, develop, and integrate ITS into educational contexts, mixed re…
• A Conversation is Worth A Thousand Recommendations: A Survey of Holistic Conversational Recommender Systems
  Conversational recommender systems (CRS) generate recommendations through an interactive process. However, not all CRS approaches use human conversations as their source of interaction data; the major…
• ALIGN: Prompt-based Attribute Alignment for Reliable, Responsible, and Personalized LLM-based Decision-Making
  Large language models (LLMs) are increasingly being used as decision aids. However, users have diverse values and preferences that can affect their decision-making, which requires novel methods for LL…
• ARGS: Alignment as Reward-Guided Search
  we introduce ARGS, Alignment as Reward-Guided Search, a novel framework that integrates alignment into the decoding process, eliminating the need for expensive RL training. By adjusting the model’s pr…
• Active Listening: Personalized Question Generation in Open-Domain Social Conversation with User Model Based Prompting
  Large language models (LLMs) capable of casual conversation have recently become widely available. We hypothesize that users of conversational systems want a more personalized experience, and existing…
• Adaptive Learning Systems: Personalized Curriculum Design Using LLM-Powered Analytics
  Abstract—Large language models (LLMs) are revolutionizing the field of education by enabling personalized learning experiences tailored to individual student needs. In this paper, we introduce a frame…
• Assessment of Personality Dimensions Across Situations Using Conversational Speech
  Abstract—Prior research indicates that users prefer assistive technologies whose personalities align with their own. This has sparked interest in automatic personality perception (APP), which aims to …
• Beyond Answers: How LLMs Can Pursue Strategic Thinking in Education
  Abstract—Artificial Intelligence (AI) holds transformative potential in education, enabling personalized learning, enhancing inclusivity, and encouraging creativity and curiosity. In this paper, we ex…
• Can LLM be a Personalized Judge?
  Ensuring that large language models (LLMs) reflect diverse user values and preferences is crucial as their user bases expand globally. It is therefore encouraging to see the growing interest in LLM pe…
• Chatbots in Knowledge-Intensive Contexts: Comparing Intent and LLM-Based Systems
  Cognitive assistants (CA) are chatbots that provide context-aware support to human workers in knowledge-intensive tasks. Traditionally, cognitive assistants respond in specific ways to predefined user…
• Decision-Oriented Dialogue for Human–AI Collaboration
  “All these situations share an underlying structured decision problem in the face of uncertainty, where communicating and collaborating with others is often critical to arrive at the best solution. D…
• Do Phone-Use Agents Respect Your Privacy?
  We study whether phone-use agents respect privacy while completing benign mobile tasks. This question has remained hard to answer because privacy-compliant behavior is not operationalized for phone-us…
• Enhancing personalized multi-turn dialogue with curiosity reward
  Current training methods like Reinforcement Learning from Human Feedback (RLHF) prioritize helpfulness and safety but fall short in fostering truly empathetic, adaptive, and personalized interactions.…
• From Persona to Person: Enhancing the Naturalness with Multiple Discourse Relations Graph Learning in Personalized Dialogue Generation
  Abstract. In dialogue generation, the naturalness of responses is crucial for effective human-machine interaction. Personalized response generation poses even greater challenges, as the responses must…
• From Text to Emoji: How PEFT-Driven Personality Manipulation Unleashes the Emoji Potential in LLMs
  we employed Opinion QA Based Parameter-Efficient Fine- Tuning (PEFT), specifically Quantized Low- Rank Adaptation (QLoRA), to manipulate the Big Five personality traits: Openness, Conscientiousness, E…
• H2HTalk: Evaluating Large Language Models as Emotional Companion
  We present Heart-to-Heart Talk (H2HTalk), a benchmark assessing companions across personality development and empathetic interaction, balancing emotional intelligence with linguistic fluency. H2HTalk …
• Hello Again! LLM-powered Personalized Agent for Long-term Dialogue
  Open-domain dialogue systems have seen remarkable advancements with the development of large language models (LLMs). Nonetheless, most existing dialogue systems predominantly focus on brief single-ses…
• Intent Mismatch Causes LLMs to Get Lost in Multi-Turn Conversation
  Multi-turn conversation has emerged as a predominant interaction paradigm for Large Language Models (LLMs). Users often employ follow-up questions to refine their intent, expecting LLMs to adapt dynam…
• Language Model Personalization via Reward Factorization
  Modern large language models (LLMs) are optimized for human-aligned responses using Reinforcement Learning from Human Feedback (RLHF). However, existing RLHF approaches assume a universal preference m…
• Learning Retrieval Augmentation for Personalized Dialogue Generation
  Personalized dialogue generation, focusing on generating highly tailored responses by leveraging persona profiles and dialogue context, has gained significant attention in conversational AI applicatio…
• Learning To Guide Human Experts Via Personalized Large Language Models
  “Consider the problem of diagnosing lung pathologies based on the outcome of an X-ray scan. This task cannot be fully automated, for safety reasons, necessitating human supervision at some step of the…
• Making Sense of Memory in AI Agents
  However, there’s also another approach to categorizing memory types for AI agents from a design pattern perspective. Sarah Wooders from Letta argues that an LLM is a tokens-in-tokens-out function, not…
• Memorization and Knowledge Injection in Gated LLMs
  Large Language Models (LLMs) currently struggle to sequentially add new memories and integrate new knowledge. These limitations contrast with the human ability to continuously learn from new experienc…
• Neural Assistant: Joint Action Prediction, Response Generation, and Latent Knowledge Reasoning
  Task-oriented dialog presents a difficult challenge encompassing multiple problems including multi-turn language understanding and generation, knowledge retrieval and reasoning, and action prediction.…
• Octopus v2: On-device language model for super agent
  Current on-device models for function calling face issues with latency and accuracy. Our research presents a new method that empowers an on-device model with 2 billion parameters to surpass the perfor…
• PRIME: Large Language Model Personalization with Cognitive Memory and Thought Processes
  Large language model (LLM) personalization aims to align model outputs with individuals’ unique preferences and opinions. While recent efforts have implemented various personalization methods, a unifi…
• PersLLM: A Personified Training Approach for Large Language Models
  Large language models (LLMs) exhibit humanlike intelligence, enabling them to simulate human behavior and support various applications that require both humanized communication and extensive knowledge…
• PersonaAgent: When Large Language Model Agents Meet Personalization at Test Time
  This limitation motivates us to develop PersonaAgent, the first personalized LLM agent framework designed to address versatile personalization tasks. Specifically, PersonaAgent integrates two compleme…
• Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedback
  Large language models (LLMs) are used to generate content for an increasingly wide range of tasks, and are set to reach a growing audience in coming years due to integration in product interfaces like…
• Personalization of Large Language Models: A Survey
  Personalization of Large Language Models (LLMs) has recently become increasingly important with a wide range of applications. Despite the importance and recent progress, most existing works on persona…
• Personalized Dialogue Generation with Persona-Adaptive Attention
  Persona-based dialogue systems aim to generate consistent responses based on historical context and predefined persona. Unlike conventional dialogue generation, the persona-based dialogue needs to con…
• Personalized Language Modeling from Personalized Human Feedback
  we propose Personalized-RLHF (P-RLHF), an efficient framework that utilizes a lightweight user model to capture individual user preferences and jointly learns the user model and the personalized LLM f…
• Personalizing Reinforcement Learning from Human Feedback with Variational Preference Learning
  However, current RLHF techniques cannot account for the naturally occurring differences in individual human preferences across a diverse population. When these differences arise, traditional RLHF fram…
• Predictive Preference Learning from Human Interventions
  Learning from human involvement aims to incorporate the human subject to monitor and correct agent behavior errors. Although most interactive imitation learning methods focus on correcting the agent’s…
• Real-Time Procedural Learning From Experience for AI Agents
  AI agents are artificial intelligence systems capable of observing and taking actions in an environment. As adoption spreads across industries, there is a growing need for AI agents to quickly learn d…
• Recent Trends in Personalized Dialogue Generation: A Review of Datasets, Methodologies, and Evaluations
  Enhancing user engagement through personalization in conversational agents has gained significance, especially with the advent of large language models that generate fluent responses. Personalized dia…
• Revisiting Prompt Engineering: A Comprehensive Evaluation for LLM-based Personalized Recommendation
  Large language models (LLMs) can perform recommendation tasks by taking prompts written in natural language as input. Compared to traditional methods such as collaborative filtering, LLM-based recomme…
• See you soon again, chatbot? A design taxonomy to characterize user-chatbot relationships with different time horizons
  Users interact with chatbots for various purposes and motivations – and for different periods of time. However, since chatbots are considered social actors and given that time is an essential componen…
• Supporting Physical Activity Behavior Change with LLM-Based Conversational Agents
   Through formative interviews with 12 health professionals and 10 non-experts, we identify design considerations and…
• The Assistant Axis: Situating and Stabilizing the Default Persona of Language Models
  Large language models can represent a variety of personas but typically default to a helpful Assistant identity cultivated during post-training. We investigate the structure of the space of model pers…
• The Challenges in Designing a Prevention Chatbot for Eating Disorders: Observational Study
  Background: Chatbots have the potential to provide cost-effective mental health prevention programs at scale and increase interactivity, ease of use, and accessibility of intervention programs. Objec…
• Unintended Impacts of LLM Alignment on Global Representation
  Before being deployed for user-facing applications, developers align Large Language Models (LLMs) to user preferences through a variety of procedures, such as Reinforcement Learning From Human Feedbac…
• Virtual Assistance in Any Context
  Abstract Several domain-specific assistants in the form of chatbots have conquered many commercial and private areas. However, there is still a limited level of systematic knowledge of the distinctive…