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Found 28 papers, 6 papers with code
MRF-Chat: Improving Dialogue with Markov Random Fields
Recent state-of-the-art approaches in open-domain dialogue include training end-to-end deep-learning models to learn various conversational features like emotional content of response, symbolic transitions of dialogue contexts in a knowledge graph and persona of the agent and the user, among others.
A Demonstration of Adaptive Collaboration of Large Language Models for Medical Decision-Making
Medical Decision-Making (MDM) is a multi-faceted process that requires clinicians to assess complex multi-modal patient data patient, often collaboratively.
Can Language Models Take A Hint? Prompting for Controllable Contextualized Commonsense Inference
Generating commonsense assertions within a given story context remains a difficult task for modern language models.
HEART-felt Narratives: Tracing Empathy and Narrative Style in Personal Stories with LLMs
We introduce a novel, theory-based taxonomy, HEART (Human Empathy and Narrative Taxonomy) that delineates elements of narrative style that can lead to empathy with the narrator of a story.
EmpathicStories++: A Multimodal Dataset for Empathy towards Personal Experiences
Modeling empathy is a complex endeavor that is rooted in interpersonal and experiential dimensions of human interaction, and remains an open problem within AI.
MDAgents: An Adaptive Collaboration of LLMs for Medical Decision-Making
MDAgents achieved the best performance in seven out of ten benchmarks on tasks requiring an understanding of medical knowledge and multi-modal reasoning, showing a significant improvement of up to 4. 2% (p < 0. 05) compared to previous methods' best performances.
Improving Dialogue Agents by Decomposing One Global Explicit Annotation with Local Implicit Multimodal Feedback
We describe an approach for aligning an LLM-based dialogue agent based on global (i. e., dialogue-level) rewards, while also taking into account naturally-occurring multimodal signals.
Look Around! Unexpected gains from training on environments in the vicinity of the target
Here we present a new methodology to evaluate such generalization of RL agents under small shifts in the transition probabilities.
Health-LLM: Large Language Models for Health Prediction via Wearable Sensor Data
Notably, we observe that our context enhancement can yield up to 23. 8% improvement in performance.
Understanding Teacher Perspectives and Experiences after Deployment of AI Literacy Curriculum in Middle-school Classrooms
Artificial Intelligence (AI) and its associated applications are ubiquitous in today's world, making it imperative that students and their teachers understand how it works and the ramifications arising from its usage.
Studying Artist Sentiments around AI-generated Artwork
Concerns about plagiarizing their artworks and styles for datasets and uncertainty around the future of digital art sparked movements in artist communities shunning the use of AI for generating art and protecting artists' rights.
AI Audit: A Card Game to Reflect on Everyday AI Systems
The game mechanics reward systems that are ethically developed or that take steps to mitigate potential harms.
Modeling Empathic Similarity in Personal Narratives
The most meaningful connections between people are often fostered through expression of shared vulnerability and emotional experiences in personal narratives.
HIINT: Historical, Intra- and Inter- personal Dynamics Modeling with Cross-person Memory Transformer
Accurately modeling affect dynamics, which refers to the changes and fluctuations in emotions and affective displays during human conversations, is crucial for understanding human interactions.
Constructing Dreams using Generative AI
Generative AI tools introduce new and accessible forms of media creation for youth.
PersonaLLM: Investigating the Ability of Large Language Models to Express Personality Traits
Despite the many use cases for large language models (LLMs) in creating personalized chatbots, there has been limited research on evaluating the extent to which the behaviors of personalized LLMs accurately and consistently reflect specific personality traits.
Multipar-T: Multiparty-Transformer for Capturing Contingent Behaviors in Group Conversations
As we move closer to real-world AI systems, AI agents must be able to deal with multiparty (group) conversations.
Adversarial Transformer Language Models for Contextual Commonsense Inference
Contextualized or discourse aware commonsense inference is the task of generating coherent commonsense assertions (i. e., facts) from a given story, and a particular sentence from that story.
Joint Engagement Classification using Video Augmentation Techniques for Multi-person Human-robot Interaction
This work serves as the first step toward fully unlocking the potential of end-to-end video understanding models pre-trained on large public datasets and augmented with data augmentation and visualization techniques for affect recognition in the multi-person human-robot interaction in the wild.
Build-a-Bot: Teaching Conversational AI Using a Transformer-Based Intent Recognition and Question Answering Architecture
As such, we propose the development of an open-source tool (Build-a-Bot) for students and teachers to not only create their own transformer-based chatbots based on their own course material, but also learn the fundamentals of AI through the model creation process.
B$^3$RTDP: A Belief Branch and Bound Real-Time Dynamic Programming Approach to Solving POMDPs
Partially Observable Markov Decision Processes (POMDPs) offer a promising world representation for autonomous agents, as they can model both transitional and perceptual uncertainties.
Explainable AI for Suicide Risk Assessment Using Eye Activities and Head Gestures
The prevalence of suicide has been on the rise since the 20th century, causing severe emotional damage to individuals, families, and communities alike.
Introducing Variational Autoencoders to High School Students
Generative Artificial Intelligence (AI) models are a compelling way to introduce K-12 students to AI education using an artistic medium, and hence have drawn attention from K-12 AI educators.
RetroGAN: A Cyclic Post-Specialization System for Improving Out-of-Knowledge and Rare Word Representations
Retrofitting is a technique used to move word vectors closer together or further apart in their space to reflect their relationships in a Knowledge Base (KB).
Combining pre-trained language models and structured knowledge
In recent years, transformer-based language models have achieved state of the art performance in various NLP benchmarks.
Migratable AI: Personalizing Dialog Conversations with migration context
The migration of conversational AI agents across different embodiments in order to maintain the continuity of the task has been recently explored to further improve user experience.
Migratable AI : Investigating users' affect on identity and information migration of a conversational AI agent
Conversational AI agents are becoming ubiquitous and provide assistance to us in our everyday activities.
Dyadic Speech-based Affect Recognition using DAMI-P2C Parent-child Multimodal Interaction Dataset
Automatic speech-based affect recognition of individuals in dyadic conversation is a challenging task, in part because of its heavy reliance on manual pre-processing.
