A Grounded Observer Framework for Establishing Guardrails for Foundation Models in Socially Sensitive Domains

• URL: http://arxiv.org/abs/2412.18639v1
• Date: Mon, 23 Dec 2024 22:57:05 GMT
• Title: A Grounded Observer Framework for Establishing Guardrails for Foundation Models in Socially Sensitive Domains
• Authors: Rebecca Ramnauth, Dražen Brščić, Brian Scassellati,
• Abstract summary: Given the complexities of foundation models, traditional techniques for constraining agent behavior cannot be directly applied. We propose a grounded observer framework for constraining foundation model behavior that offers both behavioral guarantees and real-time variability.
• Score: 1.9116784879310025
• License:
• Abstract: As foundation models increasingly permeate sensitive domains such as healthcare, finance, and mental health, ensuring their behavior meets desired outcomes and social expectations becomes critical. Given the complexities of these high-dimensional models, traditional techniques for constraining agent behavior, which typically rely on low-dimensional, discrete state and action spaces, cannot be directly applied. Drawing inspiration from robotic action selection techniques, we propose the grounded observer framework for constraining foundation model behavior that offers both behavioral guarantees and real-time variability. This method leverages real-time assessment of low-level behavioral characteristics to dynamically adjust model actions and provide contextual feedback. To demonstrate this, we develop a system capable of sustaining contextually appropriate, casual conversations ("small talk"), which we then apply to a robot for novel, unscripted interactions with humans. Finally, we discuss potential applications of the framework for other social contexts and areas for further research.

Related papers

• PersLLM: A Personified Training Approach for Large Language Models [66.16513246245401]
  We propose PersLLM, integrating psychology-grounded principles of personality: social practice, consistency, and dynamic development. We incorporate personality traits directly into the model parameters, enhancing the model's resistance to induction, promoting consistency, and supporting the dynamic evolution of personality.
  arXiv  Detail & Related papers  (2024-07-17T08:13:22Z)
• Multi-Agent Dynamic Relational Reasoning for Social Robot Navigation [50.01551945190676]
  Social robot navigation can be helpful in various contexts of daily life but requires safe human-robot interactions and efficient trajectory planning. We propose a systematic relational reasoning approach with explicit inference of the underlying dynamically evolving relational structures. We demonstrate its effectiveness for multi-agent trajectory prediction and social robot navigation.
  arXiv  Detail & Related papers  (2024-01-22T18:58:22Z)
• RealBehavior: A Framework for Faithfully Characterizing Foundation Models' Human-like Behavior Mechanisms [45.97077960079147]
  We introduce a framework, RealBehavior, which is designed to characterize the humanoid behaviors of models faithfully. Our findings suggest that a simple application of psychological tools cannot faithfully characterize all human-like behaviors.
  arXiv  Detail & Related papers  (2023-10-17T12:58:17Z)
• Training Socially Aligned Language Models on Simulated Social Interactions [99.39979111807388]
  Social alignment in AI systems aims to ensure that these models behave according to established societal values. Current language models (LMs) are trained to rigidly replicate their training corpus in isolation. This work presents a novel training paradigm that permits LMs to learn from simulated social interactions.
  arXiv  Detail & Related papers  (2023-05-26T14:17:36Z)
• Data-driven emotional body language generation for social robotics [58.88028813371423]
  In social robotics, endowing humanoid robots with the ability to generate bodily expressions of affect can improve human-robot interaction and collaboration. We implement a deep learning data-driven framework that learns from a few hand-designed robotic bodily expressions. The evaluation study found that the anthropomorphism and animacy of the generated expressions are not perceived differently from the hand-designed ones.
  arXiv  Detail & Related papers  (2022-05-02T09:21:39Z)
• Inference of Affordances and Active Motor Control in Simulated Agents [0.5161531917413706]
  We introduce an output-probabilistic, temporally predictive, modular artificial neural network architecture. We show that our architecture develops latent states that can be interpreted as affordance maps. In combination with active inference, we show that flexible, goal-directed behavior can be invoked.
  arXiv  Detail & Related papers  (2022-02-23T14:13:04Z)
• Active Inference in Robotics and Artificial Agents: Survey and Challenges [51.29077770446286]
  We review the state-of-the-art theory and implementations of active inference for state-estimation, control, planning and learning. We showcase relevant experiments that illustrate its potential in terms of adaptation, generalization and robustness.
  arXiv  Detail & Related papers  (2021-12-03T12:10:26Z)
• Social Processes: Self-Supervised Forecasting of Nonverbal Cues in Social Conversations [22.302509912465077]
  We take the first step in the direction of a bottom-up self-supervised approach in the domain of social human interactions. We formulate the task of Social Cue Forecasting to leverage the larger amount of unlabeled low-level behavior cues. We propose the Social Process (SP) models--socially aware sequence-to-sequence (Seq2Seq) models within the Neural Process (NP) family.
  arXiv  Detail & Related papers  (2021-07-28T18:01:08Z)
• Models we Can Trust: Toward a Systematic Discipline of (Agent-Based) Model Interpretation and Validation [0.0]
  We advocate the development of a discipline of interacting with and extracting information from models. We outline some directions for the development of a such a discipline.
  arXiv  Detail & Related papers  (2021-02-23T10:52:22Z)
• Simulating and classifying behavior in adversarial environments based on action-state traces: an application to money laundering [18.625578105241]
  We present a novel way of approaching these types of applications, in particular in the context of Anti-Money Laundering. We provide a mechanism through which diverse, realistic and new unobserved behavior may be generated to discover potential unobserved adversarial actions.
  arXiv  Detail & Related papers  (2020-11-03T16:30:53Z)
• Human Trajectory Forecasting in Crowds: A Deep Learning Perspective [89.4600982169]
  We present an in-depth analysis of existing deep learning-based methods for modelling social interactions. We propose two knowledge-based data-driven methods to effectively capture these social interactions. We develop a large scale interaction-centric benchmark TrajNet++, a significant yet missing component in the field of human trajectory forecasting.
  arXiv  Detail & Related papers  (2020-07-07T17:19:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.