Social Skill Training with Large Language Models

• URL: http://arxiv.org/abs/2404.04204v1
• Date: Fri, 5 Apr 2024 16:29:58 GMT
• Title: Social Skill Training with Large Language Models
• Authors: Diyi Yang, Caleb Ziems, William Held, Omar Shaikh, Michael S. Bernstein, John Mitchell,
• Abstract summary: People rely on social skills like conflict resolution to communicate effectively and to thrive in both work and personal life. This perspective paper identifies social skill barriers to enter specialized fields. We present a solution that leverages large language models for social skill training via a generic framework.
• Score: 65.40795606463101
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: People rely on social skills like conflict resolution to communicate effectively and to thrive in both work and personal life. However, practice environments for social skills are typically out of reach for most people. How can we make social skill training more available, accessible, and inviting? Drawing upon interdisciplinary research from communication and psychology, this perspective paper identifies social skill barriers to enter specialized fields. Then we present a solution that leverages large language models for social skill training via a generic framework. Our AI Partner, AI Mentor framework merges experiential learning with realistic practice and tailored feedback. This work ultimately calls for cross-disciplinary innovation to address the broader implications for workforce development and social equality.

Related papers

• The Call for Socially Aware Language Technologies [94.6762219597438]
  We argue that many of these issues share a common core: a lack of awareness of the factors, context, and implications of the social environment in which NLP operates. We argue that substantial challenges remain for NLP to develop social awareness and that we are just at the beginning of a new era for the field.
  arXiv  Detail & Related papers  (2024-05-03T18:12:39Z)
• Advancing Social Intelligence in AI Agents: Technical Challenges and Open Questions [67.60397632819202]
  Building socially-intelligent AI agents (Social-AI) is a multidisciplinary, multimodal research goal. We identify a set of underlying technical challenges and open questions for researchers across computing communities to advance Social-AI.
  arXiv  Detail & Related papers  (2024-04-17T02:57:42Z)
• SOTOPIA-$π$: Interactive Learning of Socially Intelligent Language Agents [73.35393511272791]
  We propose an interactive learning method, SOTOPIA-$pi$, improving the social intelligence of language agents. This method leverages behavior cloning and self-reinforcement training on filtered social interaction data according to large language model (LLM) ratings.
  arXiv  Detail & Related papers  (2024-03-13T17:17:48Z)
• Social learning spontaneously emerges by searching optimal heuristics with deep reinforcement learning [0.0]
  We employ a deep reinforcement learning model to optimize the social learning strategies of agents in a cooperative game in a multi-dimensional landscape. We find that the agent spontaneously learns various concepts of social learning, such as copying, focusing on frequent and well-performing neighbors, self-comparison, and the importance of balancing between individual and social learning. We demonstrate the superior performance of the reinforcement learning agent in various environments, including temporally changing environments and real social networks.
  arXiv  Detail & Related papers  (2022-04-26T15:10:27Z)
• SocialAI: Benchmarking Socio-Cognitive Abilities in Deep Reinforcement Learning Agents [23.719833581321033]
  Building embodied autonomous agents capable of participating in social interactions with humans is one of the main challenges in AI. We argue that aiming towards human-level AI requires a broader set of key social skills. We present SocialAI, a benchmark to assess the acquisition of social skills of DRL agents.
  arXiv  Detail & Related papers  (2021-07-02T10:39:18Z)
• SocialAI 0.1: Towards a Benchmark to Stimulate Research on Socio-Cognitive Abilities in Deep Reinforcement Learning Agents [23.719833581321033]
  Building embodied autonomous agents capable of participating in social interactions with humans is one of the main challenges in AI. Current approaches focus on language as a communication tool in very simplified and non diverse social situations. We argue that aiming towards human-level AI requires a broader set of key social skills.
  arXiv  Detail & Related papers  (2021-04-27T14:16:29Z)
• Emergent Social Learning via Multi-agent Reinforcement Learning [91.57176641192771]
  Social learning is a key component of human and animal intelligence. This paper investigates whether independent reinforcement learning agents can learn to use social learning to improve their performance.
  arXiv  Detail & Related papers  (2020-10-01T17:54:14Z)
• Experience Grounds Language [185.73483760454454]
  Language understanding research is held back by a failure to relate language to the physical world it describes and to the social interactions it facilitates. Despite the incredible effectiveness of language processing models to tackle tasks after being trained on text alone, successful linguistic communication relies on a shared experience of the world.
  arXiv  Detail & Related papers  (2020-04-21T16:56:27Z)

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.