LookALike: Human Mimicry based collaborative decision making

• URL: http://arxiv.org/abs/2403.10824v1
• Date: Sat, 16 Mar 2024 06:25:53 GMT
• Title: LookALike: Human Mimicry based collaborative decision making
• Authors: Rabimba Karanjai, Weidong Shi,
• Abstract summary: General Intelligence falls short when communicating role specific nuances to other systems. We propose and evaluate a novel method that leads to knowledge distillation among LLM agents. We also evaluate how our system performs better in simulated real world tasks compared to state of the art.
• Score: 2.9419583394098425
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Artificial General Intelligence falls short when communicating role specific nuances to other systems. This is more pronounced when building autonomous LLM agents capable and designed to communicate with each other for real world problem solving. Humans can communicate context and domain specific nuances along with knowledge, and that has led to refinement of skills. In this work we propose and evaluate a novel method that leads to knowledge distillation among LLM agents leading to realtime human role play preserving unique contexts without relying on any stored data or pretraining. We also evaluate how our system performs better in simulated real world tasks compared to state of the art.

Related papers

• TheAgentCompany: Benchmarking LLM Agents on Consequential Real World Tasks [52.46737975742287]
  We build a self-contained environment with data that mimics a small software company environment. We find that with the most competitive agent, 24% of the tasks can be completed autonomously. This paints a nuanced picture on task automation with LM agents.
  arXiv  Detail & Related papers  (2024-12-18T18:55:40Z)
• Interactive Dialogue Agents via Reinforcement Learning on Hindsight Regenerations [58.65755268815283]
  Many real dialogues are interactive, meaning an agent's utterances will influence their conversational partner, elicit information, or change their opinion. We use this fact to rewrite and augment existing suboptimal data, and train via offline reinforcement learning (RL) an agent that outperforms both prompting and learning from unaltered human demonstrations. Our results in a user study with real humans show that our approach greatly outperforms existing state-of-the-art dialogue agents.
  arXiv  Detail & Related papers  (2024-11-07T21:37:51Z)
• Continual Skill and Task Learning via Dialogue [3.3511259017219297]
  Continual and interactive robot learning is a challenging problem as the robot is present with human users. We present a framework for robots to query and learn visuo-motor robot skills and task relevant information via natural language dialog interactions with human users.
  arXiv  Detail & Related papers  (2024-09-05T01:51:54Z)
• WorkArena++: Towards Compositional Planning and Reasoning-based Common Knowledge Work Tasks [85.95607119635102]
  Large language models (LLMs) can mimic human-like intelligence. WorkArena++ is designed to evaluate the planning, problem-solving, logical/arithmetic reasoning, retrieval, and contextual understanding abilities of web agents.
  arXiv  Detail & Related papers  (2024-07-07T07:15:49Z)
• Your Co-Workers Matter: Evaluating Collaborative Capabilities of Language Models in Blocks World [13.005764902339523]
  We design a blocks-world environment where two agents, each having unique goals and skills, build a target structure together. To complete the goals, they can act in the world and communicate in natural language. We adopt chain-of-thought prompts that include intermediate reasoning steps to model the partner's state and identify and correct execution errors.
  arXiv  Detail & Related papers  (2024-03-30T04:48:38Z)
• Brain in a Vat: On Missing Pieces Towards Artificial General Intelligence in Large Language Models [83.63242931107638]
  We propose four characteristics of generally intelligent agents. We argue that active engagement with objects in the real world delivers more robust signals for forming conceptual representations. We conclude by outlining promising future research directions in the field of artificial general intelligence.
  arXiv  Detail & Related papers  (2023-07-07T13:58:16Z)
• Inner Monologue: Embodied Reasoning through Planning with Language Models [81.07216635735571]
  Large Language Models (LLMs) can be applied to domains beyond natural language processing. LLMs planning in embodied environments need to consider not just what skills to do, but also how and when to do them. We propose that by leveraging environment feedback, LLMs are able to form an inner monologue that allows them to more richly process and plan in robotic control scenarios.
  arXiv  Detail & Related papers  (2022-07-12T15:20:48Z)
• Creating Multimodal Interactive Agents with Imitation and Self-Supervised Learning [20.02604302565522]
  A common vision from science fiction is that robots will one day inhabit our physical spaces, sense the world as we do, assist our physical labours, and communicate with us through natural language. Here we study how to design artificial agents that can interact naturally with humans using the simplification of a virtual environment. We show that imitation learning of human-human interactions in a simulated world, in conjunction with self-supervised learning, is sufficient to produce a multimodal interactive agent, which we call MIA, that successfully interacts with non-adversarial humans 75% of the time.
  arXiv  Detail & Related papers  (2021-12-07T15:17:27Z)
• Cognitive architecture aided by working-memory for self-supervised multi-modal humans recognition [54.749127627191655]
  The ability to recognize human partners is an important social skill to build personalized and long-term human-robot interactions. Deep learning networks have achieved state-of-the-art results and demonstrated to be suitable tools to address such a task. One solution is to make robots learn from their first-hand sensory data with self-supervision.
  arXiv  Detail & Related papers  (2021-03-16T13:50:24Z)

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.