How to talk so AI will learn: Instructions, descriptions, and autonomy

How to talk so AI will learn: Instructions, descriptions, and autonomy

16 June 2022

Robert D. Hawkins

Thomas L. Griffiths

Dylan Hadfield-Menell

Papers citing "How to talk so AI will learn: Instructions, descriptions, and autonomy"

18 / 18 papers shown

Title
M3HF: Multi-agent Reinforcement Learning from Multi-phase Human Feedback of Mixed Quality Ziyan Wang Zhicheng Zhang Fei Fang Yali Du 39 0 0 03 Mar 2025
Adaptive Language-Guided Abstraction from Contrastive Explanations Andi Peng Belinda Z. Li Ilia Sucholutsky Nishanth Kumar Julie A. Shah Jacob Andreas Andreea Bobu OffRL 19 1 0 12 Sep 2024
Problem Solving Through Human-AI Preference-Based Cooperation Subhabrata Dutta Timo Kaufmann Goran Glavas Ivan Habernal Kristian Kersting Frauke Kreuter Mira Mezini Iryna Gurevych Eyke Hüllermeier Hinrich Schuetze 82 1 0 14 Aug 2024
Representational Alignment Supports Effective Machine Teaching Ilia Sucholutsky Katherine M. Collins Maya Malaviya Nori Jacoby Weiyang Liu ... J. Tenenbaum Brad Love Z. Pardos Adrian Weller Thomas L. Griffiths 48 3 0 06 Jun 2024
Pragmatic Feature Preferences: Learning Reward-Relevant Preferences from Human Input Andi Peng Yuying Sun Tianmin Shu David Abel 32 3 0 23 May 2024
Can Language Models Solve Olympiad Programming? Quan Shi Michael Tang Karthik Narasimhan Shunyu Yao ELM LRM ReLM 41 21 0 16 Apr 2024
The Role of Higher-Order Cognitive Models in Active Learning Oskar Keurulainen G. Alcan Ville Kyrki 33 0 0 09 Jan 2024
Learning a Hierarchical Planner from Humans in Multiple Generations Leonardo Hernandez Cano Yewen Pu Robert D. Hawkins Josh Tenenbaum Armando Solar-Lezama 15 2 0 17 Oct 2023
Cognitive Architectures for Language Agents T. Sumers Shunyu Yao Karthik Narasimhan Thomas L. Griffiths LLMAG LM&Ro 34 150 0 05 Sep 2023
RLHF-Blender: A Configurable Interactive Interface for Learning from Diverse Human Feedback Yannick Metz David Lindner Raphael Baur Daniel A. Keim Mennatallah El-Assady AI4CE 26 10 0 08 Aug 2023
Language Models are Bounded Pragmatic Speakers: Understanding RLHF from a Bayesian Cognitive Modeling Perspective Khanh Nguyen LRM 18 8 0 28 May 2023
Maybe Only 0.5% Data is Needed: A Preliminary Exploration of Low Training Data Instruction Tuning Haowen Chen Yiming Zhang Qi Zhang Hantao Yang Xiaomeng Hu Xuetao Ma Yifan YangGong J. Zhao ALM 61 46 0 16 May 2023
Define, Evaluate, and Improve Task-Oriented Cognitive Capabilities for Instruction Generation Models Lingjun Zhao Khanh Nguyen Hal Daumé ELM 22 6 0 21 Dec 2022
Improving Intrinsic Exploration with Language Abstractions Jesse Mu Victor Zhong Roberta Raileanu Minqi Jiang Noah D. Goodman Tim Rocktaschel Edward Grefenstette 95 63 0 17 Feb 2022
Skill Induction and Planning with Latent Language Pratyusha Sharma Antonio Torralba Jacob Andreas LM&Ro 181 108 0 04 Oct 2021
Reward (Mis)design for Autonomous Driving W. B. Knox A. Allievi Holger Banzhaf Felix Schmitt Peter Stone 67 112 0 28 Apr 2021
Interactive Learning from Activity Description Khanh Nguyen Dipendra Kumar Misra Robert Schapire Miroslav Dudík Patrick Shafto 45 34 0 13 Feb 2021
Grounding Language to Entities and Dynamics for Generalization in Reinforcement Learning H. Wang Victor Zhong Karthik Narasimhan 76 53 0 19 Jan 2021