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Found 12 papers, 3 papers with code
Stick to Your Role! Context-dependence and Stability of Personal Value Expression in Large Language Models
We present a case-study on the stability of value expression over different contexts (simulated conversations on different topics) as measured using a standard psychology questionnaire (PVQ) and on behavioral downstream tasks.
Large Language Models as Superpositions of Cultural Perspectives
We introduce the concept of perspective controllability, which refers to a model's affordance to adopt various perspectives with differing values and personality traits.
The SocialAI School: Insights from Developmental Psychology Towards Artificial Socio-Cultural Agents
Developmental psychologists have long-established the importance of socio-cognitive abilities in human intelligence.
SocialAI: Benchmarking Socio-Cognitive Abilities in Deep Reinforcement Learning Agents
In this paper, we argue that aiming towards human-level AI requires a broader set of key social skills: 1) language use in complex and variable social contexts; 2) beyond language, complex embodied communication in multimodal settings within constantly evolving social worlds.
SocialAI 0.1: Towards a Benchmark to Stimulate Research on Socio-Cognitive Abilities in Deep Reinforcement Learning Agents
Building embodied autonomous agents capable of participating in social interactions with humans is one of the main challenges in AI.
TeachMyAgent: a Benchmark for Automatic Curriculum Learning in Deep RL
Training autonomous agents able to generalize to multiple tasks is a key target of Deep Reinforcement Learning (DRL) research.
Meta Automatic Curriculum Learning
In such complex task spaces, it is essential to rely on some form of Automatic Curriculum Learning (ACL) to adapt the task sampling distribution to a given learning agent, instead of randomly sampling tasks, as many could end up being either trivial or unfeasible.
Trying AGAIN instead of Trying Longer: Prior Learning for Automatic Curriculum Learning
A major challenge in the Deep RL (DRL) community is to train agents able to generalize over unseen situations, which is often approached by training them on a diversity of tasks (or environments).
Automatic Curriculum Learning For Deep RL: A Short Survey
Automatic Curriculum Learning (ACL) has become a cornerstone of recent successes in Deep Reinforcement Learning (DRL). These methods shape the learning trajectories of agents by challenging them with tasks adapted to their capacities.
Language Grounding through Social Interactions and Curiosity-Driven Multi-Goal Learning
We propose LE2 (Language Enhanced Exploration), a learning algorithm leveraging intrinsic motivations and natural language (NL) interactions with a descriptive social partner (SP).
Teacher algorithms for curriculum learning of Deep RL in continuously parameterized environments
We consider the problem of how a teacher algorithm can enable an unknown Deep Reinforcement Learning (DRL) student to become good at a skill over a wide range of diverse environments.
Intrinsically Motivated Goal Exploration Processes with Automatic Curriculum Learning
We present an algorithmic approach called Intrinsically Motivated Goal Exploration Processes (IMGEP) to enable similar properties of autonomous learning in machines.
