Search Results for author:
Found 21 papers, 3 papers with code
Multi-turn Evaluation of Anthropomorphic Behaviours in Large Language Models
First, we develop a multi-turn evaluation of 14 anthropomorphic behaviours.
LearnLM: Improving Gemini for Learning
Today's generative AI systems are tuned to present information by default rather than engage users in service of learning as a human tutor would.
Towards Responsible Development of Generative AI for Education: An Evaluation-Driven Approach
A major challenge facing the world is the provision of equitable and universal access to quality education.
Recourse for reclamation: Chatting with generative language models
Researchers and developers increasingly rely on toxicity scoring to moderate generative language model outputs, in settings such as customer service, information retrieval, and content generation.
How FaR Are Large Language Models From Agents with Theory-of-Mind?
We propose a new evaluation paradigm for large language models (LLMs): Thinking for Doing (T4D), which requires models to connect inferences about others' mental states to actions in social scenarios.
Heterogeneous Social Value Orientation Leads to Meaningful Diversity in Sequential Social Dilemmas
In social psychology, Social Value Orientation (SVO) describes an individual's propensity to allocate resources between themself and others.
Combining Tree-Search, Generative Models, and Nash Bargaining Concepts in Game-Theoretic Reinforcement Learning
Multiagent reinforcement learning (MARL) has benefited significantly from population-based and game-theoretic training regimes.
Developing, Evaluating and Scaling Learning Agents in Multi-Agent Environments
The Game Theory & Multi-Agent team at DeepMind studies several aspects of multi-agent learning ranging from computing approximations to fundamental concepts in game theory to simulating social dilemmas in rich spatial environments and training 3-d humanoids in difficult team coordination tasks.
Subverting machines, fluctuating identities: Re-learning human categorization
Most machine learning systems that interact with humans construct some notion of a person's "identity," yet the default paradigm in AI research envisions identity with essential attributes that are discrete and static.
Warmth and competence in human-agent cooperation
Participants' perceptions of warmth and competence predict their stated preferences for different agents, above and beyond objective performance metrics.
Hidden Agenda: a Social Deduction Game with Diverse Learned Equilibria
A key challenge in the study of multiagent cooperation is the need for individual agents not only to cooperate effectively, but to decide with whom to cooperate.
Statistical discrimination in learning agents
Undesired bias afflicts both human and algorithmic decision making, and may be especially prevalent when information processing trade-offs incentivize the use of heuristics.
Collaborating with Humans without Human Data
Here, we study the problem of how to train agents that collaborate well with human partners without using human data.
A multi-agent reinforcement learning model of reputation and cooperation in human groups
Collective action demands that individuals efficiently coordinate how much, where, and when to cooperate.
Multi-agent Reinforcement Learning
reinforcement-learning
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Quantifying the effects of environment and population diversity in multi-agent reinforcement learning
Generalization is a major challenge for multi-agent reinforcement learning.
Fairness for Unobserved Characteristics: Insights from Technological Impacts on Queer Communities
Advances in algorithmic fairness have largely omitted sexual orientation and gender identity.
Neural Recursive Belief States in Multi-Agent Reinforcement Learning
We also show that higher-order belief models outperform agents with lower-order models.
Open Problems in Cooperative AI
We see opportunity to more explicitly focus on the problem of cooperation, to construct unified theory and vocabulary, and to build bridges with adjacent communities working on cooperation, including in the natural, social, and behavioural sciences.
Social diversity and social preferences in mixed-motive reinforcement learning
Recent research on reinforcement learning in pure-conflict and pure-common interest games has emphasized the importance of population heterogeneity.
Relational inductive bias for physical construction in humans and machines
While current deep learning systems excel at tasks such as object classification, language processing, and gameplay, few can construct or modify a complex system such as a tower of blocks.
Inequity aversion improves cooperation in intertemporal social dilemmas
Groups of humans are often able to find ways to cooperate with one another in complex, temporally extended social dilemmas.
