Search Results for author:
Found 11 papers, 6 papers with code
Efficient Model-agnostic Alignment via Bayesian Persuasion
This paper explores an efficient method for aligning black-box large models using smaller models, introducing a model-agnostic and lightweight Bayesian Persuasion Alignment framework.
Foundational Challenges in Assuring Alignment and Safety of Large Language Models
This work identifies 18 foundational challenges in assuring the alignment and safety of large language models (LLMs).
Incentive Compatibility for AI Alignment in Sociotechnical Systems: Positions and Prospects
The burgeoning integration of artificial intelligence (AI) into human society brings forth significant implications for societal governance and safety.
CivRealm: A Learning and Reasoning Odyssey in Civilization for Decision-Making Agents
Within CivRealm, we provide interfaces for two typical agent types: tensor-based agents that focus on learning, and language-based agents that emphasize reasoning.
AI Alignment: A Comprehensive Survey
The former aims to make AI systems aligned via alignment training, while the latter aims to gain evidence about the systems' alignment and govern them appropriately to avoid exacerbating misalignment risks.
ValueDCG: Measuring Comprehensive Human Value Understanding Ability of Language Models
Personal values are a crucial factor behind human decision-making.
ProAgent: Building Proactive Cooperative Agents with Large Language Models
Building agents with adaptive behavior in cooperative tasks stands as a paramount goal in the realm of multi-agent systems.
Heterogeneous Value Alignment Evaluation for Large Language Models
We conduct evaluations with new auto-metric \textit{value rationality} to represent the ability of LLMs to align with specific values.
STAS: Spatial-Temporal Return Decomposition for Multi-agent Reinforcement Learning
It first decomposes the global return back to each time step, then utilizes the Shapley Value to redistribute the individual payoff from the decomposed global reward.
Contextual Transformer for Offline Meta Reinforcement Learning
Firstly, we propose prompt tuning for offline RL, where a context vector sequence is concatenated with the input to guide the conditional policy generation.
Continuous Decomposition of Granularity for Neural Paraphrase Generation
While Transformers have had significant success in paragraph generation, they treat sentences as linear sequences of tokens and often neglect their hierarchical information.
