Search Results for author:
Found 12 papers, 6 papers with code
Multi-agent cooperation through learning-aware policy gradients
Self-interested individuals often fail to cooperate, posing a fundamental challenge for multi-agent learning.
Inducing Group Fairness in Prompt-Based Language Model Decisions
Further, the `remediation toolkit' is incomplete for LM-based decision makers and little is understood about how to improve decision maker group fairness while maintaining classifier performance.
Introducing v0.5 of the AI Safety Benchmark from MLCommons
We created a new taxonomy of 13 hazard categories, of which 7 have tests in the v0. 5 benchmark.
FinanceBench: A New Benchmark for Financial Question Answering
We test 16 state of the art model configurations (including GPT-4-Turbo, Llama2 and Claude2, with vector stores and long context prompts) on a sample of 150 cases from FinanceBench, and manually review their answers (n=2, 400).
SimpleSafetyTests: a Test Suite for Identifying Critical Safety Risks in Large Language Models
While some of the models do not give a single unsafe response, most give unsafe responses to more than 20% of the prompts, with over 50% unsafe responses in the extreme.
Uncovering mesa-optimization algorithms in Transformers
Some autoregressive models exhibit in-context learning capabilities: being able to learn as an input sequence is processed, without undergoing any parameter changes, and without being explicitly trained to do so.
Evaluating the Moral Beliefs Encoded in LLMs
(2) We apply this method to study what moral beliefs are encoded in different LLMs, especially in ambiguous cases where the right choice is not obvious.
Trust Your $\nabla$: Gradient-based Intervention Targeting for Causal Discovery
In this work, we propose a novel Gradient-based Intervention Targeting method, abbreviated GIT, that 'trusts' the gradient estimator of a gradient-based causal discovery framework to provide signals for the intervention acquisition function.
Federated Causal Discovery From Interventions
We propose FedCDI, a federated framework for inferring causal structures from distributed data containing interventional samples.
On the Generalization and Adaption Performance of Causal Models
Our analysis shows that the modular neural causal models outperform other models on both zero and few-shot adaptation in low data regimes and offer robust generalization.
Learning Neural Causal Models with Active Interventions
Discovering causal structures from data is a challenging inference problem of fundamental importance in all areas of science.
Variational Causal Networks: Approximate Bayesian Inference over Causal Structures
However, a crucial aspect to acting intelligently upon the knowledge about causal structure which has been inferred from finite data demands reasoning about its uncertainty.
