Search Results for author: Nino Scherrer

Found 9 papers, 5 papers with code

FinanceBench: A New Benchmark for Financial Question Answering

1 code implementation20 Nov 2023 Pranab Islam, Anand Kannappan, Douwe Kiela, Rebecca Qian, Nino Scherrer, Bertie Vidgen

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).

Question Answering Retrieval +1

SimpleSafetyTests: a Test Suite for Identifying Critical Safety Risks in Large Language Models

no code implementations14 Nov 2023 Bertie Vidgen, Nino Scherrer, Hannah Rose Kirk, Rebecca Qian, Anand Kannappan, Scott A. Hale, Paul Röttger

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.

Evaluating the Moral Beliefs Encoded in LLMs

1 code implementation NeurIPS 2023 Nino Scherrer, Claudia Shi, Amir Feder, David M. Blei

(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.

Moral Scenarios

Trust Your $\nabla$: Gradient-based Intervention Targeting for Causal Discovery

no code implementations NeurIPS 2023 Mateusz Olko, Michał Zając, Aleksandra Nowak, Nino Scherrer, Yashas Annadani, Stefan Bauer, Łukasz Kuciński, Piotr Miłoś

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.

Causal Discovery Experimental Design

Federated Causal Discovery From Interventions

3 code implementations7 Nov 2022 Amin Abyaneh, Nino Scherrer, Patrick Schwab, Stefan Bauer, Bernhard Schölkopf, Arash Mehrjou

We propose FedCDI, a federated framework for inferring causal structures from distributed data containing interventional samples.

Causal Discovery Federated Learning +1

On the Generalization and Adaption Performance of Causal Models

no code implementations9 Jun 2022 Nino Scherrer, Anirudh Goyal, Stefan Bauer, Yoshua Bengio, Nan Rosemary Ke

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.

Causal Discovery Out-of-Distribution Generalization

Variational Causal Networks: Approximate Bayesian Inference over Causal Structures

1 code implementation14 Jun 2021 Yashas Annadani, Jonas Rothfuss, Alexandre Lacoste, Nino Scherrer, Anirudh Goyal, Yoshua Bengio, Stefan Bauer

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.

Bayesian Inference Causal Inference +2

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