Marco Tulio Ribeiro | DeepAI

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∙ 06/05/2023

AHA!: Facilitating AI Impact Assessment by Generating Examples of Harms

While demands for change and accountability for harmful AI consequences ...

0 Zana Buçinca, et al. ∙

∙ 05/28/2023

Targeted Data Generation: Finding and Fixing Model Weaknesses

Even when aggregate accuracy is high, state-of-the-art NLP models often ...

6 Zexue He, et al. ∙

∙ 05/20/2023

Collaborative Development of NLP models

Despite substantial advancements, Natural Language Processing (NLP) mode...

2 Fereshte Khani, et al. ∙

∙ 04/19/2023

Supporting Human-AI Collaboration in Auditing LLMs with LLMs

Large language models are becoming increasingly pervasive and ubiquitous...

0 Charvi Rastogi, et al. ∙

∙ 03/22/2023

Sparks of Artificial General Intelligence: Early experiments with GPT-4

Artificial intelligence (AI) researchers have been developing and refini...

6 Sébastien Bubeck, et al. ∙

∙ 03/16/2023

ART: Automatic multi-step reasoning and tool-use for large language models

Large language models (LLMs) can perform complex reasoning in few- and z...

0 Bhargavi Paranjape, et al. ∙

∙ 02/14/2023

ScatterShot: Interactive In-context Example Curation for Text Transformation

The in-context learning capabilities of LLMs like GPT-3 allow annotators...

0 Tongshuang Wu, et al. ∙

∙ 12/08/2022

Editing Models with Task Arithmetic

Changing how pre-trained models behave – e.g., improving their performan...

0 Gabriel Ilharco, et al. ∙

∙ 12/06/2022

Adaptive Testing of Computer Vision Models

Vision models often fail systematically on groups of data that share com...

0 Irena Gao, et al. ∙

∙ 11/07/2022

Fixing Model Bugs with Natural Language Patches

Current approaches for fixing systematic problems in NLP models (e.g. re...

0 Shikhar Murty, et al. ∙

∙ 07/08/2022

Evaluating Systemic Error Detection Methods using Synthetic Images

We introduce SpotCheck, a framework for generating synthetic datasets to...

0 Gregory Plumb, et al. ∙

∙ 04/30/2022

ExSum: From Local Explanations to Model Understanding

Interpretability methods are developed to understand the working mechani...

0 Yilun Zhou, et al. ∙

∙ 06/03/2021

Finding and Fixing Spurious Patterns with Explanations

Machine learning models often use spurious patterns such as "relying on ...

0 Gregory Plumb, et al. ∙

∙ 01/01/2021

Polyjuice: Automated, General-purpose Counterfactual Generation

Counterfactual examples have been shown to be useful for many applicatio...

4 Tongshuang Wu, et al. ∙

∙ 06/26/2020

Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance

Increasingly, organizations are pairing humans with AI systems to improv...

20 Gagan Bansal, et al. ∙

∙ 05/08/2020

Beyond Accuracy: Behavioral Testing of NLP models with CheckList

Although measuring held-out accuracy has been the primary approach to ev...

0 Marco Tulio Ribeiro, et al. ∙

∙ 11/22/2016

Programs as Black-Box Explanations

Recent work in model-agnostic explanations of black-box machine learning...

0 Sameer Singh, et al. ∙

∙ 11/17/2016

Nothing Else Matters: Model-Agnostic Explanations By Identifying Prediction Invariance

At the core of interpretable machine learning is the question of whether...

0 Marco Tulio Ribeiro, et al. ∙

∙ 06/16/2016

Model-Agnostic Interpretability of Machine Learning

Understanding why machine learning models behave the way they do empower...

0 Marco Tulio Ribeiro, et al. ∙

∙ 02/16/2016

"Why Should I Trust You?": Explaining the Predictions of Any Classifier

Despite widespread adoption, machine learning models remain mostly black...

0 Marco Tulio Ribeiro, et al. ∙

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