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Found 23 papers, 14 papers with code
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Despite widespread adoption, machine learning models remain mostly black boxes.
Model-Agnostic Interpretability of Machine Learning
Understanding why machine learning models behave the way they do empowers both system designers and end-users in many ways: in model selection, feature engineering, in order to trust and act upon the predictions, and in more intuitive user interfaces.
Nothing Else Matters: Model-Agnostic Explanations By Identifying Prediction Invariance
At the core of interpretable machine learning is the question of whether humans are able to make accurate predictions about a model's behavior.
Programs as Black-Box Explanations
Recent work in model-agnostic explanations of black-box machine learning has demonstrated that interpretability of complex models does not have to come at the cost of accuracy or model flexibility.
Semantically Equivalent Adversarial Rules for Debugging NLP models
Complex machine learning models for NLP are often brittle, making different predictions for input instances that are extremely similar semantically.
Errudite: Scalable, Reproducible, and Testable Error Analysis
Though error analysis is crucial to understanding and improving NLP models, the common practice of manual, subjective categorization of a small sample of errors can yield biased and incomplete conclusions.
Are Red Roses Red? Evaluating Consistency of Question-Answering Models
Although current evaluation of question-answering systems treats predictions in isolation, we need to consider the relationship between predictions to measure true understanding.
Beyond Accuracy: Behavioral Testing of NLP models with CheckList
Although measuring held-out accuracy has been the primary approach to evaluate generalization, it often overestimates the performance of NLP models, while alternative approaches for evaluating models either focus on individual tasks or on specific behaviors.
Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance
However, prior studies observed improvements from explanations only when the AI, alone, outperformed both the human and the best team.
Polyjuice: Generating Counterfactuals for Explaining, Evaluating, and Improving Models
While counterfactual examples are useful for analysis and training of NLP models, current generation methods either rely on manual labor to create very few counterfactuals, or only instantiate limited types of perturbations such as paraphrases or word substitutions.
Do Feature Attribution Methods Correctly Attribute Features?
Feature attribution methods are exceedingly popular in interpretable machine learning.
Finding and Fixing Spurious Patterns with Explanations
Image classifiers often use spurious patterns, such as "relying on the presence of a person to detect a tennis racket, which do not generalize.
ExSum: From Local Explanations to Model Understanding
Interpretability methods are developed to understand the working mechanisms of black-box models, which is crucial to their responsible deployment.
Fixing Model Bugs with Natural Language Patches
Current approaches for fixing systematic problems in NLP models (e. g. regex patches, finetuning on more data) are either brittle, or labor-intensive and liable to shortcuts.
Adaptive Testing of Computer Vision Models
Vision models often fail systematically on groups of data that share common semantic characteristics (e. g., rare objects or unusual scenes), but identifying these failure modes is a challenge.
Editing Models with Task Arithmetic
Changing how pre-trained models behave -- e. g., improving their performance on a downstream task or mitigating biases learned during pre-training -- is a common practice when developing machine learning systems.
ScatterShot: Interactive In-context Example Curation for Text Transformation
ScatterShot iteratively slices unlabeled data into task-specific patterns, samples informative inputs from underexplored or not-yet-saturated slices in an active learning manner, and helps users label more efficiently with the help of an LLM and the current example set.
ART: Automatic multi-step reasoning and tool-use for large language models
We introduce Automatic Reasoning and Tool-use (ART), a framework that uses frozen LLMs to automatically generate intermediate reasoning steps as a program.
Sparks of Artificial General Intelligence: Early experiments with GPT-4
We contend that (this early version of) GPT-4 is part of a new cohort of LLMs (along with ChatGPT and Google's PaLM for example) that exhibit more general intelligence than previous AI models.
Ranked #32 on
Arithmetic Reasoning
on GSM8K
Supporting Human-AI Collaboration in Auditing LLMs with LLMs
Through the design process we highlight the importance of sensemaking and human-AI communication to leverage complementary strengths of humans and generative models in collaborative auditing.
Collaborative Development of NLP models
Our main insight is learning a \emph{local} model for each concept, and a \emph{global} model to integrate the original data with all concepts.
Targeted Data Generation: Finding and Fixing Model Weaknesses
Even when aggregate accuracy is high, state-of-the-art NLP models often fail systematically on specific subgroups of data, resulting in unfair outcomes and eroding user trust.
Adaptive Testing and Debugging of NLP Models
Current approaches to testing and debugging NLP models rely on highly variable human creativity and extensive labor, or only work for a very restrictive class of bugs.
