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07/27/2023
Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback
Reinforcement learning from human feedback (RLHF) is a technique for tra...
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06/15/2023
Can Language Models Teach Weaker Agents? Teacher Explanations Improve Students via Theory of Mind
Large Language Models (LLMs) perform complex reasoning by generating exp...
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06/09/2023
Adaptive Contextual Perception: How to Generalize to New Backgrounds and Ambiguous Objects
Biological vision systems make adaptive use of context to recognize obje...
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01/10/2023
Does Localization Inform Editing? Surprising Differences in Causality-Based Localization vs. Knowledge Editing in Language Models
Language models are known to learn a great quantity of factual informati...
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11/14/2022
Are Hard Examples also Harder to Explain? A Study with Human and Model-Generated Explanations
Recent work on explainable NLP has shown that few-shot prompting can ena...
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09/21/2022
Summarization Programs: Interpretable Abstractive Summarization with Neural Modular Trees
Current abstractive summarization models either suffer from a lack of cl...
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06/22/2022
VisFIS: Visual Feature Importance Supervision with Right-for-the-Right-Reason Objectives
Many past works aim to improve visual reasoning in models by supervising...
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03/14/2022
GrIPS: Gradient-free, Edit-based Instruction Search for Prompting Large Language Models
Providing natural language instructions in prompts is a useful new parad...
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11/26/2021
Do Language Models Have Beliefs? Methods for Detecting, Updating, and Visualizing Model Beliefs
Do language models have beliefs about the world? Dennett (1995) famously...
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11/01/2021
Low-Cost Algorithmic Recourse for Users With Uncertain Cost Functions
The problem of identifying algorithmic recourse for people affected by m...
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06/01/2021
Search Methods for Sufficient, Socially-Aligned Feature Importance Explanations with In-Distribution Counterfactuals
Feature importance (FI) estimates are a popular form of explanation, and...
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02/03/2021
When Can Models Learn From Explanations? A Formal Framework for Understanding the Roles of Explanation Data
Many methods now exist for conditioning model outputs on task instructio...
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12/31/2020
FastIF: Scalable Influence Functions for Efficient Model Interpretation and Debugging
Influence functions approximate the 'influences' of training data-points...
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10/08/2020
Leakage-Adjusted Simulatability: Can Models Generate Non-Trivial Explanations of Their Behavior in Natural Language?
Data collection for natural language (NL) understanding tasks has increa...
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05/04/2020
Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior?
Algorithmic approaches to interpreting machine learning models have prol...
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06/25/2019
Interpretable Image Recognition with Hierarchical Prototypes
Vision models are interpretable when they classify objects on the basis ...
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11/13/2018