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08/25/2023
Unified Concept Editing in Diffusion Models
Text-to-image models suffer from various safety issues that may limit th...
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08/17/2023
Linearity of Relation Decoding in Transformer Language Models
Much of the knowledge encoded in transformer language models (LMs) may b...
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08/01/2023
Instructed to Bias: Instruction-Tuned Language Models Exhibit Emergent Cognitive Bias
Recent studies show that instruction tuning and learning from human feed...
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07/13/2023
Generating Benchmarks for Factuality Evaluation of Language Models
Before deploying a language model (LM) within a given domain, it is impo...
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06/01/2023
ReFACT: Updating Text-to-Image Models by Editing the Text Encoder
Text-to-image models are trained on extensive amounts of data, leading t...
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05/24/2023
Understanding Arithmetic Reasoning in Language Models using Causal Mediation Analysis
Mathematical reasoning in large language models (LLMs) has garnered atte...
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05/22/2023
Interpreting Transformer's Attention Dynamic Memory and Visualizing the Semantic Information Flow of GPT
Recent advances in interpretability suggest we can project weights and h...
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05/17/2023
Shielded Representations: Protecting Sensitive Attributes Through Iterative Gradient-Based Projection
Natural language processing models tend to learn and encode social biase...
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03/29/2023
ContraSim – A Similarity Measure Based on Contrastive Learning
Recent work has compared neural network representations via similarity-b...
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03/14/2023
Editing Implicit Assumptions in Text-to-Image Diffusion Models
Text-to-image diffusion models often make implicit assumptions about the...
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12/21/2022
Parallel Context Windows Improve In-Context Learning of Large Language Models
For applications that require processing large amounts of text at infere...
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12/20/2022
Debiasing NLP Models Without Demographic Information
Models trained from real-world data tend to imitate and amplify social b...
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12/20/2022
What Are You Token About? Dense Retrieval as Distributions Over the Vocabulary
Dual encoders are now the dominant architecture for dense retrieval. Yet...
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11/04/2022
Emergent Quantized Communication
The field of emergent communication aims to understand the characteristi...
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10/20/2022
Choose Your Lenses: Flaws in Gender Bias Evaluation
Considerable efforts to measure and mitigate gender bias in recent years...
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10/17/2022
Measures of Information Reflect Memorization Patterns
Neural networks are known to exploit spurious artifacts (or shortcuts) t...
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10/13/2022
Mass-Editing Memory in a Transformer
Recent work has shown exciting promise in updating large language models...
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07/28/2022
Measuring Causal Effects of Data Statistics on Language Model's `Factual' Predictions
Large amounts of training data are one of the major reasons for the high...
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06/01/2022
IDANI: Inference-time Domain Adaptation via Neuron-level Interventions
Large pre-trained models are usually fine-tuned on downstream task data,...
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05/01/2022
MRKL Systems: A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning
Huge language models (LMs) have ushered in a new era for AI, serving as ...
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04/14/2022
How Gender Debiasing Affects Internal Model Representations, and Why It Matters
Common studies of gender bias in NLP focus either on extrinsic bias meas...
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04/11/2022
A Multilingual Perspective Towards the Evaluation of Attribution Methods in Natural Language Inference
Most evaluations of attribution methods focus on the English language. I...
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02/10/2022
Locating and Editing Factual Knowledge in GPT
We investigate the mechanisms underlying factual knowledge recall in aut...
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10/14/2021
On the Pitfalls of Analyzing Individual Neurons in Language Models
While many studies have shown that linguistic information is encoded in ...
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09/09/2021
Debiasing Methods in Natural Language Understanding Make Bias More Accessible
Model robustness to bias is often determined by the generalization on ca...
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08/31/2021
A Generative Approach for Mitigating Structural Biases in Natural Language Inference
Many natural language inference (NLI) datasets contain biases that allow...
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06/10/2021
Causal Analysis of Syntactic Agreement Mechanisms in Neural Language Models
Targeted syntactic evaluations have demonstrated the ability of language...
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06/10/2021
Variational Information Bottleneck for Effective Low-Resource Fine-Tuning
While large-scale pretrained language models have obtained impressive re...
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04/16/2021
Natural Language Inference with a Human Touch: Using Human Explanations to Guide Model Attention
Natural Language Inference (NLI) models are known to learn from biases a...
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02/24/2021
Probing Classifiers: Promises, Shortcomings, and Alternatives
Probing classifiers have emerged as one of the prominent methodologies f...
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12/02/2020
Learning from others' mistakes: Avoiding dataset biases without modeling them
State-of-the-art natural language processing (NLP) models often learn to...
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10/22/2020
Similarity Analysis of Self-Supervised Speech Representations
Self-supervised speech representation learning has recently been a prosp...
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10/06/2020
Analyzing Individual Neurons in Pre-trained Language Models
While a lot of analysis has been carried to demonstrate linguistic knowl...
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06/07/2020
Probing Neural Dialog Models for Conversational Understanding
The predominant approach to open-domain dialog generation relies on end-...
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05/04/2020
The Sensitivity of Language Models and Humans to Winograd Schema Perturbations
Large-scale pretrained language models are the major driving force behin...
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05/03/2020
Similarity Analysis of Contextual Word Representation Models
This paper investigates contextual word representation models from the l...
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04/26/2020
Causal Mediation Analysis for Interpreting Neural NLP: The Case of Gender Bias
Common methods for interpreting neural models in natural language proces...
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04/08/2020
Exploiting Redundancy in Pre-trained Language Models for Efficient Transfer Learning
Large pre-trained contextual word representations have transformed the f...
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11/08/2019
Memory-Augmented Recurrent Neural Networks Can Learn Generalized Dyck Languages
We introduce three memory-augmented Recurrent Neural Networks (MARNNs) a...
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11/01/2019
On the Linguistic Representational Power of Neural Machine Translation Models
Despite the recent success of deep neural networks in natural language p...
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09/27/2019
A Constructive Prediction of the Generalization Error Across Scales
The dependency of the generalization error of neural networks on model a...
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07/09/2019
On Adversarial Removal of Hypothesis-only Bias in Natural Language Inference
Popular Natural Language Inference (NLI) datasets have been shown to be ...
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07/09/2019
Don't Take the Premise for Granted: Mitigating Artifacts in Natural Language Inference
Natural Language Inference (NLI) datasets often contain hypothesis-only ...
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07/09/2019
Analyzing Phonetic and Graphemic Representations in End-to-End Automatic Speech Recognition
End-to-end neural network systems for automatic speech recognition (ASR)...
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06/27/2019
Findings of the First Shared Task on Machine Translation Robustness
We share the findings of the first shared task on improving robustness o...
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06/20/2019
Adversarial Regularization for Visual Question Answering: Strengths, Shortcomings, and Side Effects
Visual question answering (VQA) models have been shown to over-rely on l...
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06/09/2019
LSTM Networks Can Perform Dynamic Counting
In this paper, we systematically assess the ability of standard recurren...
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06/07/2019
Analyzing the Structure of Attention in a Transformer Language Model
The Transformer is a fully attention-based alternative to recurrent netw...
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06/04/2019
Improving Neural Language Models by Segmenting, Attending, and Predicting the Future
Common language models typically predict the next word given the context...
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03/21/2019