Mechanistic Interpretability Benchmark

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atticusg 

authored a paper 3 months ago

Recurrent Neural Networks Learn to Store and Generate Sequences using Non-Linear Representations

Paper • 2408.10920 • Published Aug 20 • 1

AdamBelfki 

authored a paper 5 months ago

NNsight and NDIF: Democratizing Access to Foundation Model Internals

Paper • 2407.14561 • Published Jul 18 • 33

amueller 

authored a paper 5 months ago

NNsight and NDIF: Democratizing Access to Foundation Model Internals

Paper • 2407.14561 • Published Jul 18 • 33

sarahwie 

authored 5 papers 6 months ago

Self-Refine: Iterative Refinement with Self-Feedback

Paper • 2303.17651 • Published Mar 30, 2023 • 2

Reframing Human-AI Collaboration for Generating Free-Text Explanations

Paper • 2112.08674 • Published Dec 16, 2021

Teach Me to Explain: A Review of Datasets for Explainable Natural Language Processing

Paper • 2102.12060 • Published Feb 24, 2021

Attentiveness to Answer Choices Doesn't Always Entail High QA Accuracy

Paper • 2305.14596 • Published May 24, 2023 • 1

The Unreasonable Effectiveness of Easy Training Data for Hard Tasks

Paper • 2401.06751 • Published Jan 12 • 1

atticusg 

authored 12 papers 7 months ago

DynaSent: A Dynamic Benchmark for Sentiment Analysis

Paper • 2012.15349 • Published Dec 30, 2020

ScoNe: Benchmarking Negation Reasoning in Language Models With Fine-Tuning and In-Context Learning

Paper • 2305.19426 • Published May 30, 2023

CEBaB: Estimating the Causal Effects of Real-World Concepts on NLP Model Behavior

Paper • 2205.14140 • Published May 27, 2022

Rigorously Assessing Natural Language Explanations of Neurons

Paper • 2309.10312 • Published Sep 19, 2023

Linear Representations of Sentiment in Large Language Models

Paper • 2310.15154 • Published Oct 23, 2023

Neural Natural Language Inference Models Partially Embed Theories of Lexical Entailment and Negation

Paper • 2004.14623 • Published Apr 30, 2020

A Reply to Makelov et al. (2023)'s "Interpretability Illusion" Arguments

Paper • 2401.12631 • Published Jan 23

ReFT: Representation Finetuning for Language Models

Paper • 2404.03592 • Published Apr 4 • 91

pyvene: A Library for Understanding and Improving PyTorch Models via Interventions

Paper • 2403.07809 • Published Mar 12 • 1

Causal Abstraction for Faithful Model Interpretation

Paper • 2301.04709 • Published Jan 11, 2023

Finding Alignments Between Interpretable Causal Variables and Distributed Neural Representations

Paper • 2303.02536 • Published Mar 5, 2023 • 1

RAVEL: Evaluating Interpretability Methods on Disentangling Language Model Representations

Paper • 2402.17700 • Published Feb 27 • 2