Inseq: An Interpretability Toolkit for Sequence Generation Models

Inseq: An Interpretability Toolkit for Sequence Generation Models

27 February 2023

Nils Feldhus

Oskar van der Wal

Arianna Bisazza

Papers citing "Inseq: An Interpretability Toolkit for Sequence Generation Models"

14 / 14 papers shown

Title
Gender Bias in Explainability: Investigating Performance Disparity in Post-hoc Methods Mahdi Dhaini Ege Erdogan Nils Feldhus Gjergji Kasneci 21 0 0 02 May 2025
SPES: Spectrogram Perturbation for Explainable Speech-to-Text Generation Dennis Fucci Marco Gaido Beatrice Savoldi Matteo Negri Mauro Cettolo L. Bentivogli 38 1 0 03 Nov 2024
Evaluating Human Alignment and Model Faithfulness of LLM Rationale Mohsen Fayyaz Fan Yin Jiao Sun Nanyun Peng 37 3 0 28 Jun 2024
MambaLRP: Explaining Selective State Space Sequence Models F. Jafari G. Montavon Klaus-Robert Müller Oliver Eberle Mamba 41 9 0 11 Jun 2024
InterroLang: Exploring NLP Models and Datasets through Dialogue-based Explanations Nils Feldhus Qianli Wang Tatiana Anikina Sahil Chopra Cennet Oguz Sebastian Möller 17 9 0 09 Oct 2023
Explaining How Transformers Use Context to Build Predictions Javier Ferrando Gerard I. Gállego Ioannis Tsiamas Marta R. Costa-jussá 8 31 0 21 May 2023
Quantifying Context Mixing in Transformers Hosein Mohebbi Willem H. Zuidema Grzegorz Chrupała A. Alishahi 161 24 0 30 Jan 2023
"Will You Find These Shortcuts?" A Protocol for Evaluating the Faithfulness of Input Salience Methods for Text Classification Jasmijn Bastings Sebastian Ebert Polina Zablotskaia Anders Sandholm Katja Filippova 102 75 0 14 Nov 2021
Evaluating the Faithfulness of Importance Measures in NLP by Recursively Masking Allegedly Important Tokens and Retraining Andreas Madsen Nicholas Meade Vaibhav Adlakha Siva Reddy 96 35 0 15 Oct 2021
Incorporating Residual and Normalization Layers into Analysis of Masked Language Models Goro Kobayashi Tatsuki Kuribayashi Sho Yokoi Kentaro Inui 153 45 0 15 Sep 2021
The Tatoeba Translation Challenge -- Realistic Data Sets for Low Resource and Multilingual MT Jörg Tiedemann 160 163 0 13 Oct 2020
Scaling Laws for Neural Language Models Jared Kaplan Sam McCandlish T. Henighan Tom B. Brown B. Chess R. Child Scott Gray Alec Radford Jeff Wu Dario Amodei 220 3,054 0 23 Jan 2020
A causal framework for explaining the predictions of black-box sequence-to-sequence models David Alvarez-Melis Tommi Jaakkola CML 210 201 0 06 Jul 2017
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 243 9,042 0 06 Jun 2015