Influence Functions for Sequence Tagging Models

25 October 2022

Papers citing "Influence Functions for Sequence Tagging Models"

8 / 8 papers shown

Title
Unleashing the Power of Data Tsunami: A Comprehensive Survey on Data Assessment and Selection for Instruction Tuning of Language Models Yulei Qin Yuncheng Yang Pengcheng Guo Gang Li Hang Shao Yuchen Shi Zihan Xu Yun Gu Ke Li Xing Sun ALM 88 12 0 31 Dec 2024
Deeper Understanding of Black-box Predictions via Generalized Influence Functions Hyeonsu Lyu Jonggyu Jang Sehyun Ryu H. Yang TDI AI4CE 13 5 0 09 Dec 2023
CleanCoNLL: A Nearly Noise-Free Named Entity Recognition Dataset Susanna Rücker A. Akbik 22 5 0 24 Oct 2023
Influence Function Based Second-Order Channel Pruning-Evaluating True Loss Changes For Pruning Is Possible Without Retraining Hongrong Cheng Miao Zhang Javen Qinfeng Shi AAML 20 1 0 13 Aug 2023
How do languages influence each other? Studying cross-lingual data sharing during LM fine-tuning Rochelle Choenni Dan Garrette Ekaterina Shutova 25 15 0 22 May 2023
Inseq: An Interpretability Toolkit for Sequence Generation Models Gabriele Sarti Nils Feldhus Ludwig Sickert Oskar van der Wal Malvina Nissim Arianna Bisazza 25 64 0 27 Feb 2023
Big Bird: Transformers for Longer Sequences Manzil Zaheer Guru Guruganesh Kumar Avinava Dubey Joshua Ainslie Chris Alberti ... Philip Pham Anirudh Ravula Qifan Wang Li Yang Amr Ahmed VLM 251 2,012 0 28 Jul 2020
A causal framework for explaining the predictions of black-box sequence-to-sequence models David Alvarez-Melis Tommi Jaakkola CML 219 201 0 06 Jul 2017