Explanation-based Finetuning Makes Models More Robust to Spurious Cues

Explanation-based Finetuning Makes Models More Robust to Spurious Cues

8 May 2023

Josh Magnus Ludan

Marianna Apidianaki

Chris Callison-Burch

Papers citing "Explanation-based Finetuning Makes Models More Robust to Spurious Cues"

7 / 7 papers shown

Title
ALMANACS: A Simulatability Benchmark for Language Model Explainability Edmund Mills Shiye Su Stuart J. Russell Scott Emmons 40 7 0 20 Dec 2023
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models Jason W. Wei Xuezhi Wang Dale Schuurmans Maarten Bosma Brian Ichter F. Xia Ed H. Chi Quoc Le Denny Zhou LM&Ro LRM AI4CE ReLM 315 8,402 0 28 Jan 2022
Toward Annotator Group Bias in Crowdsourcing Haochen Liu J. Thekinen Sinem Mollaoglu Da Tang Ji Yang Youlong Cheng Hui Liu Jiliang Tang 39 16 0 08 Oct 2021
Measuring Association Between Labels and Free-Text Rationales Sarah Wiegreffe Ana Marasović Noah A. Smith 274 170 0 24 Oct 2020
Are We Modeling the Task or the Annotator? An Investigation of Annotator Bias in Natural Language Understanding Datasets Mor Geva Yoav Goldberg Jonathan Berant 235 319 0 21 Aug 2019
e-SNLI: Natural Language Inference with Natural Language Explanations Oana-Maria Camburu Tim Rocktaschel Thomas Lukasiewicz Phil Blunsom LRM 252 620 0 04 Dec 2018
Hypothesis Only Baselines in Natural Language Inference Adam Poliak Jason Naradowsky Aparajita Haldar Rachel Rudinger Benjamin Van Durme 187 576 0 02 May 2018