Differences

This shows you the differences between two versions of the page.

--- nlp:bias [2022/03/01 23:14] – jmflanig
+++ nlp:bias [2025/05/14 18:36] (current) – [Dataset Bias] jmflanig
@@ Line 2: / Line 2: @@
 ===== Bias (Fairness, Society and Ethics) =====
+==== Bias in General ====
+  * **Overviews**
+    * [[https://arxiv.org/pdf/2110.08527.pdf|Meade et al 2021 - An Empirical Survey of the Effectiveness of Debiasing Techniques for Pre-trained Language Models]]
+  * **Papers**
+    * [[https://arxiv.org/pdf/2103.00453.pdf|Schick et al 2021 - Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP]]
+==== In Large Language Models ====
+    * [[https://arxiv.org/pdf/2311.04892.pdf|Gupta et al 2023 - Bias Runs Deep: Implicit Reasoning Biases in Persona-Assigned LLMs]]
 ==== Gender Bias ====
   * **Overviews**
     * [[https://arxiv.org/pdf/1906.08976.pdf|Sun et al 2019 - Mitigating Gender Bias in Natural Language Processing: Literature Review]]
-  * [[https://arxiv.org/pdf/1607.06520.pdf|Bolukbasi et al 2016 - Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings]]
+  * **Papers**
-  * [[https://arxiv.org/pdf/1707.09457.pdf|Zhao et al 2017 - Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints]]
+    * [[https://web.stanford.edu/class/linguist156/Lakoff_1973.pdf|Lakoff 1973 - Language and Woman's Place]] Linguistics paper from 1973 by [[https://en.wikipedia.org/wiki/Robin_Lakoff|Robin Lakoff]], often credited for making language and gender a major debate in linguistics.
-  * [[https://arxiv.org/pdf/1903.03862.pdf|Gonen & Goldberg 2019 - Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them]]
+    * [[https://arxiv.org/pdf/1607.06520.pdf|Bolukbasi et al 2016 - Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings]]
-  * [[https://arxiv.org/pdf/1904.03310.pdf|Zhao et al 2019 - Gender Bias in Contextualized Word Embeddings]]
+    * [[https://arxiv.org/pdf/1707.09457.pdf|Zhao et al 2017 - Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints]]
-  * [[https://www.aclweb.org/anthology/P19-1161v2.pdf|Zmigrod et al 2019 - Counterfactual Data Augmentation for Mitigating Gender Stereotypes in Languages with Rich Morphology]]
+    * [[https://arxiv.org/pdf/1903.03862.pdf|Gonen & Goldberg 2019 - Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them]]
-  * BUG dataset: [[https://arxiv.org/pdf/2109.03858.pdf|Levy et al 2021 - Collecting a Large-Scale Gender Bias Dataset for Coreference Resolution and Machine Translation]]
+    * [[https://arxiv.org/pdf/1904.03310.pdf|Zhao et al 2019 - Gender Bias in Contextualized Word Embeddings]]
-  * In Applications
+    * [[https://www.aclweb.org/anthology/P19-1161v2.pdf|Zmigrod et al 2019 - Counterfactual Data Augmentation for Mitigating Gender Stereotypes in Languages with Rich Morphology]]
+    * BUG dataset: [[https://arxiv.org/pdf/2109.03858.pdf|Levy et al 2021 - Collecting a Large-Scale Gender Bias Dataset for Coreference Resolution and Machine Translation]]
+    * [[https://aclanthology.org/2022.findings-acl.55.pdf|Gupta et al 2022 - Mitigating Gender Bias in Distilled Language Models via Counterfactual Role Reversal]]
+    * Data Augmentation
+      * [[https://aclanthology.org/P19-1161v2.pdf|Zmigrod et al 2019 - Counterfactual Data Augmentation for Mitigating Gender Stereotypes in Languages with Rich Morphology]]
+  * **In Applications**
     * Coreference Resolution
       * [[https://arxiv.org/pdf/1804.06876.pdf|Zhao et al 2018 - Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods]] Introduces **Winobias** coreference dataset. They use data-augmentation and word-embedding debiasing techniques, to remove bias in coref systems //without reducing performance on existing coref datasets//.
         * SoWinoBias: [[https://aclanthology.org/2021.gebnlp-1.12.pdf|Dawkins 2021 - Second Order WinoBias (SoWinoBias) Test Set for Latent Gender Bias Detection in Coreference Resolution]]
       * [[https://arxiv.org/pdf/1904.03310.pdf|Zhao et al 2019 - Gender Bias in Contextualized Word Embeddings]]
+    * Dialog
+      * [[https://arxiv.org/pdf/2005.00614.pdf|Dinan et al 2020 - Multi-Dimensional Gender Bias Classification]] [[https://huggingface.co/datasets/md_gender_bias|dataset]]
+    * Machine Translation
+  * **Datasets**
+    * MDGender: [[https://arxiv.org/pdf/2005.00614.pdf|Dinan et al 2020 - Multi-Dimensional Gender Bias Classification]] [[https://huggingface.co/datasets/md_gender_bias|dataset]]
+  * **Resources**
+    * Gendered word lists
+      * [[https://huggingface.co/datasets/md_gender_bias|MDGender]]
-===== Dataset Bias (Annotation Artifacts) =====
+===== Dataset Bias =====
-For an introduction, read [[https://arxiv.org/pdf/1803.02324.pdf|Gururangan 2018]] and [[https://arxiv.org/pdf/1808.05326.pdf|Zellers 2018]].
+Includes for example, annotation artifacts. For an introduction, read [[https://arxiv.org/pdf/1803.02324.pdf|Gururangan 2018]] and [[https://arxiv.org/pdf/1808.05326.pdf|Zellers 2018]].
   * [[https://arxiv.org/pdf/1805.01042.pdf|Poliak et al 2018 - Hypothesis Only Baselines in Natural Language Inference]]
   * [[https://arxiv.org/pdf/1803.02324.pdf|Gururangan et al 2018 - Annotation Artifacts in Natural Language Inference Data]]
+  * [[https://arxiv.org/pdf/1902.01007.pdf|McCoy et al 2019 - Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference]]
   * [[https://arxiv.org/pdf/1908.07898.pdf|Geva et al 2019 - Are We Modeling the Task or the Annotator? An Investigation of Annotator Bias in Natural Language Understanding Datasets]]
+  * [[https://arxiv.org/pdf/2204.12708|Schwartz & Stanovsky 2022 - On the Limitations of Dataset Balancing: The Lost Battle Against Spurious Correlations]]
 ==== Reducing Annotation Artifacts During Dataset Creation ====