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Investigating Societal Biases in a Poetry Composition System

arXiv 2011.02686 ↗First posted 5 Nov 2020cs.CLcited by 47

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Abstract (arXiv)

There is a growing collection of work analyzing and mitigating societal biases in language understanding, generation, and retrieval tasks, though examining biases in creative tasks remains underexplored. Creative language applications are meant for direct interaction with users, so it is important to quantify and mitigate societal biases in these applications. We introduce a novel study on a pipeline to mitigate societal biases when retrieving next verse suggestions in a poetry composition system. Our results suggest that data augmentation through sentiment style transfer has potential for mitigating societal biases.

Key facts. Cited by 47 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Nov 2020.

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Citing papers per year: 2021: 4; 2022: 8; 2023: 17; 2024: 11; 2025: 4; 2026: 3.

47 papers.

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  1. 2022Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?cited by 1,413
  2. 2021MetaICL: Learning to Learn In Contextcited by 450
  3. 2021Societal Biases in Language Generation: Progress and Challengescited by 201
  4. 2023What In-Context Learning "Learns" In-Context: Disentangling Task Recognition and Task Learningcited by 149
  5. 2021CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLPcited by 142
  6. 2022Ground-Truth Labels Matter: A Deeper Look into Input-Label Demonstrationscited by 116
  7. 2023Mitigating Label Biases for In-context Learningcited by 88
  8. 2023Overthinking the Truth: Understanding how Language Models Process False Demonstrationscited by 78
  9. 2023Knowledge is a Region in Weight Space for Fine-tuned Language Modelscited by 58
  10. 2023Active Learning Principles for In-Context Learning with Large Language Modelscited by 53
  11. 2022ColD Fusion: Collaborative Descent for Distributed Multitask Finetuningcited by 52
  12. 2023Fusing Models with Complementary Expertisecited by 45
35 more
  1. 2024Dual Operating Modes of In-Context Learningcited by 42
  2. 2024SAPT: A Shared Attention Framework for Parameter-Efficient Continual Learning of Large Language Modelscited by 37
  3. 2023Split-and-Denoise: Protect large language model inference with local differential privacycited by 30
  4. 2023Interpretable-by-Design Text Understanding with Iteratively Generated Concept Bottleneckcited by 21
  5. 2024Stress-Testing Long-Context Language Models with Lifelong ICL and Task Haystackcited by 19
  6. 2024Misconfidence-based Demonstration Selection for LLM In-Context Learningcited by 14
  7. 2023Prompting with Pseudo-Code Instructionscited by 13
  8. 2022Eliciting and Understanding Cross-Task Skills with Task-Level Mixture-of-Expertscited by 12
  9. 2023Learning to Initialize: Can Meta Learning Improve Cross-task Generalization in Prompt Tuning?cited by 11
  10. 2023Massively Multilingual Corpus of Sentiment Datasets and Multi-faceted Sentiment Classification Benchmarkcited by 10
  11. 2023This Prompt is Measuring <MASK>: Evaluating Bias Evaluation in Language Modelscited by 9
  12. 2024Token-based Decision Criteria Are Suboptimal in In-context Learningcited by 9
  13. 2023Gen-Z: Generative Zero-Shot Text Classification with Contextualized Label Descriptionscited by 7
  14. 2024Rectifying Demonstration Shortcut in In-Context Learningcited by 7
  15. 2022Few-shot Adaptation Works with UnpredicTable Datacited by 6
  16. 2023LEAP: Efficient and Automated Test Method for NLP Softwarecited by 6
  17. 2024Investigating the Pre-Training Dynamics of In-Context Learning: Task Recognition vs. Task Learningcited by 6
  18. 2021Augmenting Poetry Composition with Verse by Versecited by 5
  19. 2023Beyond Output Matching: Bidirectional Alignment for Enhanced In-Context Learningcited by 5
  20. 2024NoisyICL: A Little Noise in Model Parameters Calibrates In-context Learningcited by 4
  21. 2024Improving In-Context Learning with Prediction Feedback for Sentiment Analysiscited by 4
  22. 2024Zero-to-Strong Generalization: Eliciting Strong Capabilities of Large Language Models Iteratively without Gold Labelscited by 4
  23. 2025Corrective In-Context Learning: Evaluating Self-Correction in Large Language Modelscited by 4
  24. 2022Assessment of Massively Multilingual Sentiment Classifierscited by 3
  25. 2023RoAST: Robustifying Language Models via Adversarial Perturbation with Selective Trainingcited by 3
  26. 2023Aesthetics of Sanskrit Poetry from the Perspective of Computational Linguistics: A Case Study Analysis on Siksastakacited by 2
  27. 2024Less is KEN: a Universal and Simple Non-Parametric Pruning Algorithm for Large Language Modelscited by 2
  28. 2022KnowDA: All-in-One Knowledge Mixture Model for Data Augmentation in Low-Resource NLPcited by 1
  29. 2025IA2: Alignment with ICL Activations Improves Supervised Fine-Tuningcited by 1
  30. 2026Improving LLM Final Representations with Inter-Layer Geometrycited by 1
  31. 2022Dynamic Latent Separation for Deep Learningcited by 0
  32. 2025Uncertainty-driven Embedding Convolutioncited by 0
  33. 2025The Impact of Role Design in In-Context Learning for Large Language Modelscited by 0
  34. 2026MEGnifying Emotion: Sentiment Analysis from Annotated Brain Datacited by 0
  35. 2026A Hybrid Framework for Song Lyric Annotation Based on Human-LLM Alignmentcited by 0