# Investigating Societal Biases in a Poetry Composition System

arXiv: https://arxiv.org/abs/2011.02686 · HTML: https://mlsanity.com/paper/2011.02686/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Nov 2020.

## Built on by (most cited first, 30 of 48)

- [Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?](https://mlsanity.com/paper/2202.12837/) (2022)
- [MetaICL: Learning to Learn In Context](https://mlsanity.com/paper/2110.15943/) (2021)
- [Societal Biases in Language Generation: Progress and Challenges](https://mlsanity.com/paper/2105.04054/) (2021)
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- [Mitigating Label Biases for In-context Learning](https://mlsanity.com/paper/2305.19148/) (2023)
- [Overthinking the Truth: Understanding how Language Models Process False Demonstrations](https://mlsanity.com/paper/2307.09476/) (2023)
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- [Dual Operating Modes of In-Context Learning](https://mlsanity.com/paper/2402.18819/) (2024)
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- [Stress-Testing Long-Context Language Models with Lifelong ICL and Task Haystack](https://mlsanity.com/paper/2407.16695/) (2024)
- [Prompting with Pseudo-Code Instructions](https://mlsanity.com/paper/2305.11790/) (2023)
- [Misconfidence-based Demonstration Selection for LLM In-Context Learning](https://mlsanity.com/paper/2401.06301/) (2024)
- [Eliciting and Understanding Cross-Task Skills with Task-Level Mixture-of-Experts](https://mlsanity.com/paper/2205.12701/) (2022)
- [Learning to Initialize: Can Meta Learning Improve Cross-task Generalization in Prompt Tuning?](https://mlsanity.com/paper/2302.08143/) (2023)
- [Massively Multilingual Corpus of Sentiment Datasets and Multi-faceted Sentiment Classification Benchmark](https://mlsanity.com/paper/2306.07902/) (2023)
- [This Prompt is Measuring <MASK>: Evaluating Bias Evaluation in Language Models](https://mlsanity.com/paper/2305.12757/) (2023)
- [Gen-Z: Generative Zero-Shot Text Classification with Contextualized Label Descriptions](https://mlsanity.com/paper/2311.07115/) (2023)
- [Rectifying Demonstration Shortcut in In-Context Learning](https://mlsanity.com/paper/2403.09488/) (2024)
- [Token-based Decision Criteria Are Suboptimal in In-context Learning](https://mlsanity.com/paper/2406.16535/) (2024)
- [Few-shot Adaptation Works with UnpredicTable Data](https://mlsanity.com/paper/2208.01009/) (2022)
- [LEAP: Efficient and Automated Test Method for NLP Software](https://mlsanity.com/paper/2308.11284/) (2023)
- [Investigating the Pre-Training Dynamics of In-Context Learning: Task Recognition vs. Task Learning](https://mlsanity.com/paper/2406.14022/) (2024)

## 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.