Unveiling Implicit Toxicity in Life Advice: The LifeTox Dataset

Core Concepts

LifeTox introduces a dataset for identifying implicit toxicity in advice-seeking scenarios, showcasing its efficacy in addressing complex challenges.

Abstract

LifeTox dataset designed to detect implicit toxicity in advice-seeking contexts. Utilizes open-ended questions from Reddit forums for diverse personal experiences. RoBERTa fine-tuned on LifeTox shows strong performance in toxicity classification tasks. Comparison with large language models demonstrates the effectiveness of LifeTox. Human evaluation confirms the reliability and accuracy of the dataset. Training LLMs on LifeTox enhances generalizability and performance.

Stats

LifeTox comprises di- verse contexts derived from personal experi- ences through open-ended questions. RoBERTa fine-tuned on LifeTox matches or surpasses the zero-shot performance of large language models in tox- icity classification tasks.

Quotes

"To bridge this gap, we introduce LifeTox, a dataset of 87,510 real-life scenarios and respective advice crawled from two twin subreddit forums." "LifeTox distinctively stands out from previous safety benchmarks with its unique features."

Key Insights Distilled From

LifeTox

by Minbeom Kim,... at arxiv.org 03-20-2024

https://arxiv.org/pdf/2311.09585.pdf

Deeper Inquiries

How can implicit toxicity detection be applied beyond advice-seeking scenarios?

Implicit toxicity detection can be applied beyond advice-seeking scenarios in various contexts where language models are utilized, such as social media platforms, customer service interactions, educational settings, and content moderation. By training models on datasets like LifeTox that focus on understanding the underlying intent and societal impact of language rather than just explicit profanity use, these models can better identify harmful behaviors or messages that may not be overtly toxic. This approach enables a more nuanced understanding of potential risks in communication across different domains.

What are potential limitations or biases that could arise from using the LifeTox dataset?

One potential limitation of using the LifeTox dataset is the inherent bias present in the data collected from specific Reddit forums (LifeProTips and UnethicalLifeProTips). The advice shared on these platforms may not represent a diverse range of perspectives or cultural backgrounds, leading to a skewed view of what constitutes implicit toxicity. Additionally, there may be subjective judgments made by moderators when labeling comments as safe or unsafe, introducing human bias into the dataset. These factors could result in an incomplete representation of real-world implicit toxicity scenarios.

How might training large language models on datasets like LifeTox impact their societal implications?

Training large language models on datasets like LifeTox can have significant implications for society. By improving these models' ability to detect implicit toxicity accurately, they can contribute to creating safer online environments by flagging potentially harmful content before it spreads widely. However, there is also a risk that biased training data or flawed algorithms could inadvertently reinforce existing prejudices or stereotypes present in the dataset. Therefore, careful consideration must be given to how these models are trained and deployed to mitigate any negative societal impacts they may have.