Summary of Efficient Models For the Detection Of Hate, Abuse and Profanity, by Christoph Tillmann et al.
Efficient Models for the Detection of Hate, Abuse and Profanity
by Christoph Tillmann, Aashka Trivedi, Bishwaranjan Bhattacharjee
First submitted to arxiv on: 8 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers investigate the impact of Hate, Abuse, and Profanity (HAP) content on Large Language Models (LLMs). LLMs are used for various NLP tasks like sentiment analysis, document classification, and summarization. However, their training data often contains HAP content, which can cause models to learn hateful or profane language. For instance, the open-source RoBERTa model from the HuggingFace Transformers library returns the word “stupid” when prompted to replace a mask token in a sentence. The paper highlights the importance of creating civil and unbiased LLMs for all languages, not just English. To achieve this, researchers develop HAP detectors and explore ways to utilize them to generate acceptable output. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how language models can learn bad things from the internet. These models are used to do things like understand how people feel or summarize text. But sometimes, they learn words that are hurtful or mean-spirited because of the data they’re trained on. Researchers want to make sure these models don’t produce harmful content. They’re working on ways to detect this kind of language and prevent it from being generated. |
Keywords
» Artificial intelligence » Classification » Mask » Nlp » Summarization » Token
