OffLanDat: Community-Based Implicit Offensive Language Dataset

核心概念

The author introduces OffLanDat, a dataset for implicit offensive language detection generated by ChatGPT. The approach involves prompt engineering to overcome ethical constraints and capture community-based offensive content.

摘要

Offensive language detection faces challenges with implicit content lacking specific keywords. OffLanDat addresses this by using ChatGPT and human annotation. The dataset includes diverse target groups and categories not found in existing datasets. Comparison between human and ChatGPT annotations shows similarities and differences in interpretation.

統計資料

OffLanDat consists of 8270 texts, with 6616 labeled as 'offensive' and 1654 as 'not offensive'.
BERT model achieved the highest Macro F1 score of 0.53 among the models.
Human annotators achieved an average Cohen’s Kappa score of 0.54 for consensus labels.

引述

"You’re pretty smart for a woman."
"People with speech issues should stay in the background; it’s less bothersome."

從以下內容提煉的關鍵洞見

OffLanDat

by Amit Das,Mos... 於 arxiv.org 03-06-2024

https://arxiv.org/pdf/2403.02472.pdf

深入探究

How can prompt engineering be further optimized to enhance the accuracy of detecting implicit offensive language?

Prompt engineering plays a crucial role in guiding large language models like ChatGPT to generate and detect implicit offensive language accurately. To optimize prompt engineering for enhanced accuracy in detecting implicit offensive language, several strategies can be implemented:

Tailored Prompts: Design prompts that are specific to the context of the target group or category being analyzed. By customizing prompts based on the characteristics of different groups, the model can better understand nuances and subtleties in potentially offensive content.

Incorporate Contextual Cues: Include contextual cues within prompts that provide additional information about the social, cultural, or historical background relevant to the target group. This helps the model grasp the implications of certain statements within a given context.

Iterative Prompt Refinement: Continuously refine and iterate on prompts based on feedback from human annotators and model performance evaluations. Adjusting prompts based on real-world data ensures they remain effective in capturing implicit forms of offensive language.

Multi-Modal Prompts: Experiment with incorporating multi-modal elements such as images or videos into prompts to provide richer context for understanding potentially offensive content beyond text-based cues.

Prompt Diversity: Use a diverse set of prompts that vary in structure, tone, and complexity to expose models to a wide range of linguistic patterns associated with implicit offensiveness across different target groups.

Human-in-the-Loop Validation: Integrate human validation at various stages of prompt development and testing to ensure that generated responses align with human perceptions of offensiveness accurately.

How can prompt engineering be further optimized to enhance the accuracy of detecting implicit offensive language?

Relying on large language models like ChatGPT for sensitive content moderation comes with several implications:

Ethical Considerations: Large language models have been criticized for perpetuating biases present in training data when used for sensitive tasks like hate speech detection.

Scalability Concerns: The computational resources required by these models may limit their scalability for real-time monitoring and moderation across vast amounts of user-generated content.

Interpretability Challenges: Understanding how these complex models arrive at decisions related to identifying explicit or implicit offenses can be challenging due to their black-box nature.

4..Resource Constraints: Implementing large-scale pre-trained models may pose financial challenges due to licensing costs or infrastructure requirements needed for deployment.

How can the inclusion of new categories and target groups in offensive language datasets impact bias detection algorithms?

The inclusion new categories target groups Offensive Language Datasets has significant implications bias detection algorithms:
1.-Enhanced Algorithm Performance:
By expanding dataset coverage include underrepresented marginalized communities ,bias detection algorithms become more robust accurate identifying discriminatory harmful content towards those populations
2.-Reduced Bias Amplification:
Including diverse categories mitigates risk reinforcing existing biases algorithmic decision-making process ensuring fair treatment individuals regardless backgrounds
3.-Improved Generalization:
Training bias detection algorithms broader spectrum categories enables them generalize effectively unseen data points extrapolate learnings previously encountered scenarios
4.-**Comprehensive Insights:
Incorporating new categories provides comprehensive insights into emerging forms online abuse discrimination allowing platforms take proactive measures combatting evolving threats
By considering including new categories target groups Offensive Language Datasets,bias detection algorithms strengthened refined promoting safer inclusive online environments users