Exploring Inherent Biases in LLMs within Korean Social Context: A Comparative Analysis of ChatGPT and GPT-4 (2024.naacl-srw)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have been criticized for perpetuating stereotypes against diverse groups based on race, sexual orientation, and other attributes. |
| Approach: | They devised a set of prompts that reflect major societal issues in Korea and assign varied personas to both ChatGPT and GPT-4 to assess the toxicity of the generated sentences. |
| Outcome: | The proposed model produces twice the level of toxic content as ChatGPT and GPT-4 under certain conditions. |
Similar Papers
Quite Good, but Not Enough: Nationality Bias in Large Language Models - a Case Study of ChatGPT (2024.lrec-main)
Copied to clipboard
| Challenge: | Nationality is a key demographic element that enhances the performance of large language models, but it has received less scrutiny regarding inherent biases. |
| Approach: | They investigated nationality bias in ChatGPT, a large language model for text generation. |
| Outcome: | The proposed model generates 4,680 discourses about nationality in Chinese and English, with 195 countries, 4 temperature settings, and 3 prompt types. |
Toxicity in chatgpt: Analyzing persona-assigned language models (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Large language models (LLMs) have shown incredible capabilities and transcended the natural language processing community. |
| Approach: | They evaluate toxicity in over half a million generations of ChatGPT by assigning it a persona . they find that outputs engage in incorrect stereotypes, harmful dialogue, hurtful opinions . |
| Outcome: | a new study shows that assigning a persona to a chatbot can increase toxicity in half a million generations. |
Uncovering Stereotypes in Large Language Models: A Task Complexity-based Approach (2024.eacl-long)
Copied to clipboard
| Challenge: | Recent Large Language Models (LLMs) have unlocked unprecedented applications of AI. |
| Approach: | They propose to use a social benchmark to evaluate the bias protection provided by Large Language Models (LLMs) with a variety of tasks with varying complexities to assess their effectiveness. |
| Outcome: | The proposed benchmark shows that both ChatGPT and GPT-4 have strong biases with respect to nationality, gender, race, and religion. |
KoSBI: A Dataset for Mitigating Social Bias Risks Towards Safer Large Language Model Applications (2023.acl-industry)
Copied to clipboard
| Challenge: | Existing research and resources are not readily applicable in South Korea due to the differences in language and culture, both of which significantly affect the biases and targeted demographic groups. |
| Approach: | They propose a social bias dataset of 34k pairs of contexts and sentences in Korean covering 72 demographic groups in 15 categories. |
| Outcome: | The proposed dataset reduces social biases by 16.47%p on average for HyperClova (30B and 82B), and GPT-3. |
Linguistic Bias in ChatGPT: Language Models Reinforce Dialect Discrimination (2024.emnlp-main)
Copied to clipboard
| Challenge: | a large-scale study of linguistic bias exhibited by ChatGPT covers 10 dialects of English . standard varieties of English, especially SAE, dominate available training data . |
| Approach: | They use ChatGPT to generate models that default to "standard" varieties of English . they also use a feature annotation and native speaker evaluation to analyze the responses . |
| Outcome: | The proposed models default to "standard" varieties of English, but non-"standard" ones exhibit stereotyping, demeaning content, lack of comprehension, condescending responses. |
TWBias: A Benchmark for Assessing Social Bias in Traditional Chinese Large Language Models through a Taiwan Cultural Lens (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Large language models have shown remarkable capabilities in natural language processing, but concerns about social bias amplification remain. |
| Approach: | They propose a social bias evaluation benchmark for Traditional Chinese LLMs that integrates chat templates and diverse prompts for comprehensive bias assessment. |
| Outcome: | The proposed model incorporates chat templates and diverse prompts for comprehensive bias assessment focusing on Taiwan's cultural context and prioritizing gender and ethnicity bias evaluation. |
ROBBIE: Robust Bias Evaluation of Large Generative Language Models (2023.emnlp-main)
Copied to clipboard
David Esiobu, Xiaoqing Tan, Saghar Hosseini, Megan Ung, Yuchen Zhang, Jude Fernandes, Jane Dwivedi-Yu, Eleonora Presani, Adina Williams, Eric Smith
| Challenge: | generative large language models (LLMs) are becoming more performant and prevalent . we need tools to measure and improve their fairness, authors say . |
| Approach: | They propose to compare 6 different prompt-based bias and toxicity metrics across 12 demographic axes and 5 families of generative large language models. |
| Outcome: | The proposed model can be tested on more datasets to better characterize and mitigate biases . the study compared 6 prompt-based bias and toxicity metrics across 12 demographic axes and 5 families of generative large language models. |
An Empirical Analysis on Large Language Models in Debate Evaluation (2024.acl-short)
Copied to clipboard
| Challenge: | Prior research in automatic debate evaluation relied on pre-trained encoders and the modeling of argument relations and structures. |
| Approach: | They investigate the capabilities and inherent biases of advanced large language models (LLMs) such as GPT-3.5 and GPT-4 in the context of debate evaluation. |
| Outcome: | The proposed models outperform state-of-the-art methods on extensive datasets and show that they are more accurate than previous models. |
Not All Countries Celebrate Thanksgiving: On the Cultural Dominance in Large Language Models (2024.acl-long)
Copied to clipboard
| Challenge: | e.g., ChatGPT often provides inappropriate English-culture-related answers when users ask in non-English languages. |
| Approach: | They build a benchmark of concrete and abstract cultural objects to evaluate the cultural dominance issue in large language models. |
| Outcome: | The proposed model can significantly mitigate cultural dominance issue in large language models . the model can provide accurate answers in English, while the model is ethically sound . |
Understanding Large Language Model Vulnerabilities to Social Bias Attacks (2025.acl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have demonstrated remarkable linguistic capabilities across tasks . however, there is a growing concern about their potential to perpetuate social biases . |
| Approach: | They evaluate LLMs across gender, racial, and religious bias types . they also explore cross-bias and multiple-biases attacks . |
| Outcome: | The proposed models are more susceptible to gender bias attacks than racial or religious biases. |