Analyzing Toxicity in OpenAI WebText and OPENWEBTEXT CORPUS: Impact of Social Positions, Biases, and Toxic Text Output

– Paper analyzes toxicity in OpenAI WebText and OPENWEBTEXT CORPUS.
– Finds toxic language in the analyzed data.
– Authors highlight the disproportionate effect of powerful social positions on language style in LLM training data.
– Privileged groups like men, white populations, higher socioeconomic status, and American/Western European perspectives are favored.
– Mentions the ETHOS dataset for online hate speech detection.
– Mentions the CrowS-pairs dataset for measuring social biases in masked language models.

– The paper mentions OpenAI WebText and OPENWEBTEXT CORPUS as data sources for analysis.
– The paper discusses the potential for biased, hegemonic, and toxic text output from large language models.
– The paper raises the question of whether larger language models are necessary.

– Solo Performance Prompting (SPP) enhances problem-solving abilities in complex tasks.
– SPP reduces factual hallucination and maintains strong reasoning capabilities.
– Cognitive synergy emerges in GPT-4 but not in less capable models.

– Analysis of toxicity in OpenAI WebText and OPENWEBTEXT CORPUS.
– Toxic language found in the data.
– Authors from powerful social positions have disproportionate influence on language style.
– Privileged groups favored in LLM training data.
– Neural toxic degeneration causes toxicity in generated text.

– Toxic language is found in the OpenAI WebText and OPENWEBTEXT CORPUS.
– Authors from powerful social positions have a disproportionate effect on language style in LLM training data.
– Larger language models have the potential for biased, hegemonic, and toxic text output.
– Performance disparities exist in language models for different demographic groups.
– Social bias and stereotypes are present in language models’ predictions.

– 327 prompts yield at least one generation with 0.9 toxicity from all models.
– 1225 prompts yield at least one generation with 0.9 toxicity from out of the box models.

– Solo Performance Prompting (SPP) helps a computer program think like different people.
– It uses different personas to solve problems and get accurate knowledge.
– SPP reduces mistakes and makes better plans compared to other methods.
– It works well in tasks like writing stories and solving puzzles.
– SPP is better in GPT-4 model compared to other models.