KoSBI: A Dataset for Mitigating Social Bias Risks Towards Safer Large Language Model Applications

Abstract

Large language models (LLMs) learn not only natural text generation abilities but also social biases against different demographic groups from real-world data.This poses a critical risk when deploying LLM-based applications.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.This limitation requires localized social bias datasets to ensure the safe and effective deployment of LLMs.To this end, we present KOSBI, a new social bias dataset of 34k pairs of contexts and sentences in Korean covering 72 demographic groups in 15 categories.We find that through filtering-based moderation, social biases in generated content can be reduced by 16.47%p on average for HyperCLOVA (30B and 82B), and GPT-3.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 15 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.