Abhinav Havaldar

When RAG Fails to Equalize: Geo-bias in Factual Question Answering over Public Companies

Retrieval-augmented generation (RAG) is widely assumed to mitigate factual errors in large language models (LLMs), but it remains unclear whether retrieval uniformly compensates for missing knowledge. We study this question in a controlled factual QA setting over public companies, constructing a benchmark of approximat...