When LLMs Read Tables Carelessly: Measuring and Reducing Data Referencing Errors

Researchers identified data referencing errors in large language models when processing tables. They proposed a method to mitigate these errors through critic-based filtering and rejection sampling, which improved answer accuracy by up to 12.0%.

1sec.ai