WorldBench: A Dynamic and Flexible LLM Benchmark Composed of Per-Country Data from the World Bank
What are the two critical challenges LLMs face?
Large language models (LLMs) face two critical challenges: hallucination and performance disparities. Hallucination refers to LLMs generating plausible but inaccurate text, which is particularly risky in factual recall tasks. Performance disparities involve inconsistent reliability across different subsets of inputs, often linked to sensitive attributes like race, gender, or language. These issues emphasize the need for diverse benchmarks to evaluate LLM reliability and address potential fairness concerns.
How does hallucination impact LLMs' factual recall?
Hallucination in Large Language Models (LLMs) refers to the generation of plausible but inaccurate text4. This phenomenon impacts LLMs' factual recall by introducing false information in their responses. It poses risks in tasks that require factual accuracy, as the models may confidently provide incorrect answers. Addressing hallucination is crucial for improving the reliability and trustworthiness of LLMs in various applications.
What causes performance disparities in LLMs?
Performance disparities in LLMs can be caused by biases in the training data, which may not represent all groups equally. This can lead to varying levels of accuracy when answering questions about different parts of the world or different demographic groups. Additionally, LLMs may struggle with multicultural content and language nuances, further contributing to disparities.