Large language models (LLMs) are being used more frequently as conversational systems, leading to increased reliance on them for answers. To understand how these models respond to questions about ongoing debates, we need datasets with human-annotated labels reflecting contemporary discussions. To address this, we propose a new way of creating a dataset for controversial questions.
Introducing DELPHI: Data for Evaluating LLMs’ Performance in Handling Controversial Issues
Controversy is an important part of any discussion, and with the increasing reliance on large language models (LLMs) as conversational systems, it is crucial to understand how these models respond to questions related to ongoing debates. However, there is a lack of datasets that reflect contemporary discussions. To address this, we have developed DELPHI, a novel construction of a controversial questions dataset.
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If you want to stay competitive and take advantage of AI, consider using DELPHI. It can help you:
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