Examining Controversial Questions: A Dataset for Language Models
The Significance of Controversy in Conversational Systems
In today’s digital era, controversies play a vital role in shaping our society. With the increasing reliance on large language models (LLMs) as conversational systems, it becomes crucial to understand how these models respond to questions surrounding ongoing debates. However, the availability of datasets reflecting contemporary discussions is limited. Therefore, to encourage research in this area, we have developed a unique controversial questions dataset by expanding the well-known Quora Question Pairs Dataset. This dataset poses several challenges related to knowledge recency, safety, fairness, and bias. By evaluating different LLMs using a subset of this dataset, we aim to shed light on how these models handle controversial issues and the positions they take. Ultimately, this research aims to enhance the comprehension and management of complex societal debates by LLMs.
Introducing the Controversial Questions Dataset
Our controversial questions dataset is an extension of the publicly available Quora Question Pairs Dataset. We have carefully curated this dataset to reflect the contemporary discussions surrounding controversial topics. By utilizing this dataset, researchers have the opportunity to analyze and explore the interaction between LLMs and controversial subjects. The dataset raises important questions about the recency of knowledge, ensuring user safety, fairness, and potential biases in the responses provided by these language models.
Evaluating Language Models and Their Handling of Controversial Issues
In order to understand how LLMs respond to controversial questions, we have assessed various language models using a subset of the controversial questions dataset. Our evaluation sheds light on how these models handle contentious topics and the perspectives they adopt. The findings from this study will not only contribute to our knowledge of LLMs’ interaction with controversial issues but also pave the way for improvements in their comprehension and management of complex societal debates.
By examining the responses of LLMs to controversial questions, we can gain insights into the strengths and limitations of these models in handling nuanced discussions. This research opens doors for further advancements in conversational systems, ensuring they are better equipped to provide accurate and fair responses to users seeking answers to contentious topics. Improved comprehension and handling of controversial questions will lead to more informed conversations and foster a deeper understanding of our society.