Eliciting Reasoning in Large Language Models: A Comparative Study

In this article, we explore the challenges and approaches to identifying the speaker of a quote in literary texts. The task of speaker attribution is crucial for understanding the nuances of dialogue in literature, as it helps readers identify who is speaking and what their intentions are. However, this task can be difficult due to the complexity of language use in literary texts.
The article discusses several approaches to speaker attribution, including those based on named entity recognition, conference resolution, and BERT models. These methods face challenges such as error propagation from subtasks, which can undermine their performance. The authors highlight the importance of addressing these challenges to improve the accuracy of speaker attribution in literary texts.
To better understand the complexities of speaker attribution, the article uses analogies and metaphors to explain the process. For example, it compares the task of speaker attribution to solving a puzzle, where each piece represents a different speaker identity. The authors also highlight the importance of context in determining the speaker of a quote, comparing it to a game of hide-and-seek where the speaker’s identity must be inferred based on clues in the text.
The article concludes by emphasizing the need for further research and development in speaker attribution to improve our understanding of literary texts. By demystifying complex concepts and using engaging analogies, the authors provide a clear and concise summary of the current state of speaker attribution in literary texts.