Papers with Books
Letting Emotions Flow: Success Prediction by Modeling the Flow of Emotions in Books (N18-2)
Copied to clipboard
| Challenge: | We obtained the best weighted F1-score of 69% for predicting books’ success in a multitask setting. |
| Approach: | They propose to model the flow of emotions over a book using recurrent neural networks and quantify its usefulness in predicting success in books. |
| Outcome: | The proposed model obtained the best weighted F1-score of 69% for predicting books’ success in a multitask setting. |
Deciphering the Impact of Pretraining Data on Large Language Models through Machine Unlearning (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing studies have suggested that the composition of the pretraining corpus exerts a significant impact upon the performance of LLMs. |
| Approach: | They analyze the impact of 48 datasets from 5 major categories of pretraining data of Large Language Models and measure their impacts on LLMs using benchmarks about nine major categories. |
| Outcome: | The proposed analysis provides insights into the organization of data to support more efficient pretraining of Large Language Models. |
Chapter Captor: Text Segmentation in Novels (2020.emnlp-main)
Copied to clipboard
| Challenge: | Using a hybrid approach, we identify chapter boundaries in novels . chapter boundaries are typically denoted by formatting conventions such as page breaks, white-space, chapter numbers, and titles. |
| Approach: | They build a project Gutenberg data set of 9,126 English novels to analyze chapter boundaries . they use neural inference and rule matching to recognize chapter title headers . |
| Outcome: | The proposed method achieves an F1 score of 0.77 on the segmentation task . the annotated data reveal interesting historical trends in the chapter structure of novels . |