We present an approach to improving non-English prompts based on […] ...
In 2024, reasoning have emerged as a new frontier for […] ...
We dissect the recently introduced Fast Feed-Forward (FFF) neural network […] ...
This report describes the setup and results of the shared task of human-like long story generation, the LSG Challenge, which asks to generate a consistent, human-like long story (a Harry Potter fanfic in English for a general audience) given a prompt of about 1,000 tokens. We evaluated the submissions using both automated metrics and human evaluation protocols. ...
We propose a shared task of human-like long story generation, LSG Challenge, that asks models to output a consistent human-like long story (a Harry Potter generic audience fanfic in English), given a prompt of about 1K tokens. We suggest a novel statistical metric of the text structuredness, GloVe Autocorrelations Power/ Exponential Law Mean Absolute Percentage Error Ratio (GAPELMAPER) and the use of previously-known UNION metric and a human evaluation protocol. We hope that LSG can open new avenues for researchers to investigate sampling approaches, prompting strategies, autoregressive and non-autoregressive text generation architectures and break the barrier to generate consistent long (40K+ word) texts. ...
This paper documents the approach of Team NTR for the Second Shared Task on Automatic Minuting (AutoMin) at INLG 2023. The goal of this work is to develop a module for automatic generation of meeting minutes based on a meeting transcript text produced by an Automated Speech Recognition (ASR) system (Task A). We consider minuting as a supervised machine learning task on pairs of texts: the transcript of the meeting and its minutes. We use a two-staged minuting pipeline that consists of segmentation and summarization. We experiment with semantic segmentation and multi-language approaches and Large Language Model Dolly, and achieve Rouge1-F of 0.2455 and BERT-Score of 0.8063 on the English part of ELITR test set and Rouge1-F of 0.2430 and BERT-Score of 0.8332 on the EuroParl dev set with the submitted Naive Segmentation + Dolly7b pipeline. ...