A framework for evaluating LLM on iterative game content refinement read more of LLMAKER - Consistent Game Content Creation via LLMs

MAD-TSC is a multilingual aligned dataset for target-dependent sentiment classification (TSC) on the news domain. read more of MAD-TSC: A Multilingual Aligned News Dataset for Target-dependent Sentiment Classification

JECT-CLONE is an AI-powered service that generates personalized morning news briefings for journalists and editors. It applies computational creativity algorithms to JECT.AI's semantic news index of over 25 million articles to recommend novel and relevant... read more of JECT-CLONE: AI-Generated Daily Emails

An open-source LLaMa2 language model of 7b parameters fine-tuned (using as base model NousResearch/Nous-Hermes-llama-2-7b) to follow instructions in italian. read more of LLM fine-tuned to follow instructions in Italian

This model analyses the input text and provides an answer whether in the text there is a change of topic or not (resp. TOPPICCHANGE, SAMETOPIC). read more of Topic change detector in Italian news content

The aim of the LeQua 2022 dataset is to allow the comparative evaluation of methods for “learning to quantify” in textual datasets, i.e., methods for training predictors of the relative frequencies of the classes of interest in sets of unlabelled textual ... read more of Learning to quantify: LeQua 2022 datasets