LLM-DetectAIve: a Tool for Fine-Grained Machine-Generated Text Detection

The widespread accessibility of large language models (LLMs) to the general public has significantly amplified the dissemination of machine-generated texts (MGTs). Advancements in prompt manipulation have exacerbated the difficulty in discerning the origin of a text (human-authored vs machinegenerated). This raises concerns regarding the potential misuse of MGTs, particularly within educational and academic domains. In this paper, we present $\textbf{LLM-DetectAIve}$ -- a system designed for fine-grained MGT detection. It is able to classify texts into four categories: human-written, machine-generated, machine-written machine-humanized, and human-written machine-polished. Contrary to previous MGT detectors that perform binary classification, introducing two additional categories in LLM-DetectiAIve offers insights into the varying degrees of LLM intervention during the text creation. This might be useful in some domains like education, where any LLM intervention is usually prohibited. Experiments show that LLM-DetectAIve can effectively identify the authorship of textual content, proving its usefulness in enhancing integrity in education, academia, and other domains. LLM-DetectAIve is publicly accessible at https://huggingface.co/spaces/raj-tomar001/MGT-New. The video describing our system is available at https://youtu.be/E8eT_bE7k8c.

DeltaScore: Evaluating Story Generation with Differentiating Perturbations

Various evaluation metrics exist for natural language generation tasks, but they have limited utility for story generation since they generally do not correlate well with human judgments and do not measure fine-grained story aspects, such as fluency versus relatedness, as they are intended to assess overall generation quality. In this paper, we propose deltascore, an approach that utilizes perturbation to evaluate fine-grained story aspects. Our core idea is based on the hypothesis that the better the story performs in a specific aspect (e.g., fluency), the more it will be affected by a particular perturbation (e.g., introducing typos). To measure the impact, we calculate the likelihood difference between the pre- and post-perturbation stories using a language model. We evaluate deltascore against state-of-the-art model-based and traditional similarity-based metrics across multiple story domains, and investigate its correlation with human judgments on five fine-grained story aspects: fluency, coherence, relatedness, logicality, and interestingness. Our results demonstrate that deltascore performs impressively in evaluating fine-grained story aspects, and we discovered a striking outcome where a specific perturbation appears to be highly effective in measuring most aspects.

Can Very Large Pretrained Language Models Learn Storytelling With A Few Examples?

While pre-trained language models can generate individually fluent sentences for automatic story generation, they struggle to generate stories that are coherent, sensible and interesting. Current state-of-the-art (SOTA) story generation models explore using higher-level features such as plots or commonsense knowledge to improve the quality of generated stories. Prompt-based learning using very large pre-trained language models (VLPLMs) such as GPT3 has demonstrated impressive performance even across various NLP tasks. In this paper, we present an extensive study using automatic and human evaluation to compare the story generation capability of VLPLMs to those SOTA models in three different datasets where stories differ in style, register and length. Our results show that VLPLMs generate much higher quality stories than other story generation models, and to a certain extent rival human authors, although preliminary investigation also reveals that they tend to ``plagiarise'' real stories in scenarios that involve world knowledge.

Exploring Story Generation with Multi-task Objectives in Variational Autoencoders

GPT-2 has been frequently adapted in story generation models as it provides powerful generative capability. However, it still fails to generate consistent stories and lacks diversity. Current story generation models leverage additional information such as plots or commonsense into GPT-2 to guide the generation process. These approaches focus on improving generation quality of stories while our work look at both quality and diversity. We explore combining BERT and GPT-2 to build a variational autoencoder (VAE), and extend it by adding additional objectives to learn global features such as story topic and discourse relations. Our evaluations show our enhanced VAE can provide better quality and diversity trade off, generate less repetitive story content and learn a more informative latent variable.