Automated Reading Passage Generation with OpenAI's Large Language Model
- URL: http://arxiv.org/abs/2304.04616v1
- Date: Mon, 10 Apr 2023 14:30:39 GMT
- Title: Automated Reading Passage Generation with OpenAI's Large Language Model
- Authors: Ummugul Bezirhan, Matthias von Davier
- Abstract summary: This paper utilizes OpenAI's latest transformer-based language model, GPT-3, to generate reading passages.
Existing reading passages were used in carefully engineered prompts to ensure the AI-generated text has similar content and structure to a fourth-grade reading passage.
All AI-generated passages, along with original passages were evaluated by human judges according to their coherence, appropriateness to fourth graders, and readability.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The widespread usage of computer-based assessments and individualized
learning platforms has resulted in an increased demand for the rapid production
of high-quality items. Automated item generation (AIG), the process of using
item models to generate new items with the help of computer technology, was
proposed to reduce reliance on human subject experts at each step of the
process. AIG has been used in test development for some time. Still, the use of
machine learning algorithms has introduced the potential to improve the
efficiency and effectiveness of the process greatly. The approach presented in
this paper utilizes OpenAI's latest transformer-based language model, GPT-3, to
generate reading passages. Existing reading passages were used in carefully
engineered prompts to ensure the AI-generated text has similar content and
structure to a fourth-grade reading passage. For each prompt, we generated
multiple passages, the final passage was selected according to the Lexile score
agreement with the original passage. In the final round, the selected passage
went through a simple revision by a human editor to ensure the text was free of
any grammatical and factual errors. All AI-generated passages, along with
original passages were evaluated by human judges according to their coherence,
appropriateness to fourth graders, and readability.
Related papers
- DeTeCtive: Detecting AI-generated Text via Multi-Level Contrastive Learning [24.99797253885887]
We argue that the key to accomplishing this task lies in distinguishing writing styles of different authors.
We propose DeTeCtive, a multi-task auxiliary, multi-level contrastive learning framework.
Our method is compatible with a range of text encoders.
arXiv Detail & Related papers (2024-10-28T12:34:49Z) - Detecting Machine-Generated Long-Form Content with Latent-Space Variables [54.07946647012579]
Existing zero-shot detectors primarily focus on token-level distributions, which are vulnerable to real-world domain shifts.
We propose a more robust method that incorporates abstract elements, such as event transitions, as key deciding factors to detect machine versus human texts.
arXiv Detail & Related papers (2024-10-04T18:42:09Z) - Is Contrasting All You Need? Contrastive Learning for the Detection and Attribution of AI-generated Text [4.902089836908786]
WhosAI is a triplet-network contrastive learning framework designed to predict whether a given input text has been generated by humans or AI.
We show that our proposed framework achieves outstanding results in both the Turing Test and Authorship tasks.
arXiv Detail & Related papers (2024-07-12T15:44:56Z) - Enhancing Text Authenticity: A Novel Hybrid Approach for AI-Generated Text Detection [8.149808049643344]
We propose a novel hybrid approach that combines TF-IDF techniques with advanced machine learning models.
Our approach achieves superior performance compared to existing methods.
arXiv Detail & Related papers (2024-06-01T10:21:54Z) - Beyond Turing: A Comparative Analysis of Approaches for Detecting Machine-Generated Text [1.919654267936118]
Traditional shallow learning, Language Model (LM) fine-tuning, and Multilingual Model fine-tuning are evaluated.
Results reveal considerable differences in performance across methods.
This study paves the way for future research aimed at creating robust and highly discriminative models.
arXiv Detail & Related papers (2023-11-21T06:23:38Z) - TEMPERA: Test-Time Prompting via Reinforcement Learning [57.48657629588436]
We propose Test-time Prompt Editing using Reinforcement learning (TEMPERA)
In contrast to prior prompt generation methods, TEMPERA can efficiently leverage prior knowledge.
Our method achieves 5.33x on average improvement in sample efficiency when compared to the traditional fine-tuning methods.
arXiv Detail & Related papers (2022-11-21T22:38:20Z) - Contextual-Utterance Training for Automatic Speech Recognition [65.4571135368178]
We propose a contextual-utterance training technique which makes use of the previous and future contextual utterances.
Also, we propose a dual-mode contextual-utterance training technique for streaming automatic speech recognition (ASR) systems.
The proposed technique is able to reduce both the WER and the average last token emission latency by more than 6% and 40ms relative.
arXiv Detail & Related papers (2022-10-27T08:10:44Z) - Toward Educator-focused Automated Scoring Systems for Reading and
Writing [0.0]
This paper addresses the challenges of data and label availability, authentic and extended writing, domain scoring, prompt and source variety, and transfer learning.
It employs techniques that preserve essay length as an important feature without increasing model training costs.
arXiv Detail & Related papers (2021-12-22T15:44:30Z) - Lexically Aware Semi-Supervised Learning for OCR Post-Correction [90.54336622024299]
Much of the existing linguistic data in many languages of the world is locked away in non-digitized books and documents.
Previous work has demonstrated the utility of neural post-correction methods on recognition of less-well-resourced languages.
We present a semi-supervised learning method that makes it possible to utilize raw images to improve performance.
arXiv Detail & Related papers (2021-11-04T04:39:02Z) - Hierarchical Bi-Directional Self-Attention Networks for Paper Review
Rating Recommendation [81.55533657694016]
We propose a Hierarchical bi-directional self-attention Network framework (HabNet) for paper review rating prediction and recommendation.
Specifically, we leverage the hierarchical structure of the paper reviews with three levels of encoders: sentence encoder (level one), intra-review encoder (level two) and inter-review encoder (level three)
We are able to identify useful predictors to make the final acceptance decision, as well as to help discover the inconsistency between numerical review ratings and text sentiment conveyed by reviewers.
arXiv Detail & Related papers (2020-11-02T08:07:50Z) - Exploring the Limits of Transfer Learning with a Unified Text-to-Text
Transformer [64.22926988297685]
Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP)
In this paper, we explore the landscape of introducing transfer learning techniques for NLP by a unified framework that converts all text-based language problems into a text-to-text format.
arXiv Detail & Related papers (2019-10-23T17:37:36Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.