Generate, Annotate, and Learn: NLP with Synthetic Text

He, Xuanli; Nassar, Islam; Kiros, Jamie; Haffari, Gholamreza; Norouzi, Mohammad

doi:10.1162/tacl_a_00492

Cited by 11 publications

(6 citation statements)

References 45 publications

(51 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To overcome the limitations of real-world data availability, NLP researchers have explored the use of synthetic datasets for several applications. For example, He et al [42] utilized language models to generate synthetic unlabeled text. They introduced the Generate, Annotate, and Learn (GAL) framework that leverages synthetic text for knowledge distillation, self-training, and few-shot learning purposes.…”

Section: Synthetic Data Collectionmentioning

confidence: 99%

Socially Aware Synthetic Data Generation for Suicidal Ideation Detection Using Large Language Models

Ghanadian,

Nejadgholi,

Osman

2024

IEEE Access

View full text Add to dashboard Cite

Suicidal ideation detection is a vital research area that holds great potential for improving mental health support systems. However, the sensitivity surrounding suicide-related data poses challenges in accessing large-scale, annotated datasets necessary for training effective machine learning models. To address this limitation, we introduce an innovative strategy that leverages the capabilities of generative AI models, such as ChatGPT, Flan-T5, and Llama, to create synthetic data for suicidal ideation detection. Our data generation approach is grounded in social factors extracted from psychology literature and aims to ensure coverage of essential information related to suicidal ideation. In our study, we benchmarked against state-of-the-art NLP classification models, specifically, those centered around the BERT family structures. When trained on the real-world dataset, UMD, these conventional models tend to yield F1-scores ranging from 0.75 to 0.87. Our synthetic data-driven method, informed by social factors, offers consistent F1scores of 0.82 for both models, suggesting that the richness of topics in synthetic data can bridge the performance gap across different model complexities. Most impressively, when we combined a mere 30% of the UMD dataset with our synthetic data, we witnessed a substantial increase in performance, achieving an F1-score of 0.88 on the UMD test set. Such results underscore the cost-effectiveness and potential of our approach in confronting major challenges in the field, such as data scarcity and the quest for diversity in data representation.

show abstract

Section: Synthetic Data Collectionmentioning

confidence: 99%

Socially Aware Synthetic Data Generation for Suicidal Ideation Detection Using Large Language Models

Ghanadian,

Nejadgholi,

Osman

2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The quality of current synthetically-generated text has encouraged researchers to explore their potential for complementing labor-intensive tasks, such as annotation and evaluation. For instance, He et al (2022) generated synthetic unlabeled text tailored for a specific NLP task. Then, they used an existing supervised classifier to silver-annotate those sentences, aiming to establish a fully synthetic process for generating, annotating, and learning instances relevant to the target problem.…”

Section: Natural Language Annotation and Data Generation Using Llmsmentioning

confidence: 99%

Contrasting Linguistic Patterns in Human and LLM-Generated News Text

Muñoz-Ortiz,

Gómez-Rodríguez,

Vilares

2024

Preprint

View full text Add to dashboard Cite

We conduct a quantitative analysis contrasting human-written English news text with comparable large language model (LLM) output from from six different LLMs that cover three different families and four sizes in total. Our analysis spans several measurable linguistic dimensions, including morphological, syntactic, psychometric, and sociolinguistic aspects. The results reveal various measurable differences between human and AI-generated texts. Human texts exhibit more scattered sentence length distributions, more variety of vocabulary, a distinct use of dependency and constituent types, shorter constituents, and more optimized dependency distances. Humans tend to exhibit stronger negative emotions (such as fear and disgust) and less joy compared to text generated by LLMs, with the toxicity of these models increasing as their size grows. LLM outputs use more numbers, symbols and auxiliaries (suggesting objective language) than human texts, as well as more pronouns. The sexist bias prevalent in human text is also expressed by LLMs, and even magnified in all of them but one. Differences between LLMs and humans are larger than between LLMs.

show abstract

“…The application of temperature scaling during the distillation process facilitated the transfer of softer probability distributions from the teacher to the student, promoting a more nuanced learning process [38,39]. The introduction of intermediate layer matching aimed to align the representational spaces of the student and teacher models more closely, enhancing the fidelity of knowledge transfer [40][41][42]. Techniques such as progressive distillation, where the student model was iteratively refined using multiple teacher models, sought to aggregate diverse knowledge representations, further improving performance [43,44].…”

Section: Knowledge Distillationmentioning

confidence: 99%

Reducing LLM Hallucination Using Knowledge Distillation: A Case Study with Mistral Large and MMLU Benchmark

McDonald,

Papadopoulos,

Benningfield

2024

Preprint

View full text Add to dashboard Cite

The application of knowledge distillation to reduce hallucination in large language models represents a novel and significant advancement in enhancing the reliability and accuracy of AI-generated content. The research presented demonstrates the efficacy of transferring knowledge from a high-capacity teacher model to a more compact student model, leading to substantial improvements in exact match accuracy and notable reductions in hallucination rates. The methodology involved the use of temperature scaling, intermediate layer matching, and a comprehensive evaluation using the MMLU benchmark, which assessed the model’s performance across a diverse set of tasks. Experimental results indicated that the distilled model outperformed the baseline in generating accurate and contextually appropriate responses while maintaining computational efficiency. The findings underscore the potential of knowledge distillation as a scalable solution for improving the robustness of large language models, making them more applicable to real-world scenarios that demand high factual accuracy. Future research directions include exploring multilingual and multi-modal distillation, integrating reinforcement learning, and developing more refined evaluation metrics to further enhance model performance.

show abstract

Generate, Annotate, and Learn: NLP with Synthetic Text

Cited by 11 publications

References 45 publications

Socially Aware Synthetic Data Generation for Suicidal Ideation Detection Using Large Language Models

Socially Aware Synthetic Data Generation for Suicidal Ideation Detection Using Large Language Models

Contrasting Linguistic Patterns in Human and LLM-Generated News Text

Reducing LLM Hallucination Using Knowledge Distillation: A Case Study with Mistral Large and MMLU Benchmark

Contact Info

Product

Resources

About