Challenge: Large language models (LLMs) can be used to generate text data for training and evaluating other models.
Approach: They propose to use logit suppression and temperature sampling to diversify text generation but at the cost of data accuracy.
Outcome: The proposed approach can increase diversity but at the cost of data accuracy.

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Making Large Language Models Better Data Creators (2023.emnlp-main)

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Challenge: Large language models (LLMs) have advanced the field of NLP significantly, but deploying them for downstream applications is still challenging due to cost, responsiveness, control, or concerns around privacy and security.
Approach: They propose a unified data creation pipeline that requires only a single formatting example.
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Scaling Data Diversity for Fine-Tuning Language Models in Human Alignment (2024.lrec-main)

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Challenge: Large language models (LLMs) can reveal toxic or offensive content inadvertently or intentionally.
Approach: They propose to control the diversity of both sides according to the number of samples for fine-tuning, which can directly reflect their impact.
Outcome: The proposed approach improves the performance of large language models after fine-tuning.
Linguistic and Embedding-Based Profiling of Texts Generated by Humans and Large Language Models (2025.emnlp-main)

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Challenge: Recent studies have focused on using LLMs to classify text as either human-written or machine-generated .
Approach: They characterize human-written and machine-generated texts using a set of linguistic features across different linguistic levels such as morphology, syntax, and semantics.
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Data Quality Enhancement on the Basis of Diversity with Large Language Models for Text Classification: Uncovered, Difficult, and Noisy (2025.coling-main)

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Challenge: Existing methods for text classification based on large language models are difficult to apply directly to solve.
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Cross-Lingual Transfer of Debiasing and Detoxification in Multilingual LLMs: An Extensive Investigation (2025.findings-acl)

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Challenge: Prior work has shown that finetuning on specialized datasets can mitigate this behavior, and doing so in English can transfer to other languages.
Approach: They propose to fine tune generative large language models to provide safe responses to harmful user input and to use direct preference optimization to mitigate toxicity.
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G2: Guided Generation for Enhanced Output Diversity in LLMs (2025.emnlp-main)

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Challenge: Existing approaches to enhance output diversity but compromise quality of outputs.
Approach: They propose a training-free plug-and-play method that enhances output diversity while preserving generation quality.
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Improving the OOD Performance of Closed-Source LLMs on NLI Through Strategic Data Selection (2026.findings-eacl)

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Challenge: Existing methods to improve robustness require changing the fine-tuning process or large-scale data augmentation, which are infeasible or cost prohibitive for closed-source models.
Approach: They propose to prioritize more complex examples or replace existing training examples with LLM-generated data to improve performance on OOD NLI datasets.
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The Data Frontier for Large Language Models: Selection, Synthesis, and Tools (2026.acl-tutorials)

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Challenge: acquiring and curating high-quality training data remains a significant bottleneck . acquiring such high-quality data is a key challenge for researchers and practitioners .
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Exploring Precision and Recall to assess the quality and diversity of LLMs (2024.acl-long)

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Challenge: Existing benchmarks for large language models are limited to specific tasks, but they are now widely available for a wide range of tasks.
Approach: They propose a framework for large language models such as Llama-2 and Mistral that imports precision and recall metrics from image generation to text generation.
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Generating Diverse Training Samples for Relation Extraction with Large Language Models (2025.acl-long)

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Challenge: Existing models for Relation Extraction (RE) have good results on many benchmarks, but data scarcity is a common problem.
Approach: They propose to use Large Language Models to generate training data for Relation Extraction . they propose to make LLMs produce dissimilar samples by direct instruction .
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