Text Data Augmentation for Large Language Models: A Comprehensive Survey of Methods, Challenges, and Opportunities (2501.18845v1)

Abstract: The increasing size and complexity of pre-trained LLMs have demonstrated superior performance in many applications, but they usually require large training datasets to be adequately trained. Insufficient training sets could unexpectedly make the model overfit and fail to cope with complex tasks. LLMs trained on extensive corpora have prominent text generation capabilities, which improve the quality and quantity of data and play a crucial role in data augmentation. Specifically, distinctive prompt templates are given in personalised tasks to guide LLMs in generating the required content. Recent promising retrieval-based techniques further improve the expressive performance of LLMs in data augmentation by introducing external knowledge to enable them to produce more grounded-truth data. This survey provides an in-depth analysis of data augmentation in LLMs, classifying the techniques into Simple Augmentation, Prompt-based Augmentation, Retrieval-based Augmentation and Hybrid Augmentation. We summarise the post-processing approaches in data augmentation, which contributes significantly to refining the augmented data and enabling the model to filter out unfaithful content. Then, we provide the common tasks and evaluation metrics. Finally, we introduce existing challenges and future opportunities that could bring further improvement to data augmentation.

The paper presents a comprehensive review of prompt engineering in LLMs, offering a systematic taxonomy and a critical analysis of methods to effectively design, evaluate, and deploy prompts for numerous downstream tasks. The work examines both manual and automated approaches for prompt construction and discusses the interplay between prompt complexity and model performance.

The review is organized around several key axes:

• Taxonomy of Prompt Types and Strategies:
  • The authors decompose prompt engineering techniques into categories such as single-step versus multi-step prompting, including chain-of-thought mechanisms, role-based prompts, and structured template-based prompts.
  • They analyze how different prompt formulations can serve varied objectives: from guiding factual generation to mitigating hallucinations and enhancing context sensitivity.
  • Various methods are discussed that range from direct instruction following to approaches that integrate auxiliary tasks or exemplar demonstrations.
• Methodological Underpinnings:
  • The survey synthesizes technical details from recent literature, highlighting the design choices that affect prompt robustness, such as leveraging in-context learning and iterative refinement.
  • The discussion includes elaboration on few-shot versus zero-shot prompt configurations, where the prompt may involve external demonstrations or meta-instructions designed to activate latent model capabilities.
  • The review further considers quantitative aspects, including how prompt sensitivity can be modeled as a function $f(P, x, \theta)$ where $P$ denotes the prompt, $x$ the input data, and $\theta$ the model parameters, emphasizing the challenges in ensuring consistency and generalizability.
• Evaluation Metrics and Benchmarking Procedures:
  • The paper details automatic evaluation metrics (e.g., accuracy, exact match scores, ROUGE, BLEU) and also advocates for human evaluation criteria centered on consistency, coherence, and informativeness.
  • It critically assesses the reliability of these metrics when applied across diverse tasks, emphasizing that prompt effectiveness often varies with the task domain and model configuration.
• Challenges and Opportunities:
  • A comprehensive discussion outlines current limitations such as prompt brittleness and susceptibility to adversarial phrasing, as well as issues of transferability across domains.
  • The review raises important questions regarding the trade-off between prompt complexity and performance gains, as well as the computational cost associated with iterative prompt optimization techniques.
  • Future directions are proposed that include developing adaptive prompt learning frameworks and integrating retrieval-based methods as complementary components to mitigate inherent model biases and hallucinations.
• Interplay with Data Augmentation and Model Adaptability:
  • The authors also contextualize prompt engineering within broader data augmentation paradigms, noting that carefully engineered prompts can effectively serve as implicit data transformations that enrich the training signal.
  • They discuss scenarios where prompts compensate for data scarcity issues by inducing diverse procedural reasoning in models, thereby indirectly enhancing model adaptability.

Overall, the paper offers a dense survey of current methodologies in prompt engineering for LLMs, balanced with an analysis of the underlying challenges that remain unresolved. The discussion is supported by extensive comparisons across numerous experimental results and theoretical perspectives, making it a valuable resource for researchers aiming to harness the full potential of prompt-based interactions with large-scale LLMs.