In an article recently submitted to the ArXiv* server, researchers introduced an innovative approach called Data Augmentation for In-Context Learning (DAIL) to address a common challenge in In-Context Learning (ICL) when high-quality annotated demonstrations are not readily available in real-world scenarios. DAIL leverages the insight that large language models (LLMs) are more familiar with the content they generate. It employs the language model to create paraphrases of test samples and utilizes majority voting to determine the final result based on individual predictions.
Study: DAIL: Revolutionizing In-Context Learning with Data Augmentation through Language Models. Image credit: Generated using DALL.E.3
*Important notice: arXiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as definitive, used to guide development decisions, or treated as established information in the field of artificial intelligence research.
Extensive empirical evaluations demonstrate that DAIL surpasses standard ICL techniques and other ensemble-based methods, particularly in low-resource scenarios. Furthermore, the researchers explore voting consistency as a confidence score for the model when prediction logits are unavailable. Researchers expect this work to inspire further research in ICL, particularly in low-resource settings.
Impact of LLMs on ICL
The rapid rise of LLMs in Natural Language Processing (NLP) has led to growing interest in ICL. ICL stands out by not requiring modifications to LLM parameters. Instead, it leverages instructions, usually involving a task description or prompt, in-context samples or demonstrations, and a specific test case for inference. The approach's tuning-free nature has made it a valuable tool across diverse NLP research domains. However, ICL's dependence on available demonstrations poses challenges, particularly in real-world scenarios with limited annotated samples and fine-grained classification tasks with large target-label spaces.
Introducing DAIL
DAIL leverages the self-generated content familiarity of LLMs. Empirical validation confirms that self-paraphrasing, where the LLM generates paraphrases of the original test sample, significantly contributes to the efficacy of DAIL. This augmentation method generates multiple candidates, pairing them with task-specific prompts and a limited number of randomly chosen demonstrations from the training set.
DAIL operates under a low-resource constraint, allowing no more than one demonstration per label. A majority voting mechanism determines the final label, considering candidates from paraphrased texts and the original sample. DAIL also introduces the concept of voting consistency as a confidence score for LLMs with inaccessible logits.
Experiments and findings
The experiments cover various classification tasks, including Stanford Sentiment Treebank 2 (SST2), SST5, Customer Reviews (CR), Emotion, Text Retrieval Conference (TREC), and Agricultural News (AGNews). Furthermore, researchers employ fine-grained classification datasets, including Empathetic Dialogues and Yahoo Answer Topics, to evaluate DAIL's performance with extensive label spaces. They compare the results against standard ICL methods and consider different numbers of paraphrases per sample (ranging from one to four) to demonstrate DAIL's performance. The study compares DAIL and other ensemble-based methods, Self-Consistency and Prompt-Ensemble, on different datasets. Notably, DAIL shows its superiority in terms of accuracy, especially for specific datasets.
Voting Consistency as Confidence Score: The study explores using voting consistency as a confidence score for the model's predictions when logits are inaccessible, particularly in low-resource scenarios. The results indicate a positive correlation between voting consistency and accuracy, suggesting its reliability as a confidence metric. This approach could address challenges such as model hallucination and confidence calibration in LLMs.
Case Study: A case study illustrates how DAIL operates, focusing on a sentiment prediction task using ChatGPT. The example sentence, which contains both positive and negative expressions, presents a challenge for sentiment prediction. Standard ICL fails to capture the nuanced sentiment and predicts 'Neutral.' However, DAIL generates paraphrases and, through majority voting, successfully predicts the correct 'Negative' sentiment label. This case study demonstrates the effectiveness of DAIL's approach to handle complex sentiment analysis tasks.
Findings: In the conducted experiments, DAIL exhibited promising results, surpassing standard ICL accuracy across various datasets. DAIL-4, which employs four paraphrases per sample, achieved the most significant improvements, especially in datasets with more target classes, like Yahoo Answer Topics. The study reaffirms the core hypothesis that large language models benefit from self-paraphrasing, and this approach enhances their capability to handle diverse content more effectively.
Comparison with Ensemble-Based Methods: The authors also compared DAIL with two other ensemble-based methods, Self-Consistency and Prompt-Ensemble, on various classification datasets. DAIL showcased its strength, notably outperforming the different methods in tasks like SST5 and TREC. It's essential to note that Self-Consistency's performance in these classification datasets could have been stronger than in reasoning datasets, highlighting DAIL's comprehensive nature as it doesn't rely on external sources like reasoning samples. This comparison reinforces DAIL's effectiveness in the low-resource scenario, making it a compelling choice for specific NLP applications.
Summary
To sum up, this work presents DAIL, a data augmentation technique for ICL. DAIL leverages multiple paraphrases generated from the test sample and combines them through ensembling to make predictions. Comparative experiments with standard ICL and other ensemble-based methods highlight DAIL's efficacy in low-resource ICL scenarios. Furthermore, the study explores voting consistency as a reliable confidence estimation metric, revealing a positive correlation between voting consistency and model accuracy.
However, it is essential to acknowledge the limitations of DAIL. The technique requires multiple inferences, adding computational costs compared to standard ICL. Additionally, DAIL's self-paraphrase mechanism depends on a language model's ability to generate high-quality paraphrases, which may not be suitable for smaller language models lacking this capability.
*Important notice: arXiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as definitive, used to guide development decisions, or treated as established information in the field of artificial intelligence research.
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