Unveiling the Impact of Fine-Tuned Large Language Models on Generalization Ability

Concetti Chiave

Fine-tuning with in-context learning enhances LLMs' generalization for generation tasks.

Sintesi

The study explores the impact of fine-tuning on Large Language Models (LLMs) and their generalization abilities. It compares fine-tuned models with original counterparts across various tasks and datasets. The findings suggest that fine-tuning affects generalization differently for generation and classification tasks. Additionally, fine-tuning with in-context learning improves out-of-domain and cross-task generalization for generation tasks.

Statistiche

Few-shot fine-tuning and ICL exhibit similar levels of generalization.
Fine-tuned models without ICL generally perform better than baseline Llama-2 using ICL.
Fine-tuned LLMs often perform worse using in-context learning than the zero-shot setting.
Fine-tuned models trained with varying sample sizes exhibit superior 0-shot performance compared to the original baseline Llama2 using ICL.
Fine-tuned models underperform compared to the baseline model on generation tasks but outperform on classification tasks.

Citazioni

"Fine-tuned models without in-context learning can generally perform better than baseline Llama-2 using ICL."
"The FTICL models may mitigate catastrophic forgetting for generation tasks by allowing the model to retain its learned capabilities more effectively."
"FTICL tends to deviate less from the original LLM than vanilla fine-tuning, preserving more general knowledge inherent in LLMs."

Approfondimenti chiave tratti da

Unveiling the Generalization Power of Fine-Tuned Large Language Models

by Haoran Yang,... alle arxiv.org 03-15-2024

https://arxiv.org/pdf/2403.09162.pdf

Domande più approfondite

Implications of format specialization on fine-tuned LLMs' generalization abilities

Format specialization refers to the phenomenon where fine-tuned Large Language Models (LLMs) become overly tailored to task-specific formats, potentially compromising their adaptability to new tasks. This can have significant implications for the generalization abilities of these models. When LLMs are fine-tuned on classification tasks, they may exhibit a high degree of format specialization, leading to challenges in adapting to generation tasks. On the other hand, models fine-tuned on generation tasks might struggle with negative transfer when applied to out-of-domain datasets due to this format specialization.

Impact of prompt format on cross-task generalization in large language models

The prompt format plays a crucial role in determining the success of cross-task generalization in large language models. Different prompts can lead to varying levels of performance when models are evaluated across different tasks. In scenarios where prompts from one task inadvertently influence the model's interpretation during evaluation on another task, it can result in reduced performance and hinder cross-task generalization capabilities. Therefore, designing effective and task-appropriate prompts is essential for ensuring optimal performance and enhancing cross-task generalization in large language models.

Optimizing optimization processes for improved FTICL performance on classification tasks

To enhance Fine-Tuning with In-Context Learning (FTICL) performance on classification tasks, optimizing the optimization process itself is crucial. One key aspect is addressing issues related to loss reduction during training. For classification tasks, FTICL may face challenges in further reducing loss due to potential model laziness or reliance on provided labels from in-context examples rather than leveraging relevant information effectively.
Optimizing optimization processes could involve experimenting with different optimizers or hyperparameters tailored specifically for FTICL on classification tasks. By refining the training process through targeted adjustments such as adjusting learning rates or incorporating regularization techniques that encourage better utilization of contextually relevant information while minimizing over-reliance on provided labels, it may be possible to improve overall FTICL performance and facilitate better adaptation across diverse classification tasks.