Few-shot Fine-tuning vs. In-context Learning: A Fair Comparison and Evaluation (2305.16938v2)

Abstract: Few-shot fine-tuning and in-context learning are two alternative strategies for task adaptation of pre-trained LLMs. Recently, in-context learning has gained popularity over fine-tuning due to its simplicity and improved out-of-domain generalization, and because extensive evidence shows that fine-tuned models pick up on spurious correlations. Unfortunately, previous comparisons of the two approaches were done using models of different sizes. This raises the question of whether the observed weaker out-of-domain generalization of fine-tuned models is an inherent property of fine-tuning or a limitation of the experimental setup. In this paper, we compare the generalization of few-shot fine-tuning and in-context learning to challenge datasets, while controlling for the models used, the number of examples, and the number of parameters, ranging from 125M to 30B. Our results show that fine-tuned LLMs can in fact generalize well out-of-domain. We find that both approaches generalize similarly; they exhibit large variation and depend on properties such as model size and the number of examples, highlighting that robust task adaptation remains a challenge.

Analyzing Few-shot Fine-tuning versus In-context Learning in NLP

The paper "Few-shot Fine-tuning vs. In-context Learning: A Fair Comparison and Evaluation" addresses an essential aspect of task adaptation strategies in the context of pre-trained LLMs such as OPT. The authors scrutinize two prominent methods: Few-shot Fine-tuning (FT) and In-context Learning (ICL), particularly under the lens of generalization capabilities on in-domain and out-of-domain (OOD) datasets. The empirical paper presented in the paper provides a much-needed comparison between these two approaches using equivalent setups, including similarity in model sizes and parameters, which ensures a fair comparison.

Overview of Task Adaptation Strategies

FT and ICL represent distinctive methodologies for leveraging pre-trained LLMs for specific tasks. FT typically involves adapting a model using a small set of labeled examples, hence the name "few-shot", relying on gradient-based optimization to adjust model parameters. In contrast, ICL adapts a model by merely conditioning it through contextual prompts without fine-tuning the weights, offering a potential advantage in reusability across multiple tasks.

Experimental Framework

The authors employ several pre-trained OPT models ranging from 125M to 30B parameters to compare FT and ICL under controlled conditions. The evaluation focuses on natural language inference and paraphrase identification tasks using datasets such as MNLI, RTE, and QQP for training, and HANS and PAWS-QQP for assessing OOD generalization. For ICL, the models' performance is assessed with 16 textual demonstrations, whereas FT involves fine-tuning with varying numbers of examples, providing a robust basis for comparison. Moreover, the paper emphasizes the importance of pattern selection and model initialization in both adaptation strategies, showing that these factors significantly impact performance.

Results and Key Insights

Contrary to earlier findings that ICL may inherently enable superior OOD generalization given its apparent resilience to certain biases that affect FT, this paper reveals more nuanced results. It suggests that, when evaluated using models of identical sizes, FT often matches or outperforms ICL on OOD benchmarks. This is evident particularly in larger models like the OPT-30B, where FT exhibited superior generalization compared to ICL.

Furthermore, the paper shows that model size plays a crucial role in achieving effective OOD generalization, particularly within the FT framework, where increasing the number of training examples consistently improves performance. This indicates that while ICL provides a straightforward model adaptation without additional training steps, its generalization capability is heavily contingent upon the underlying model's architecture and the configuration of the demonstrations.

Theoretical and Practical Implications

From a theoretical standpoint, these insights underscore the necessity of fair comparisons between different adaptation methods, especially given the disparity observed when different model configurations are initially considered. Practically, the findings advocate for parameter-efficient fine-tuning approaches, suggesting that they can effectively rival ICL's capabilities without incurring additional inference costs related to context length.

Speculations on Future Developments

As the field of AI continues to evolve, it is foreseeable that enhanced methodologies combining the strengths of FT and ICL could emerge. There may be potential for developing techniques that leverage the contextual flexibility of ICL with the strong task-specific adaptations of FT. Additionally, broader applications of multi-lingual, parameter-efficient FT approaches could play a significant role in extending these models to lower-resource languages. The ongoing advancement in hardware and distributed computing methods will likely facilitate such comprehensive explorations and model tamanho enhancements.

In conclusion, this exhaustive analysis reinforces the need for equivalent baselines when assessing the generalization capacities of FT and ICL. The paper contributes significantly to the current understanding of model adaptability in the field of NLP, offering a refined perspective on how pre-trained models can be optimized for diverse applications.