Enhancing Language Model Generalization: Bridging the Gap Between In-Context Learning and Fine-Tuning

Language models (LMs) exhibit impressive capabilities as in-context learners when pretrained on extensive internet text corpora, enabling effective generalization from just a few task examples. However, fine-tuning these models for specific downstream tasks presents significant challenges. While fine-tuning typically requires hundreds to thousands of examples, it often results in limited generalization patterns. For instance, models fine-tuned on statements like “B’s mother is A” have difficulty answering related questions such as “Who is A’s son?” In contrast, LMs can effectively manage such reverse relations in context. This discrepancy prompts a deeper investigation into the differences between in-context learning and fine-tuning generalization patterns and how these distinctions should guide adaptation strategies for downstream tasks.

Research aimed at enhancing LMs’ adaptability has taken several key approaches:

In-context learning studies that examine learning and generalization patterns through empirical, mechanistic, and theoretical analyses.
Out-of-context learning research that explores how models utilize information not explicitly included in prompts.
Data augmentation techniques that employ LLMs to improve performance from limited datasets, specifically addressing issues like the reversal curse through hardcoded augmentations, deductive closure training, and the generation of reasoning pathways.
Synthetic data approaches that have evolved from early hand-designed data to enhance generalization in various domains, such as linguistics or mathematics, to more recent methods that generate data directly from language models.

Recent collaborative research from Google DeepMind and Stanford University has led to the creation of several datasets designed to isolate knowledge from pretraining data, thereby facilitating clean generalization tests. The performance of these models is assessed across various generalization types by exposing pretrained models to controlled information subsets, both in-context and through fine-tuning. Findings indicate that in-context learning tends to show more flexible generalization compared to fine-tuning in data-matched settings. However, there are exceptions where fine-tuning can generalize to reversals within larger knowledge structures. Building upon these insights, researchers have developed methods that enhance fine-tuning generalization by incorporating in-context inferences into the fine-tuning data.

To analyze the effectiveness of these approaches, researchers employed multiple datasets specifically designed to isolate particular generalization challenges or to embed them within broader learning contexts. Evaluation relied on multiple-choice likelihood scoring without providing answer choices in context. The experiments involved fine-tuning the Gemini 1.5 Flash model using batch sizes of 8 or 16. In-context evaluations combined training documents as context for the instruction-tuned model, with random subsampling employed to minimize interference issues in larger datasets.

The key innovation is a dataset augmentation strategy utilizing in-context generalization to enhance fine-tuning dataset coverage, incorporating both local and global strategies that use distinct contexts and prompts. For instance, on the Reversal Curse dataset, in-context learning achieved near-ceiling performance on reversals, while conventional fine-tuning displayed near-zero accuracy as models favored incorrect celebrity names from training. Fine-tuning augmented with in-context inferences matched the high performance of pure in-context learning. Evaluations on simple nonsensical reversals revealed similar performance patterns, though the advantages were less pronounced. For simple syllogisms, while the pretrained model performed at chance level—indicating no data contamination—fine-tuning yielded above-chance generalization for certain types where logical inferences aligned with simple linguistic patterns. However, in-context learning consistently outperformed fine-tuning, with augmented fine-tuning yielding the best overall results.

In conclusion, this paper examines the differences in generalization between in-context learning and fine-tuning when LMs encounter novel information structures. The findings highlight that in-context learning demonstrates superior generalization for certain inference types, prompting the development of methods to boost fine-tuning performance by integrating in-context inferences into training data. Despite these promising outcomes, the study faces limitations, including dependency on nonsense words and implausible operations, as well as its focus on specific LMs, which may restrict the generality of results. Future research should explore learning and generalization differences across various models to further expand on these findings, particularly with newer reasoning models.

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