Self-Adaptive In-Context Learning: An Information Compression Perspective for In-Context Example Selection and Ordering (2212.10375v2)

Abstract: Despite the surprising few-shot performance of in-context learning (ICL), it is still a common practice to randomly sample examples to serve as context. This paper advocates a new principle for ICL: self-adaptive in-context learning. The self-adaption mechanism is introduced to help each sample find an in-context example permutation (i.e., selection and ordering) that can derive the correct prediction, thus maximizing performance. To validate the effectiveness of self-adaptive ICL, we propose a general select-then-rank framework and instantiate it with new selection and ranking algorithms. Upon extensive evaluation on eight different NLP datasets, our self-adaptive ICL method achieves a 40% relative improvement over the common practice setting. Further analysis reveals the enormous potential of self-adaptive ICL that it might be able to close the gap between ICL and finetuning given more advanced algorithms. Our code is released to facilitate future research in this area: https://github.com/Shark-NLP/self-adaptive-ICL

Self-Adaptive In-Context Learning: An Information Compression Perspective for In-Context Example Selection and Ordering

The paper by Wu et al. tackles the inherent instability of In-Context Learning (ICL) by proposing a novel framework to select and order in-context examples for each input individually. This framework, termed self-adaptive in-context learning, integrates principles from information theory to enhance example selection and permutation, with the objective to optimize downstream task performance.

Overview of Self-Adaptive ICL

ICL leverages pre-trained LLMs (PLMs) to perform tasks by conditioning on a prompt composed of input-output pairs, rather than explicit fine-tuning. Despite its promising few-shot capabilities, conventional ICL approaches rely on random or corpus-level example selection, which often results in volatile performance outcomes. The paper presents an adaptive approach that considers the specific context of each input to resolve these inconsistencies, resulting in notable performance enhancements.

Methodology

The self-adaptive ICL uses a select-then-rank framework, designed to identify high-performing in-context organizations without relying on validation datasets:

1. Selection: Heuristic rules, such as nearest neighbors based on semantic similarity, are employed to filter candidate examples, significantly reducing search space for example permutations.
2. Ranking: Utilizing the Minimum Description Length (MDL) principle, the framework evaluates permutations to find the optimal example organization. From an information compression perspective, the method seeks the organization that efficiently compresses and predicts the test sample, seeking to minimize the description length needed for data transmission.

Results

Empirical analysis across eight NLP datasets revealed that self-adaptive ICL offers a 40% relative improvement over conventional settings. This showcases the potential of aligning example selection more closely with individual instances rather than the dataset as a whole, yielding performance gains comparable to finetuning.

Implications and Future Directions

The findings emphasize the potential to refine ICL approaches further through advanced example selection and ranking strategies. With improvements in search algorithms, there is potential to markedly narrow the performance disparity between ICL and finetuning. Furthermore, the robustness of self-adaptive ICL across diverse PLMs signifies its potential applicability to various model architectures and tasks.

Challenges remain in optimizing the balance between computational efficiency and the effectiveness of example searching, particularly in few-shot scenarios where large retrieval sets are unavailable. Future work should explore alternative selection algorithms and hybrid approaches that simultaneously optimize prompts and example organizations.

In conclusion, this research offers a promising avenue toward stabilizing and enhancing ICL performance, exploiting rich theoretical foundations while suggesting practical methodologies for adaptive learning systems.