Incremental Utility: A Novel Approach to Improve Few-Shot In-Context Learning with Large Language Models

Core Concepts

Introducing a novel method called "incremental utility" to estimate how much additional knowledge a demonstration brings to a large language model for few-shot in-context learning tasks, and showing its effectiveness compared to previous utility estimation approaches.

Abstract

This paper presents an analysis on different utility functions for selecting demonstrations in few-shot in-context learning (ICL) with large language models (LLMs). The authors introduce a novel method called "incremental utility" that estimates how much incremental knowledge a demonstration brings to the LLM by contrasting its 0-shot and 1-shot performance. The key highlights are: The authors compare two types of utility functions: (1) the LLM's output probability of generating the ground-truth output, and (2) a task-specific reward function given the LLM's prediction. The output probability is effective when the probability values are well distributed across the whole range, especially on classification tasks. The downstream metric reward is more robust for longer outputs like in segmentation and translation tasks. The proposed incremental utility further improves ICL by effectively training the reranking model using contrastive examples that show both positive and negative impacts of demonstrations. Constrained retrieval, which ensures equal coverage of class labels in the retrieved candidates, is helpful when the retrieved set is imbalanced. The authors provide general instructions on when to use the different utility functions based on the task characteristics. The analysis is comprehensive, covering binary/multi-class classification, segmentation, and translation tasks across multiple languages. The authors also discuss the generalization of their findings by experimenting with different LLMs and retrievers.

Stats

The authors report the following key statistics and figures: The output probability (OP) values are well distributed across the whole range [0.0, 1.0] on the classification datasets, while the downstream metric (DM) values show a more balanced distribution on the non-classification datasets. 60-80% of the uOP values fall into the [0.0, 0.05) bucket on the SSENT and XML-MT datasets, indicating the LLM's difficulty in generating long text outputs. The number of contrastive training examples, where a demonstration has both positive and negative impacts, varies across datasets and correlates with the effectiveness of the incremental utility.

Quotes

None.

Key Insights Distilled From

Take One Step at a Time to Know Incremental Utility of Demonstration

by Kazuma Hashi... at arxiv.org 04-04-2024

https://arxiv.org/pdf/2311.09619.pdf

Deeper Inquiries

How can the proposed incremental utility be extended to handle the compositional effects of multiple demonstrations, beyond the independent estimation for each demonstration

To extend the proposed incremental utility to handle the compositional effects of multiple demonstrations, we can consider a few key strategies: Aggregate Incremental Utility: Instead of estimating the utility for each demonstration independently, we can aggregate the incremental utilities of multiple demonstrations. This can involve summing up the incremental utilities or applying a weighted average based on the importance or relevance of each demonstration. Interaction Effects: We can explore how different combinations of demonstrations interact with each other in influencing the LLM's predictions. By analyzing the joint effects of multiple demonstrations, we can better understand their combined impact on the incremental knowledge gained by the LLM. Sequential Learning: If the demonstrations are presented sequentially, we can investigate how the incremental utility changes as new demonstrations are added. This sequential learning approach can help capture the dynamic nature of knowledge acquisition by the LLM. Hierarchical Modeling: Introducing a hierarchical modeling approach can account for the hierarchical relationships between demonstrations. By considering the demonstrations at different levels of abstraction or specificity, we can capture the nuanced effects of each demonstration on the LLM's learning process. By incorporating these strategies, we can enhance the incremental utility framework to handle the compositional effects of multiple demonstrations more effectively.

What are the potential biases in the LLM's outputs that may be transferred to the reranking models, and how can we further debias the feedback signals

Inherent biases in LLM outputs can significantly impact the performance and generalization of reranking models trained on these outputs. Some potential biases that may be transferred to the reranking models include: Societal Biases: LLMs trained on large-scale datasets may inadvertently learn and propagate societal biases present in the data. These biases can manifest in the predictions generated by the LLM and subsequently influence the utility estimation of demonstrations. Data Imbalance: Biases arising from imbalanced datasets can skew the LLM's predictions towards overrepresented classes or patterns. This imbalance can lead to biased utility estimations for demonstrations related to minority classes or underrepresented concepts. To further debias the feedback signals and mitigate the impact of these biases, several approaches can be considered: Bias Correction Techniques: Implementing bias correction techniques such as reweighting samples, oversampling minority classes, or using adversarial training to reduce bias in the LLM's outputs. Fairness-aware Training: Incorporating fairness-aware training objectives to explicitly minimize bias in the utility estimation process. This can involve optimizing the reranking models to provide fair and unbiased assessments of demonstrations. Diverse Training Data: Ensuring diversity in the training data used for utility estimation can help mitigate biases by exposing the models to a wide range of examples and perspectives. By actively addressing and mitigating biases in the LLM's outputs, we can improve the fairness and reliability of the feedback signals used for training reranking models.

Can the insights from this work be applied to other types of few-shot learning tasks beyond in-context learning, such as few-shot fine-tuning or prompting

The insights from this work on incremental utility and demonstration selection can indeed be applied to other types of few-shot learning tasks beyond in-context learning. Here are some ways in which these insights can be leveraged: Few-Shot Fine-Tuning: In few-shot fine-tuning scenarios, where a model is adapted to a new task with limited examples, understanding the incremental utility of each demonstration can guide the selection of the most informative examples for fine-tuning. By prioritizing demonstrations with high incremental utility, the fine-tuning process can be more efficient and effective. Prompting Strategies: For few-shot prompting tasks, similar to the instruction-tuned LLMs in the context of in-context learning, the concept of incremental utility can help in designing effective prompts that provide the necessary incremental knowledge to the model. By tailoring prompts based on incremental utility estimates, the model can better generalize to new tasks with limited data. Transfer Learning: The principles of incremental utility and demonstration selection can also be applied to transfer learning settings, where a pre-trained model is adapted to related tasks. By identifying demonstrations that offer the most incremental knowledge for the target task, transfer learning can be optimized for improved performance and adaptation. By applying the insights and methodologies developed for in-context learning to other few-shot learning scenarios, we can enhance the efficiency and effectiveness of model adaptation and generalization in various settings.