Enhancing Large Language Model Performance through Effective In-Context Sampling

Core Concepts

Leveraging multiple in-context learning prompt inputs can consistently improve the performance and confidence of large language models on various natural language tasks.

Abstract

The content explores a novel In-Context Sampling (ICS) paradigm to enhance the performance of large language models (LLMs) on natural language tasks. The key insights are: LLMs can learn disparate implicit knowledge from different in-context learning (ICL) demonstration examples, leading to varying predictions for the same data. ICS augments multiple ICL prompt inputs by sampling representative demonstration candidates and voting the most confident prediction. This is inspired by the query-by-committee strategy. Experiments on three LLMs (FLAN-T5-XL, Mistral-7B, Mixtral-8x7B) across four NLI and one QA dataset show that ICS can consistently outperform the traditional ICL approach. Three data similarity-based ICS strategies (diversity, similarity, hybrid) further improve performance over the random ICS baseline, demonstrating the potential of carefully curated ICS techniques. The effectiveness of ICS strategies is inversely proportional to the task difficulty, with larger improvements on easier datasets.

Stats

"LLMs can understand not only more complex and detailed task narratives but also a few task examples with annotations within the prompt inputs, namely few-shot In-Context Learning (ICL)." "We hypothesize that different ICL demonstrations provide LLMs with distinct knowledge about the task, leading to disparate understanding and predictions for the same data." "Extensive experiments with three open-source LLMs (FlanT5-XL, Mistral-7B, and Mixtral-8x7B) on four NLI datasets (e-SNLI, Multi-NLI, ANLI, and Contract-NLI) and one QA dataset (CommonsenseQA) illustrate that ICS can consistently enhance LLMs' performance."

Quotes

"While most existing works on LLM prompting techniques focus only on how to select a better set of data samples inside one single prompt input (In-Context Learning or ICL), why can not we design and leverage multiple prompts together to further improve the LLM's performance?" "We hypothesize that different ICL demonstrations provide LLMs with distinct knowledge about the task, leading to disparate understanding and predictions for the same data."

Key Insights Distilled From

More Samples or More Prompts? Exploring Effective In-Context Sampling for LLM Few-Shot Prompt Engineering

by Bingsheng Ya... at arxiv.org 04-03-2024

https://arxiv.org/pdf/2311.09782.pdf

Deeper Inquiries

How can the ICS paradigm be extended to other types of tasks beyond NLI and QA?

The ICS paradigm can be extended to other types of tasks beyond NLI and QA by adapting the sampling and augmentation strategies to suit the specific requirements of those tasks. For tasks that involve image recognition, for example, the demonstration candidates could be sampled from a diverse set of images, and the prompt inputs could be augmented with different visual cues or annotations. Similarly, for tasks in the healthcare domain, the demonstration candidates could be sampled from medical records or patient data, and the prompt inputs could be augmented with relevant medical terminology or context.

What are the potential limitations of the current ICS strategies, and how can they be further optimized?

One potential limitation of the current ICS strategies is the reliance on data similarity-based sampling strategies, which may not always capture the most relevant or informative examples for a given task. To optimize ICS strategies further, a hybrid approach that combines data-based and model-based sampling strategies could be explored. This could involve leveraging model uncertainty metrics to select demonstration candidates and incorporating diversity-based strategies to ensure a more comprehensive representation of the data space. Additionally, the current ICS strategies may not fully address the computational complexity and time-consuming nature of sampling and augmenting prompt inputs. Optimizing the algorithms and workflows involved in these processes, such as implementing parallel processing or efficient data structures, could help mitigate these limitations and improve the scalability of the ICS paradigm.

How can the ICS paradigm be integrated with other prompting techniques, such as Chain-of-Thought, to achieve synergistic performance improvements?

Integrating the ICS paradigm with other prompting techniques, such as Chain-of-Thought, can lead to synergistic performance improvements by combining the strengths of each approach. Chain-of-Thought focuses on generating a sequence of rationales, while ICS emphasizes sampling demonstration candidates and augmenting prompt inputs for confident predictions. By integrating these techniques, the model can benefit from both the structured reasoning provided by Chain-of-Thought and the data-driven learning facilitated by ICS. One way to integrate these techniques is to use Chain-of-Thought to generate initial prompt structures and then apply ICS to sample diverse demonstration candidates and augment the prompts with additional context or examples. This combined approach can enhance the model's understanding of the task narrative and improve its ability to make accurate predictions. Additionally, leveraging the insights from both techniques can lead to more robust and interpretable models, ultimately enhancing performance across a wide range of tasks.