More is not always better? Enhancing Many-Shot In-Context Learning with Differentiated and Reweighting Objectives

Abstract: LLMs excel at few-shot in-context learning (ICL) without requiring parameter updates. However, as ICL demonstrations increase from a few to many, performance tends to plateau and eventually decline. We identify two primary causes for this trend: the suboptimal negative log-likelihood (NLL) optimization objective and the incremental data noise. To address these issues, we introduce \textit{DrICL}, a novel optimization method that enhances model performance through \textit{Differentiated} and \textit{Reweighting} objectives. Globally, DrICL utilizes differentiated learning to optimize the NLL objective, ensuring that many-shot performance surpasses zero-shot levels. Locally, it dynamically adjusts the weighting of many-shot demonstrations by leveraging cumulative advantages inspired by reinforcement learning, thereby mitigating the impact of noisy data. Recognizing the lack of multi-task datasets with diverse many-shot distributions, we develop the \textit{Many-Shot ICL Benchmark} (ICL-50)-a large-scale benchmark of 50 tasks that cover shot numbers from 1 to 350 within sequences of up to 8,000 tokens-for both fine-tuning and evaluation purposes. Experimental results demonstrate that LLMs enhanced with DrICL achieve significant improvements in many-shot setups across various tasks, including both in-domain and out-of-domain scenarios. We release the code and dataset hoping to facilitate further research in many-shot ICL\footnote{https://github.com/xiaoqzhwhu/DrICL}.

• The paper introduces DR-ICL, a framework combining global differentiated learning and local advantage-based reweighting to enhance LLM performance in many-shot in-context learning.
• The methodology mitigates performance decline by filtering data noise and balancing many-shot with zero-shot instances for robust generalization.
• Results on the MICLB benchmark across 50 datasets demonstrate consistent improvements in accuracy and stability for tasks with extensive contextual data.

Enhancing Many-Shot In-Context Learning with Differentiated and Reweighting Objectives

Introduction

The paper "More is not always better? Enhancing Many-Shot In-Context Learning with Differentiated and Reweighting Objectives" explores the limitations of large-scale in-context learning (ICL) as the number of examples provided in learning increases significantly. In particular, it addresses the performance plateau and subsequent decline observed when the number of demonstrations increases from a few to many within LLMs. Identifying suboptimal Negative Log-Likelihood (NLL) optimization objectives and incremental data noise as the primary causes, the paper introduces a novel technique called DR-ICL for improved optimization and performance in many-shot settings.

Methodology

The DR-ICL strategy consists of two core components: global differentiated learning and local advantage-based reweighting. These approaches work together to fine-tune LLMs more effectively in many-shot scenarios, ensuring that the model surpasses zero-shot performance and adjusts dynamically to the varying number of demonstration examples.

Global Differentiated Learning: This method adjusts the NLL objective function across many-shot and zero-shot contexts. The paper proposes that the many-shot instances’ performance should consistently surpass that of zero-shot examples by promoting an improved understanding of contextual cues.

Local Advantage-Based Reweighting: Drawing inspiration from reinforcement learning, this method focuses on dynamically weighting each demonstration based on accrued cumulative advantages. This localized adjustment serves to filter noise more effectively as the number of demonstration shots increases. By dividing sequences into reweighting windows, it assigns differential importance, allowing the model to maintain robust generalization capabilities.

Figure 1: The DR-ICL Training Framework. (a) The global differentiated learning for many-shot and zero-shot demonstrations. (b) The local advantage-based reweighting method.

Experimentation

The researchers developed the Many-Shot ICL Benchmark (MICLB) to provide a comprehensive foundation for evaluating many-shot ICL strategies. MICLB spans 50 different datasets across seven distinct NLP tasks, accommodating shot numbers from 1 to 350 and sequences of up to 8,000 tokens, which allows for rigorous performance testing under many-shot conditions.

Evaluation and Results: The paper conducted extensive experiments using open-source LLMs enhanced with DR-ICL on MICLB. The results highlight that LLMs with DR-ICL significantly improve performance in many-shot configurations. These improvements are consistent across various tasks, both in-domain and out-of-domain. The study showcases that leveraging differentiated learning objectives and adaptive reweighting can substantially enhance the performance stability and accuracy of LLMs in challenging, data-intense environments.

Figure 2: The performance with incremental k-shots for Mistral-7B-Instruct-v0.2 and Llama-2-7b-chat-hf on CLSClusteringS2S under different strategies.

Discussion

DR-ICL presents a nuanced advancement in handling many-shot ICL by combining strategic model optimization with robust data handling methodologies. The differentiated learning aspect ensures a balanced trade-off between leveraging many-shot and managing zero-shot learning, while the reweighting mechanism dynamically filters data noise, improving the overall signal quality in demonstrations.

Performance and Scalability: The introduction of DR-ICL results in greater stability and accuracy of LLMs in many-shot ICL settings. It also addresses the scalability issue by effectively managing ultra-large datasets through the MICLB benchmark, which allows for varied and comprehensive evaluations.

Future Directions: The implications of the research suggest potential future developments in AI, focusing on further refining learning objectives to accommodate even larger datasets and demonstration examples. Future work could explore expanding this methodology to other domains beyond NLP, or integrating additional learning paradigms to drive LLM capacity further.

Conclusion

The DR-ICL framework has proven effective in addressing the challenges associated with many-shot in-context learning. By refining the learning objectives and dynamically reweighting many-shot demonstrations, the strategy laid out in the paper offers a substantial improvement in LLM performance in extensive contextual demonstration scenarios. This work not only enhances existing models but also paves the way for further exploration into optimized learning techniques for complex, data-rich environments.