Understanding the Target Audience for FEEDER
The primary audience for FEEDER: A Pre-Selection Framework for Efficient Demonstration Selection in Large Language Models (LLMs) includes researchers, data scientists, and AI practitioners. These professionals are deeply involved in developing, fine-tuning, and deploying AI models for various applications, such as natural language processing, sentiment analysis, and reasoning tasks.
Pain Points
- Difficulty in selecting the most representative demonstrations from extensive training datasets.
- High computational costs associated with current demonstration selection methods.
- Challenges in maintaining LLM performance as the number of training examples increases.
Goals
- Enhance the efficiency of demonstration selection without compromising model performance.
- Reduce the size of training datasets while retaining essential information.
- Improve the stability and reliability of LLMs across various tasks.
Interests
The audience is particularly interested in innovative methods for optimizing LLM performance, research on few-shot learning and in-context learning techniques, and the applications of LLMs in real-world business scenarios.
Communication Preferences
Clear, concise, and technical communication is preferred, especially when it includes data-driven insights and peer-reviewed statistics. Practical examples and case studies that illustrate the application of research findings in business contexts are highly valued.
Overview of FEEDER
Large language models (LLMs) have demonstrated exceptional performance across various tasks through few-shot inference, also known as in-context learning (ICL). One significant challenge in this area is selecting the most representative demonstrations from large training datasets. Early methods relied on similarity scores between examples and input questions, while current approaches incorporate additional selection rules to enhance efficiency. However, these improvements often lead to increased computational overhead as the number of shots rises.
Researchers from Shanghai Jiao Tong University, Xiaohongshu Inc., Carnegie Mellon University, Peking University, University College London, and the University of Bristol have introduced FEEDER (FEw yet Essential Demonstration prE-selectoR). This method identifies a core subset of demonstrations that contain the most representative examples from training data, tailored to specific LLMs. FEEDER employs “sufficiency” and “necessity” metrics during the pre-selection stage, utilizing a tree-based algorithm to construct this subset. Notably, FEEDER reduces training data size by 20% while maintaining performance and integrates seamlessly with various downstream demonstration selection techniques in ICL across LLMs ranging from 300M to 8B parameters.
Evaluation and Results
FEEDER has been evaluated on six text classification datasets: SST-2, SST-5, COLA, TREC, SUBJ, and FPB, covering tasks from sentiment classification to textual entailment. It has also been assessed on reasoning datasets like GSM8K, semantic-parsing datasets such as SMCALFlow, and scientific question-answering datasets like GPQA. The official splits for each dataset were followed to obtain training and test data. Multiple LLM variants were utilized for performance evaluation, including GPT-2, GPT-neo (1.3B parameters), GPT-3 (6B parameters), Gemma-2 (2B parameters), Llama-2 (7B parameters), Llama-3 (8B parameters), and Qwen-2.5 (32B parameters).
Results indicate that FEEDER enables the retention of nearly half the training samples while achieving superior or comparable performance. In complex tasks, LLMs like Gemma-2 show improved performance with FEEDER, even in scenarios where LLMs typically struggle. FEEDER effectively manages larger numbers of shots, addressing performance drops that occur when increasing examples from 5 to 10 due to noisy or repeated demonstrations. By evaluating the sufficiency and necessity of each demonstration, FEEDER minimizes negative impacts on LLM performance and enhances stability.
Conclusion
In summary, FEEDER is a demonstration pre-selector designed to leverage LLM capabilities and domain knowledge to identify high-quality demonstrations through an efficient discovery approach. It reduces training data requirements while maintaining comparable performance, offering a practical solution for efficient LLM deployment. Future research directions include exploring applications with larger LLMs and extending FEEDER’s capabilities to areas such as data safety and management. FEEDER represents a significant advancement in demonstration selection, providing researchers and practitioners with an effective tool for optimizing LLM performance while reducing computational overhead.
FAQ
- What is FEEDER? FEEDER is a pre-selection framework designed to optimize the selection of demonstrations for large language models, enhancing efficiency while maintaining performance.
- Who can benefit from using FEEDER? Researchers, data scientists, and AI practitioners working with large language models can significantly benefit from FEEDER.
- How does FEEDER improve demonstration selection? FEEDER uses “sufficiency” and “necessity” metrics to identify the most representative demonstrations, reducing the dataset size while retaining essential information.
- What are the results of using FEEDER? FEEDER allows for the retention of nearly half the training samples while achieving superior or comparable performance across various tasks.
- What future research directions are suggested for FEEDER? Future research may explore applications with larger LLMs and extend FEEDER’s capabilities to areas like data safety and management.
