APE: A Data-Centric Benchmark for Efficient LLM Adaptation in Text Summarization

2025-05-26

AI Summary

To address the challenge of efficiently adapting large language models (LLMs) to domain-specific tasks under limited computational resources (e.g., a single T4 GPU), this paper proposes Adjacent Possible Exploration (APE)—a data-driven, iterative fine-tuning paradigm grounded in the “adjacent possible” theory. APE operates on small batches (200 samples), progressively updating only a critical subset of model parameters, thereby avoiding full-parameter fine-tuning. Evaluated on news summarization, APE achieves a 40% BLEU improvement within just 60 minutes of training—matching or surpassing state-of-the-art parameter-efficient fine-tuning methods such as LoRA. Rigorous validation via performance-based filtering and dual evaluation (BLEU scores and human assessment) ensures result reliability. The implementation is publicly released, establishing a novel lightweight adaptation paradigm for LLMs in resource-constrained settings.

Technology Category

Machine Learning: Large Multimodal Models (LMMs)Natural Language Processing: (Large) Language ModelsCognitive Modeling & Cognitive Systems: Adaptive Behavior

Application Category

User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendationEconomics, Online Markets and Human Computation: Cost models of using LLMs in production systemsSearch and Retrieval-Augmented AI: Large language models for search

Abstract

We present Adjacent Possible Exploration (APE), a simple yet effective method for adapting large language models to specific tasks using minimal computational resources. Unlike traditional fine-tuning that requires extensive compute, APE iteratively fine-tunes models on small, carefully selected data batches (200 examples), retaining only improvements. On news summarization, APE achieves 40 percent BLEU improvement using just a T4 GPU in 60 minutes, matching or exceeding more complex methods like LoRA while remaining conceptually simple. Our approach is particularly valuable for researchers and practitioners with limited computational resources. We provide open-source code and demonstrate APE's effectiveness through both automatic metrics and human evaluation. While inspired by evolutionary theory's"adjacent possible", APE's core insight has a very practical application: small, iterative data perturbations can efficiently guide LLMs toward task-specific performance without expensive retraining.

Problem

• Efficient LLM adaptation with minimal computational resources
• Improving text summarization using small, selected data batches
• Matching complex methods' performance with simple, iterative fine-tuning

Innovation

• Iterative fine-tuning with small data batches
• Minimal computational resources required
• Matches complex methods with simplicity