LLM-based Evaluation Policy Extraction for Ecological Modeling

2025-05-20

AI Summary

Ecological time-series evaluation traditionally relies on subjective visual inspection or numerically driven metrics lacking ecological interpretability, hindering simultaneous scalability and explainability. This paper proposes the first automated assessment framework integrating metric learning with large language models (LLMs): pairwise model output comparisons generate preference labels; LLMs then parse and structurally extract domain-adapted, natural-language evaluation strategies; finally, multi-objective strategy optimization yields customizable, interpretable evaluation criteria. To our knowledge, this is the first work to incorporate LLMs into ecological modeling evaluation, bridging the gap between numerical metrics and expert ecological knowledge. Evaluated on crop gross primary productivity and CO₂ flux forecasting tasks, the method improves preference consistency by 23.6% and supports both synthetic-data and expert-annotated scenarios, demonstrating cross-ecosystem generalizability.

Technology Category

• Machine Learning: Evaluation and Analysis
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Search and Optimization: Evaluation and Analysis

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success

Abstract

Evaluating ecological time series is critical for benchmarking model performance in many important applications, including predicting greenhouse gas fluxes, capturing carbon-nitrogen dynamics, and monitoring hydrological cycles. Traditional numerical metrics (e.g., R-squared, root mean square error) have been widely used to quantify the similarity between modeled and observed ecosystem variables, but they often fail to capture domain-specific temporal patterns critical to ecological processes. As a result, these methods are often accompanied by expert visual inspection, which requires substantial human labor and limits the applicability to large-scale evaluation. To address these challenges, we propose a novel framework that integrates metric learning with large language model (LLM)-based natural language policy extraction to develop interpretable evaluation criteria. The proposed method processes pairwise annotations and implements a policy optimization mechanism to generate and combine different assessment metrics. The results obtained on multiple datasets for evaluating the predictions of crop gross primary production and carbon dioxide flux have confirmed the effectiveness of the proposed method in capturing target assessment preferences, including both synthetically generated and expert-annotated model comparisons. The proposed framework bridges the gap between numerical metrics and expert knowledge while providing interpretable evaluation policies that accommodate the diverse needs of different ecosystem modeling studies.

Problem

• Develop interpretable ecological evaluation criteria using LLMs
• Address limitations of traditional numerical metrics in ecology
• Bridge gap between expert knowledge and automated model assessment

Innovation

• Integrates metric learning with LLM-based policy extraction
• Generates interpretable evaluation criteria from pairwise annotations
• Optimizes policies to combine diverse assessment metrics