Towards Energy-aware Requirements Dependency Classification: Knowledge-Graph vs. Vector-Retrieval Augmented Inference with SLMs

• URL: http://arxiv.org/abs/2603.23954v1
• Date: Wed, 25 Mar 2026 05:29:36 GMT
• Title: Towards Energy-aware Requirements Dependency Classification: Knowledge-Graph vs. Vector-Retrieval Augmented Inference with SLMs
• Abstract summary: There is a growing need to transition toward Small Language Models (SLMs) and energy aware architectures for sustainable requirements engineering.<n>This study proposes and empirically evaluates an energy aware framework that compares Knowledge Graph-based Retrieval (KGR) with Vector-based Semantic Retrieval (VSR) to enhance SLM-based inference.
• Score: 0.3499870393443268
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The continuous evolution of system specifications necessitates frequent evaluation of conflicting requirements, a process that is traditionally labour intensive. Although large language models (LLMs) have demonstrated significant potential for automating this detection, their massive computational requirements often result in excessive energy waste. Consequently, there is a growing need to transition toward Small Language Models (SLMs) and energy aware architectures for sustainable Requirements Engineering. This study proposes and empirically evaluates an energy aware framework that compares Knowledge Graph-based Retrieval (KGR) with Vector-based Semantic Retrieval (VSR) to enhance SLM-based inference at the 7B to 8B parameter scale. By leveraging structured graph traversal and high dimensional semantic mapping, we extract candidate requirements, which are then classified as conflicting or neutral by an inference engine. We evaluate these retrieval enhanced strategies across Zero-Shot, Few-Shot, and Chain of Thoughts prompting methods. Using a three-pillar sustainability framework measuring energy consumption (Wh), latency (s), and carbon emissions (gCO2eq) alongside standard accuracy metrics (F1 Score), this research provides a first systematic empirical evaluation and trade off analysis between predictive performance and environmental impact. Our findings highlight the effectiveness of structured versus semantic retrieval in detecting requirement conflicts, offering a reproducible, sustainability aware architecture for energy efficient requirement engineering.

Related papers

• Evaluating the Environmental Impact of using SLMs and Prompt Engineering for Code Generation [2.2165845825140695]
  This paper presents the first systematic empirical study investigating how different prompt engineering strategies impact code generation accuracy alongside sustainability factors.<n>We evaluate six prominent prompting strategies across 11 open-source models (ranging from 1B to 34B parameters) using the HumanEval+ and MBPP+ benchmarks.<n>Our findings indicate that Chain-of-Thought, being a simpler prompting technique, can provide a near-optimal balance between reasoning capability and energy efficiency.
  arXiv  Detail & Related papers  (2026-04-03T06:37:51Z)
• Perspective: Towards sustainable exploration of chemical spaces with machine learning [49.29528647472741]
  We argue that sustainable progress will rely on open data and models, reusable datasets, and domain-specific AI systems.<n>We highlight emerging strategies to enhance efficiency, including general-purpose machine learning (ML) models, multi-fidelity approaches, model distillation, and active learning.
  arXiv  Detail & Related papers  (2026-03-31T13:29:20Z)
• Heuristic-inspired Reasoning Priors Facilitate Data-Efficient Referring Object Detection [53.988759250627425]
  HeROD (Heuristic-inspired ROD) is a lightweight, model-agnostic framework that injects explicit, interpretable spatial and semantic reasonings.<n>HeROD consistently outperforms strong grounding baselines in scarcelabel regimes.
  arXiv  Detail & Related papers  (2026-03-25T10:33:22Z)
• Beyond the Academic Monoculture: A Unified Framework and Industrial Perspective for Attributed Graph Clustering [75.50670592447219]
  Attributed Graph Clustering (AGC) is a fundamental unsupervised task that partitions nodes into cohesive groups by jointly modeling structural topology and node attributes.<n>This survey provides a comprehensive, industrially grounded review of AGC from three complementary perspectives.
  arXiv  Detail & Related papers  (2026-03-21T14:15:34Z)
• AI-CARE: Carbon-Aware Reporting Evaluation Metric for AI Models [2.7946918847372277]
  We propose AI-CARE, an evaluation tool for reporting energy consumption, and carbon emissions of machine learning models.<n>We demonstrate, through theoretical analysis and empirical validation, that carbon-aware benchmarking changes the relative ranking of models.<n>Our proposal aims to shift the research community toward transparent, multi-objective evaluation and align ML progress with global sustainability goals.
  arXiv  Detail & Related papers  (2026-02-17T21:52:48Z)
• Smart but Costly? Benchmarking LLMs on Functional Accuracy and Energy Efficiency [5.771786260272727]
  We present a framework, BRACE, to benchmark Code Language Models on a unified scale of energy efficiency and functional correctness.<n>We propose two rating methods: Concentric Incremental Rating Circles (CIRC) and Observation to Expectation Rating (OTER)<n>Our analysis reveals models generally perform better in the code summarization tasks as they are not enforced to generate a grammar-based and syntactically correct output.
  arXiv  Detail & Related papers  (2025-11-10T23:44:48Z)
• Breaking the ICE: Exploring promises and challenges of benchmarks for Inference Carbon & Energy estimation for LLMs [8.377809633825196]
  We discuss the challenges of current approaches and present our evolving framework, R-ICE, which estimates prompt level inference carbon emissions.<n>Our promising validation results suggest that benchmark-based modelling holds great potential for inference emission estimation.
  arXiv  Detail & Related papers  (2025-06-10T12:23:02Z)
• Latenrgy: Model Agnostic Latency and Energy Consumption Prediction for Binary Classifiers [0.0]
  Machine learning systems increasingly drive innovation across scientific fields and industry.<n>Yet challenges in compute overhead, specifically during inference, limit their scalability and sustainability.<n>This study addresses critical gaps in the literature, chiefly the lack of generalized predictive techniques for latency and energy consumption.
  arXiv  Detail & Related papers  (2024-12-26T14:51:24Z)
• MR-Ben: A Meta-Reasoning Benchmark for Evaluating System-2 Thinking in LLMs [55.20845457594977]
  Large language models (LLMs) have shown increasing capability in problem-solving and decision-making.<n>We present a process-based benchmark MR-Ben that demands a meta-reasoning skill.<n>Our meta-reasoning paradigm is especially suited for system-2 slow thinking.
  arXiv  Detail & Related papers  (2024-06-20T03:50:23Z)
• Entropy-Regularized Token-Level Policy Optimization for Language Agent Reinforcement [67.1393112206885]
  Large Language Models (LLMs) have shown promise as intelligent agents in interactive decision-making tasks. We introduce Entropy-Regularized Token-level Policy Optimization (ETPO), an entropy-augmented RL method tailored for optimizing LLMs at the token level. We assess the effectiveness of ETPO within a simulated environment that models data science code generation as a series of multi-step interactive tasks.
  arXiv  Detail & Related papers  (2024-02-09T07:45:26Z)
• A Comparative Study of Machine Learning Algorithms for Anomaly Detection in Industrial Environments: Performance and Environmental Impact [62.997667081978825]
  This study seeks to address the demands of high-performance machine learning models with environmental sustainability. Traditional machine learning algorithms, such as Decision Trees and Random Forests, demonstrate robust efficiency and performance. However, superior outcomes were obtained with optimised configurations, albeit with a commensurate increase in resource consumption.
  arXiv  Detail & Related papers  (2023-07-01T15:18:00Z)
• An Energy and Carbon Footprint Analysis of Distributed and Federated Learning [42.37180749113699]
  Classical and centralized Artificial Intelligence (AI) methods require moving data from producers (sensors, machines) to energy hungry data centers. Emerging alternatives to mitigate such high energy costs propose to efficiently distribute, or federate, the learning tasks across devices. This paper proposes a novel framework for the analysis of energy and carbon footprints in distributed and federated learning.
  arXiv  Detail & Related papers  (2022-06-21T13:28:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.