{
  "annotation_id": "60f62813-6475-448e-8333-c87352f5fa38",
  "date_created": "2026-02-17T05:53:20.168000Z",
  "date_modified": "2026-02-17T05:53:20.168000Z",
  "file_hash": "b2797547875cfa443313fabb8ea66914ac5b5e9a132521aefd27c0659564fada",
  "private": false,
  "record": {
    "abstract": "The advent of Large Language Models (LLMs) has raised concerns about their enormous carbon footprint, starting with energy-intensive training and continuing through repeated inference. This study investigates the potential of using fine-tuned Small Language Models (SLMs) as a sustainable alternative for predefined tasks. Here, we present a comparative analysis of the performance-emissions trade-off between LLMs and fine-tuned SLMs across selected tasks under Natural Language Processing, Reasoning and Programming. Our results show that in four out of the six selected tasks, SLMs maintained comparable performances for a significant reduction in carbon emissions during inference. Our findings demonstrate the viability of smaller models in mitigating the environmental impact of resource-heavy LLMs, thus advancing towards sustainable, green AI.",
    "arxiv_id": "2601.08844",
    "authors": [
      "Anandita Garg",
      "Uma Gaba",
      "Deepan Muthirayan",
      "Anish Roy Chowdhury"
    ],
    "categories": [
      "cs.CL",
      "cs.AI",
      "cs.CY",
      "cs.LG"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "Emissions and Performance Trade-off Between Small and Large Language Models",
    "url": "https://arxiv.org/abs/2601.08844",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "01a4572d-0e61-453b-84a5-c2f974f4717e",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}