A Hybrid Lightweight LLM Chatbot for Sustainable Cryptocurrency Investment Decisions: Optimizing Small Models for Domain-Specific Performance
Bibliographic record
Abstract
This paper presents a hybrid chatbot for sustainable cryptocurrency investment, powered by small-scale Large Language Models (LLMs). We evaluate the performance of various lightweight LLMs (under 10B parameters) on blockchain and sustainability-related tasks, demonstrating that carefully orchestrated smaller models can effectively match or exceed the performance of larger models in domain-specific applications. Our multi-agent Retrieval-Augmented Generation (RAG) pipeline, incorporating real-time sustainability metrics from the Crypto Carbon Ratings Institute, achieved 87.09% accuracy in providing investment guidance, improving upon the 72.58% baseline of individual models. The results show that refined instruction engineering and specialized pipeline architecture can significantly enhance model performance without requiring larger, more energy-intensive models. This work contributes to both the practical implementation of sustainable cryptocurrency investment tools and the broader discussion of environmental considerations in AI system design and deployment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".