InferBoost: A Fast Microservice Architecture for Reducing Large Language Model Hallucinations
Bibliographic record
Abstract
Large language models (LLMs) often hallucinate—producing plausible but inaccurate responses—particularly when misjudging their own confidence [arXiv:2401.01313]. This paper introduces *InferBoost*, a modular microservice architecture designed to mitigate hallucinations through rapid, topic-specific inference augmentation. The system delivers just-in-time expert-level responses from curated subject-matter-expert (SME) models through a lightweight API, without requiring retraining or prompt engineering. The prototype demonstrates end-to-end latency of one to two seconds on standard CPUs with over 90 % inference quality for multiple domain tasks. By decoupling topic specialization from monolithic LLMs, *InferBoost* enables any client model to enhance its reliability through targeted grounding. Phase 2 will extend the framework to allow models to self-evaluate confidence and selectively invoke *InferBoost* for low-confidence inferences, maintaining real-time performance and high accuracy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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 source (direct Gemma or distilled Codex), 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".