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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.000 | 0.001 |
| 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".