How does the predictive expert caching strategy in ExpertFlow affect multi-object hallucination rates (measure

Abstract

Abstract: Since the introduction of ChatGPT, large language models (LLMs) have demonstrated significant utility in various tasks, such as answering questions through retrieval-augmented generation. Context can be retrieved using a vectorized database, serving as a foundation for LLMs to generate responses. However, hallucinations in responses can undermine the reliability of LLMs in practical applications, and they are not easily detectable in the absence of ground truth, particularly in question-and-answer scenarios. This paper proposes a framework that integrates multiple small language models to veri

Research Question

How does the predictive expert caching strategy in ExpertFlow affect multi-object hallucination rates (measured by mPOPE or entity-level F1) compared to dense models of equivalent FLOPs in MoE-VLMs across varying numbers of active experts?

Verification Level

Descriptive public verification status only; aggregate claim counts are public, but individual claim records are not exposed here.

Quality Tier

Descriptive public triage only; this tier does not alter current publication or DOI behavior.

Quality Dimensions

Automated triage signals derived from public fields; not human peer review or independent validation.

Correction Record

Public corrections are additive records. Current status does not claim the synthesis is error-free.

Provenance