Introduces a 93-question multimodal RAG benchmark with phrase-level recall and embedding-based hallucination metrics, finding closed-source pipelines outperform open-source ones especially on cross-modal and cross-document tasks.
Detecting hallucinations in large language models using semantic entropy,
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Chunk-as-a-Service with the UCOSA online algorithm enables budget-constrained selection of prompts for chunk enrichment in RAG, outperforming random selection by 52% on a combined performance metric and delivering higher performance-to-budget ratios than standard RaaS.
A framework detects LLM anomalies including hallucinations, jailbreaks, and backdoors by forensic inspection of layer-wise hidden state patterns, reporting over 95% accuracy with minimal computational overhead.
citing papers explorer
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FATHOMS-RAG: A Framework for the Assessment of Thinking and Observation in Multimodal Systems that use Retrieval Augmented Generation
Introduces a 93-question multimodal RAG benchmark with phrase-level recall and embedding-based hallucination metrics, finding closed-source pipelines outperform open-source ones especially on cross-modal and cross-document tasks.
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Budget-Constrained Online Retrieval-Augmented Generation: The Chunk-as-a-Service Model
Chunk-as-a-Service with the UCOSA online algorithm enables budget-constrained selection of prompts for chunk enrichment in RAG, outperforming random selection by 52% on a combined performance metric and delivering higher performance-to-budget ratios than standard RaaS.
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Exposing the Ghost in the Transformer: Abnormal Detection for Large Language Models via Hidden State Forensics
A framework detects LLM anomalies including hallucinations, jailbreaks, and backdoors by forensic inspection of layer-wise hidden state patterns, reporting over 95% accuracy with minimal computational overhead.