HalluScan: A Systematic Benchmark for Detecting and Mitigating Hallucinations in Instruction-Following LLMs
arXiv:2605.02443v2 Announce Type: replace Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse natural language processing tasks, yet they remain susceptible to hallucinations -- generating content that is factually incorrect, unfaithful to provided context, or misaligned with user instructions. We present HalluScan, a comprehensive benchmark framework that systematically evaluates hallucination detection and mitigation across 72 configurations spanning 6 detection methods, 4 open-weight model families, and 3 diverse domains. We introduce three key co
The proliferation of advanced LLMs necessitates robust methods to identify and mitigate their inherent limitations, particularly hallucinations that undermine trust and utility.
Hallucinations are a critical barrier to broader, more reliable adoption of LLMs in sensitive and enterprise applications, making efforts to address them strategically important.
The systematic benchmark provided by HalluScan offers a standardized framework for evaluating and improving the factual consistency and reliability of instruction-following LLMs, potentially accelerating progress in this area.
- · AI developers focused on reliability
- · Enterprises deploying LLMs
- · AI safety researchers
- · Users of LLM-powered applications
- · LLM providers with high hallucination rates
- · Applications reliant on unmitigated LLMs
Systematic benchmarks will drive more effective development of hallucination detection and mitigation techniques in LLMs.
Improved reliability will accelerate the integration of LLMs into critical enterprise workflows and decision-making processes.
Increased trust in LLM outputs could significantly expand their addressable market and impact across various industries, reinforcing the 'AI agents' narrative.
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Read at arXiv cs.CL