arXiv:2606.02628v1 Announce Type: cross Abstract: We investigate whether open-source LLMs encode a linearly separable truthfulness signal in their hidden states, and at which network depth this signal is strongest. Across three $7$B--$8$B instruction-tuned models (Llama-3.1-8B, Mistral-7B, Qwen2.5-7B) loaded in $4$-bit NF4 quantization, we extract per-layer hidden states on four hallucination benchmarks (TruthfulQA, HaluEval-QA, FEVER, and a controlled synthetic set) and compare four detection approaches: linear and MLP probes, INSIDE EigenScore, self-consistency, and attention entropy. A line
This paper leverages recent advancements in LLM analysis and quantization to provide a deeper understanding of hallucination mechanisms as AI systems become more ubiquitous.
A strategic reader should care because the ability to detect and potentially mitigate hallucination at a fundamental level in LLMs is crucial for their reliable deployment in sensitive applications.
This research provides a more direct and efficient method for identifying the internal signals of hallucination within quantized LLMs, moving beyond purely external evaluation metrics.
- · AI Safety Researchers
- · LLM Developers
- · Enterprises deploying LLMs
- · Unreliable AI applications
- · Undetected hallucination incidents
Improved tools and methodologies for debugging and improving the factual accuracy of large language models will emerge.
Increased trust and adoption of quantized LLMs in enterprise and critical infrastructure due to enhanced reliability guarantees.
The development of LLMs with intrinsic, real-time hallucination self-correction mechanisms embedded at the architectural level.
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