Hallucination Detection via Activations of Open-Weight Proxy Analyzers
We introduce a proxy-analyzer framework for detecting hallucinations in large language models. Instead of looking inside the generating model, our system reads already-generated text through a small locally hosted open-weight model and spots hallucinations using the reader's own internal activations. This works just as well when the generator is a closed API like GPT-4 as when it is any open-weight model. We built eighteen features grounded in how transformers process text, covering residual stream norms, per-head source-document attention, entropy, MLP activations, logit-lens trajectories, and three new token-level grounding statistics. We trained a stacking ensemble on 72,135 samples fr...
| Authors | Akshita Singh, Prabesh Paudel, Siddhartha Roy |
|---|---|
| Primary category | cs.CL |
| Published | 8 May 2026 |
| Last updated at source | 8 May 2026 |
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