open-weight-7-8b-instruct-models-supervised-detector-built-how-hiddenIn open-weight 7-8B instruct models, a supervised detector built on how hidden states change between adjacent layers separates factually wrong from correct answers better than a probe on any single layer, and shuffling the layer order destroys most of the signal — so the ordering of the depth trajectory, not just the content of one layer, carries the information.
Ingested from a paper but not yet reviewed by a human. It is deliberately inert: it does not move any technique’s standing, does not count toward the backtest, and is excluded anywhere a claim would carry weight. Read the source before relying on it.
Capability: Stating false facts confidently
Observed on
Requires white-box access to intermediate activations and labelled data to train the scoring head and calibrator; evaluated on English closed-book QA and dialogue (TruthfulQA, Halu.
Sources
- Single paper, single author, author-verified numbers. On Llama-3-8B/TruthfulQA the full method reports 75.5% AUROC vs 66.8% for the best single layer, 66.4% for a matched final-layer MLP, and 58.3% with shuffled layer order; baselines include logit entropy, perplexity, spectral geometry, semantic entropy and an NLI verifier. Transfer to other backbones and datasets loses a few points. No independent replication, and the detector's advantage over the multi-sample baselines is small enough to be within plausible noise given no error bars.
Disagreeing is the most useful thing you can do here. Both sides of every contested claim in this catalog were assembled by the same person, which is its weakest point.
Related claims
- A ~2M-parameter classifier head that reads hidden states already computed during generation (tapping shallow, middle and deep layers of the generating model) detects response-level hallucination at least as well as specialized hidden-state hallucination detectors, and its detection quality rises with the scale of the base model it is attached to.Stating false facts confidently · unreviewed
- A linear probe on a tool-calling model's hidden state at the last generated token detects incorrect tool-calls — including wrong-but-well-typed argument values that parsers and logs do not catch — and works better on larger models and at middle-to-late layers than at the final layer.Using the tools it is given · unreviewed
- For black-box hallucination detection using only sampled responses and token log-probabilities, semantic-entropy signals go to zero whenever the samples collapse into one semantic cluster — which happens for a large share of hallucinated queries, up to nearly all of them on tasks with confusable entities and figures — while token-level uncertainty still separates many of those cases, so the two signals fail on different queries and combining them detects more than either alone.Stating false facts confidently · unreviewed
- For closed-book hallucination detection, training a small supervised classifier over many heterogeneous uncertainty scores beats the best single score, but the gain comes from the diversity of sampling-based black-box signals: ensembles over only single-generation token-probability features add little, while black-box-only ensembles do nearly as well as the full set.Stating false facts confidently · unreviewed
- In open-source vision-language models (LLaVA-1.5, Qwen-VL-Chat, Qwen2.5-VL, LLaVA-NeXT), a training-free decoding intervention that detects large layer-to-layer and step-to-step shifts in hidden states and nudges the diverging states back toward their prior value lowers object-hallucination rates, but the lower rate comes with fewer objects mentioned and shorter outputs.Grounding answers in the image · unreviewed
Notes
Drafted from the paper by a model and filed unreviewed. Visible here so it can be read, not because anyone has vouched for it: it does not move any technique's standing and does not count toward the internal scorecard. Drafted confidence: low. Falsifier as drafted: A replication where a probe on the best single layer, or a matched-size head on the final layer, matches the cross-layer transition detector's AUROC, or where shuffling layer order leaves accuracy roughly unchanged. Proposed technique, not catalogued: Inter-layer transition-divergence hallucination probe.