llm-based-asr-steering-decoding-toward-acoustic-evidence-only-helpsIn LLM-based ASR, steering decoding toward acoustic evidence only helps if candidates are first restricted to tokens within a small likelihood margin of the greedy token: unconstrained selection by an acoustic compatibility score removes hallucinated outputs but destroys transcription accuracy through unbounded insertions, while the constrained version removes a large share of hallucinations at near-zero WER/CER cost.
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
Four open LLM-based ASR systems (Qwen3-ASR-1.7B, Qwen2-Audio-7B Base, Kimi-Audio-7B-Instruct, GLM-ASR-Nano) on two 500-utterance stress suites built to elicit code-switching and in.
Sources
- Paired greedy-vs-LCAR decoding on 8800 frozen detector-positive records plus negative controls, with an LLM detector whose precision was human-audited at 93.3% on a 400-case stratified sample. Ablation on 800 Qwen3 positives isolates the likelihood constraint: acoustic- only selection gives comparable overall hallucination fixing but >600/1000 point WER/CER increases. Normal-speech effects measured on LibriSpeech test-clean and AISHELL2. Gains vary by model and suite; [truncated]
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
- In synthetic hallucination benchmarks where faithful items are human-written and hallucinated items are LLM-rewrites, a detector's binary score partly measures human-vs-machine style rather than factuality: feeding faithful LLM rewrites of the same truthful articles makes some fine-tuned encoders flag nearly all of them as hallucinated.Whether the measurement made the finding · unreviewed
- For detecting hallucinated text spans in vision-language model outputs, span localization and confidence calibration come from different architectures: a fine-tuned generative VLM proposes better span boundaries but scores them poorly, while a discriminative token tagger trained with a direct regression loss on human-consensus confidence gives better-correlated probabilities but conservative spans, so using the generative spans as proposals and the tagger's probabilities as scores beats either alone.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
- In diffusion vision-language models generating free-form captions, object tokens that are hallucinated commit at later denoising steps and with lower per-token confidence than grounded object tokens, so the commit-step trajectory is an available internal hallucination signal that has no counterpart in autoregressive decoding.Grounding answers in the image · unreviewed
- For training-free inference-time hallucination mitigation in 7B vision- language models, reported gains on object-hallucination benchmarks are largely inseparable from reduced informativeness: hallucination rate and object recall/coverage move together, so a lower score can mean the model mentioned fewer visual entities rather than grounded better.Whether the measurement made the finding · 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: medium. Falsifier as drafted: An unconstrained acoustic-reranking decoder that matches or beats the likelihood-constrained version on both hallucination removal and standard-test WER/CER, or a constrained variant whose hallucination reduction comes with large WER regressions on normal speech. Proposed technique, not catalogued: likelihood-constrained acoustic reranking.