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  1. Home
  2. Signal Canvas
  3. From Oracle to Noisy Context: Mitigating Contextual Exposure
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From Oracle to Noisy Context: Mitigating Contextual Exposure Bias in Speech-LLMs

Stale8d ago
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Stale evidence

Evidence Receipt

Freshness: 2026-04-02T02:30:40.136932+00:00

Claims: 0

References: 0

Proof: unverified

Freshness: stale

Source paper: From Oracle to Noisy Context: Mitigating Contextual Exposure Bias in Speech-LLMs

PDF: https://arxiv.org/pdf/2603.24034v1

Repository: https://github.com/XYGuo1996/Contextual_Speech_LLMs

Source count: 0

Coverage: 50%

Last proof check: 2026-03-26T20:30:37.134Z

Paper Conversation

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From Oracle to Noisy Context: Mitigating Contextual Exposure Bias in Speech-LLMs

Overall score: 7/10
Lineage: 8749e568cea5…
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Canonical Paper Receipt

Last verification: 2026-03-26T20:30:37.134Z

Freshness: stale

Proof: unverified

Repo: active

References: 0

Sources: 0

Coverage: 50%

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Dimensions overall score 7.0

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Keep exploring

Builds On This
Distilling Conversations: Abstract Compression of Conversational Audio Context for LLM-based ASR
Score 4.0down
Builds On This
The Cascade Equivalence Hypothesis: When Do Speech LLMs Behave Like ASR$\rightarrow$LLM Pipelines?
Score 3.0down
Builds On This
SICL-AT: Another way to adapt Auditory LLM to low-resource task
Score 5.0down
Prior Work
Boosting ASR Robustness via Test-Time Reinforcement Learning with Audio-Text Semantic Rewards
Score 7.0stable
Prior Work
Speaker Verification with Speech-Aware LLMs: Evaluation and Augmentation
Score 7.0stable
Prior Work
Language-Aware Distillation for Multilingual Instruction-Following Speech LLMs with ASR-Only Supervision
Score 7.0stable
Higher Viability
Zipper-LoRA: Dynamic Parameter Decoupling for Speech-LLM based Multilingual Speech Recognition
Score 9.0up
Competing Approach
Speech LLMs are Contextual Reasoning Transcribers
Score 3.0down

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