ILM Bias in Deep Learning Models

• Internal Language Model (ILM) Bias is a systematic tendency in deep learning models to encode semantic priors that can override raw acoustic or input evidence.
• Mechanistic interpretability tools such as logit lens, linear probing, and activation patching reveal precise layer transitions where ILM bias solidifies and influences error behaviors.
• Targeted interventions in encoder and decoder layers demonstrate practical strategies to mitigate context-driven hallucinations and improve model reliability.

Internal LLM (ILM) Bias refers to structural and representational biases that emerge within a model’s internal activations and information flow, distinct from surface-level dataset or input biases. ILM bias arises when neural models—especially deep encoder–decoder or transformer architectures—learn consistently to favor certain internal abstractions, tokenizations, or high-level concepts, thus shaping the trajectory of intermediate computations in a way that meaningfully impacts error patterns, error correction, and the emergence of semantic priors. This phenomenon, though implicit, has substantial implications for model transparency, robustness, and error analysis across modalities, as exemplified by recent mechanistic interpretability investigations in automatic speech recognition (ASR) and related domains (Glazer et al., 21 Aug 2025).

1. Conceptual Foundations of ILM Bias

ILM bias can be formalized as the tendency of a model’s internal representations and processing circuits to systematically favor or encode certain types of information, priors, or patterns—often as a result of training objectives, inductive biases, or the structure of the dataset—but expressed internally, not as direct output or explicit task predictions. In the context of ASR, language modeling, or time-series processing, ILM bias manifests through:

• Retention of certain linguistic, contextual, or semantic cues across network layers, even when such cues conflict with direct acoustic or sequential evidence.
• Structural priors implicit in the architecture (e.g., tendency for mid-encoder transformer blocks to act analogously to LLMs).
• Phase transitions in confidence or representational character—such as commitment layers in decoder blocks, where the model prematurely “locks in” token predictions based on internal context rather than surface evidence (Glazer et al., 21 Aug 2025).

This concept is closely intertwined with the broader superposition and polysemanticity hypotheses, where directions in activation space come to encode overlapping or abstracted features, often reflecting deep architectural or training-driven biases.

2. Empirical Characterization of ILM Bias in ASR

Layerwise interpretability diagnostics have established a rigorous empirical foundation for the study of ILM bias. Comprehensive analyses on contemporary encoder–decoder ASR models (Whisper, Qwen2-Audio) using logit lens, linear probing, and activation patching reveal consistent internal biases:

• Encoder layers develop strong semantic/contextual priors by layer 27, well before the decoder’s final output. Direct intervention (activation patching) at the encoder level can restore fidelity to the raw acoustic signal, overriding an entrenched internal language-model-like bias.
• Logit lens saturation layers (typically ℓ∗ₜ ≈ 22–25 in the decoder) mark an abrupt shift from low confidence/acoustic supervision to high confidence/context-driven prediction, indicating the internal LLM bias has reached decision-level dominance.
• Linear probes on encoder activations show that high-level semantic classes, accent, or channel conditions are highly linearly decodable from deep encoder layers, suggesting the build-up of non-acoustic, language-model-style abstractions deep within what nominally should be “acoustic” representations.
• Activation patching demonstrates that strongly internalized semantic priors in early encoder layers cause contextually plausible but acoustically incorrect outputs (e.g., "lice"→"rice"), and patching with noise or neutral references weakens these priors, reverting outputs to the correct acoustic form.

These systematic biases are robust across multiple languages, datasets, and model architectures, confirming that ILM bias is a pervasive, model-internal phenomenon (Glazer et al., 21 Aug 2025).

3. Methodological Tools for Isolating and Quantifying ILM Bias

Three established mechanistic interpretability methodologies are instrumental in dissecting ILM bias:

• Logit Lens: Exposes, at each layer, the would-be softmax distribution over the vocabulary if generation were truncated at that point; abrupt transitions or early peaking of correct tokens illuminate where internal biases solidify or override competing evidence.
• Linear Probing: Assesses the progression and depth of linearly accessible feature encoding for both intended and spurious concepts, revealing at what layers non-acoustic (semantic, contextual, source/channel) features start dominating.
• Activation Patching: Permits direct intervention—by mixing, replacing, or ablation—at any subcomponent, providing causal evidence for which layers or units impart semantically-biased representations that can "hallucinate" contextually-plausible errors.

These tools collectively reveal that internal representations encode and transmit rich semantic and contextual information ("internal language modeling") even when it is not externally warranted, thus biasing the system toward plausible but often hallucinated or repeated outputs.

4. Phenomenological Manifestations and Impact

ILM bias produces a spectrum of model behaviors with operational relevance:

• Contextual hallucinations: The encoder builds up a semantic prior that can override true input, causing the system to prefer speech-to-text transcriptions that are contextually plausible but acoustically incorrect; this is directly reversible by patching the encoder with reference or neutral activations.
• Repetition errors: In decoder cross-attention, a sharply localized breakdown (e.g., spike and collapse of the norm in cross-attention heads at layers 18/23) can trigger repetition hallucinations—echoing known pathologies of autoregressive LLMs, but sourced from internally biased representations.
• Non-acoustic representation drift: Encoder layers, when directly projected into text (Encoder Lens), produce fluent but unanchored text (e.g., the same memorized phrase in hundreds of trials), further illustrating the drift towards internal language modeling.

Operational consequences include the need for robust error correction and for internal monitors (e.g., hallucination prediction probes with >93% accuracy at specific layers) to flag both acute and systemic failures rooted in ILM bias.

5. Theoretical Significance and Relationship to Broader Mechanistic Interpretability

ILM bias connects directly to foundational questions about the organizational principles of neural computation in deep learning systems:

• Representation superposition and polysemanticity: ILM bias is a practical instantiation of the superposition hypothesis, where internal representations overlay multiple features and higher-order concepts, often favoring those that align with training objectives (e.g., language modeling within an ASR context).
• Layerwise semantic emergence: The gradual, layerwise emergence of non-input-derived semantics confirms theoretical predictions about hierarchical abstraction in transformers, and challenges the assumption of strict modality separation in multitask models.
• Circuit localization vs. distributed bias: While some biases (e.g., repetition loops) localize to sharp circuit nodes, others are distributed across activation spaces and require both observational and interventional tools for disambiguation.

ILM bias thus both complicates and enriches efforts to reverse engineer neural models, highlighting the need for architecture-aware and interpretability-robust design, especially as internal language-modeling dynamics become critical failure modes.

6. Interventions and Prospective Directions

Mechanistic insights into ILM bias suggest several intervention and research avenues:

• Targeted circuit patching: Localizing circuit nodes which trigger specific hallucinations allows error suppression by patching or fine-tuning specific components (cross-attention heads, mid-encoder layers) without degrading overall performance.
• Internal monitoring tools: Linear probes or causal patches deployed at strategic layers can enable real-time detection and alerting of context-induced errors.
• Architecture and training modification: Mitigation of ILM bias may require adjustment of loss functions, pretraining objectives, or explicit architectural constraints to maintain alignment between upstream (acoustic/temporal) and downstream (semantic/contextual) cues in multimodal nets.
• Expansion to other domains: Similar biases are likely to arise in multimodal, time-series, and even purely symbolic models when internal task-decomposition is required, indicating the need for generalizable mechanistic frameworks.

These perspectives suggest a future in which monitoring and addressing ILM bias becomes integral to the design, deployment, and maintenance of robust, interpretable, and trustworthy deep learning systems (Glazer et al., 21 Aug 2025).