Beyond Decodability: Reconstructing Language Model Representations with an Encoding Probe

arXiv:2605.00607 · cs.CL, eess.AS · Submitted 2026-05-01 · Read on arXiv

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Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: Next we'll be talking about the paper "Beyond Decodability: Reconstructing Language Model Representations with an Encoding Probe".

Jane: The paper was written by Gaofei Shen, Martijn Bentum, Tom Lentz, Afra Alishahi and Grzegorz Chrupała from Tilburg University and Radboud University.

Tom: Stay tuned as we take you through the paper and discuss its implications.

Summary and Findings: Tom: Now that we know what the tool is, let's look at what it actually found in "Beyond Decodability: Reconstructing Language Model Representations with an Encoding Probe" when comparing these different features across various text and speech models.

Jane: The core finding is that different kinds of knowledge are stored in distinct ways within the model. For example, they observed that certain features contribute independently to the reconstruction, suggesting a clear separation of functional roles.

Lu: This supports the idea of modularity; it’s not just one giant chunk of data but separate components handling specific tasks like syntax or lexicon.

Meng: The results show us that we can actually isolate the word's meaning from its grammatical role, which is a huge step toward understanding how the AI handles complex sentences.

Lalam: It implies that the AI has learned not just to recognize words, but to understand the hierarchical structure of language, which is a key element in human communication.

Tom: Another key observation was how much speaker-related effects change dramatically depending on whether or not we tune the model for specific tasks.

Jane: The way they use an E NCODING P ROBE allows them to see that subtle differences in the training objective can fundamentally alter how a piece of knowledge is represented internally.

Lu: That’s fascinating—it tells us that the training process itself dictates how information gets organized, and it’s not just fixed once that the model is built.

Meng: If we can map which features are affected by which training objective, it gives us a very clear path for data curation and design choices going forward.

Lalam: This allows us to intentionally guide the AI's knowledge acquisition based on what we need it to be good at in society, moving toward more specialized, trustworthy systems.

Tom: The results are incredibly rich because they show that these features don't just exist; they vary significantly across training objectives and datasets.

Improvements over Previous Methods: Tom: We’ve seen that "Beyond Decodability: Reconstructing Language Model Representations with an Encoding Probe" offers a clear way to measure contribution, but what specific problems does this new method solve compared to older techniques?

Jane: The biggest problem it solves is the lack of a direct comparison—it moves us past just asking whether a feature can be decoded and toward quantifying its relative importance.

Lu: This disentanglement challenges the old assumption that all linguistic knowledge must be bundled together; the AI seems capable of handling word meaning and sentence structure using distinct, measurable mechanisms.

Meng: From a practical standpoint, this is a huge advantage because we can quantify exactly how much of the model's capacity is dedicated to language structure versus semantic understanding.

Lalam: It suggests that the AI system possesses a deep understanding of the *rules* governing language, which is entirely separate from just looking up words in its dictionary.

Tom: The authors provide concrete evidence for this independence by showing that when removing one feature set—the syntactic rules, for example—the model’s ability to reconstruct its full representation drops noticeably.

Jane: This quantitative proof is what makes the findings so robust; it moves us past theory into real empirical evidence: grammar adds something unique that word choice alone cannot provide.

Lu: And what’s compelling is how they demonstrate this separation across different linguistic domains, confirming that the model’s ability to track a noun’s role operates independently of whether it refers to a car or a person.

Meng: That universality implies the AI has learned general principles of structure, not just specific grammar rules for one language or one domain.

Lalam: This is powerful because it elevates language models from mere pattern matchers to systems that are actively building and maintaining a structured, hierarchical model of reality based on those rules.

Tom: The authors show us that this separation is possible by the E NCODING P ROBE methodology, which allows us to see the full picture.

Conclusion: Tom: So, as we wrap up our deep dive into "Beyond Decodability: Reconstructing Language Model Representations with an Encoding Probe," we’ve seen that this research fundamentally changes how we view AI's internal workings.

Jane: It moves us away from seeing them as mysterious black boxes and towards understanding them as sophisticated, accountable systems where their knowledge is actually measurable and structured.

Lu: What really stands out to me is the evidence of modularity—that complex language capabilities aren't one monolithic unit—is perhaps the most revolutionary finding for computational linguistics today.

Meng: This insight means that future AI design won't just rely on feeding more data; it will require us to build explicit architectural methods to manage and enforce these separate knowledge constraints.

Lalam: Ultimately, viewing the model through this lens allows us to build AI that isn't just capable of prediction, but one that possesses verifiable understanding and inherent auditability in its knowledge base.

Tom: The paper provides a roadmap for how we need to think about intelligence moving forward, especially with these insights into the practical implications.

Lu: It’s such a clear way to see how AI structure will look over the next decade, moving us toward genuine systemic transparency.

Meng: We now have a much more precise language to discuss what constitutes 'understanding' in an artificial context, thanks to this rigorous methodology.

Lalam: This ability to segment and test different types of knowledge is truly universal, principles that apply far beyond just text generation across all systems.

Tom: It’s been genuinely illuminating tracing these lines of knowledge with all of you; we really appreciate these brilliant insights into the practical implications of this research today.

Conclusion: Tom: To wrap up our deep dive, we’ve collectively seen how "Beyond Decodability: Reconstructing Language Model Representations with an Encoding Probe" provides a new framework for viewing the internal workings of AI.

Jane: It moves us away from seeing these systems as black boxes and toward understanding them as sophisticated, measurable, structured systems where their knowledge is accountable.

Lu: The evidence of modularity—that complex abilities aren't one monolithic unit—is perhaps the most revolutionary finding for computational linguistics today.

Meng: This means that future model development must build explicit architectural mechanisms to manage and enforce these separate knowledge constraints to achieve reliability.

Lalam: Viewing the model through this lens allows us to build AI that possesses verifiable understanding and inherent auditability in its knowledge base, which is a cultural necessity.

Jane: And that focus on auditability is huge because it grounds the technological advance in a deep sense of ethical and industrial responsibility for all users.

Tom: It’s been genuinely illuminating tracing these lines of knowledge with all of you; we really appreciate these brilliant insights into the practical implications of this research today.

Lu: Thank you for joining us to unravel the depths contained within "Beyond Decodability: Reconstructing Language Model Representations with an Encoding Probe."

Meng: It gives us a very clear understanding of the challenges ahead for AI structure in implementation and deployment.

Lalam: I agree; those principles of modularity are universal, making this research applicable across so many domains.

Tom: This has been a fantastic roadmap for how we need to think about intelligence moving forward.

Jane: We'll carry the insights from "Beyond Decodability: Reconstructing Language Model Representations with an Encoding Probe" and apply them as we prepare for our next topic, which is all about multimodal reasoning.

Tilburg University · Radboud University

cs.CL, eess.AS

Submitted: 2026-05-01

Updated: 2026-09-03

Importance score: 88/100

The gist: This paper introduces the concept of an "Encoding Probe" as a method to reconstruct and quantify the contribution of various linguistic features—such as phonetic, acoustic, syntactic, and lexical

Key concepts

Modularity: Modularity refers to how knowledge is organized within an AI model. The research shows that instead of being a single monolithic block, the model uses distinct, separate components to handle specific functions, such as grammar (syntax) or word meaning (lexicon).
Encoding Probe: The Encoding Probe is the specific methodology used to measure contribution. It allows researchers to quantify exactly how much capacity within an AI model is dedicated to certain functions, providing empirical evidence of where and how specific knowledge is stored.
Quantifying Importance: This concept moves beyond simply checking if a feature exists. It provides a quantitative measure of relative importance, allowing researchers to precisely determine how much of the AI's overall capacity is dedicated to structural rules compared to its ability to understand word meanings.

Terminology

Transformer — 20× in this paper · explained in 2 episodes
Embeddings — 16× in this paper · explained in 2 episodes
Ablation — 11× in this paper · explained in 1 episode
Accuracy — 10× in this paper · explained in 1 episode
Transformer Models — 9× in this paper · explained in 2 episodes
Decode — 8× in this paper · explained in 2 episodes
Unexplained Variance — 8× in this paper
Reconstruction Error — 5× in this paper · explained in 2 episodes
Interpretability — 4× in this paper · explained in 15 episodes
Modularity — 4× in this paper · explained in 9 episodes
Affective Computing — 2× in this paper · explained in 1 episode
Linear probe — 2× in this paper · explained in 2 episodes
Affordance — 1× in this paper · explained in 1 episode
Alignment — 1× in this paper · explained in 1 episode
Core — 1× in this paper · explained in 2 episodes
Fidelity — 1× in this paper · explained in 2 episodes
Fine-Tuning — 1× in this paper · explained in 5 episodes
Multimodal Reasoning — 1× in this paper · explained in 5 episodes
Predictive Coding — 1× in this paper · explained in 2 episodes
Regularization — 1× in this paper · explained in 3 episodes
Representation Learning — 1× in this paper · explained in 2 episodes
Self-Supervised Learning — 1× in this paper · explained in 7 episodes
Semantic Understanding — 1× in this paper · explained in 2 episodes

Summary

This paper introduces the concept of an Encoding Probe as a method to reconstruct and quantify the contribution of various linguistic features—such as phonetic, acoustic, syntactic, and lexical information—within the internal hidden states of large language models. By moving Beyond Decodability, this technique allows researchers to assess how much specific feature knowledge is encoded within a model's representations by measuring the unexplained variance (UV) when that feature is ablated from the layer’s input. This capability provides deep insights into the structural composition of model knowledge, revealing which features are most robustly represented across different model architectures and training objectives.

Feature Representation and Probe Comparison

The study first establishes that the choice of feature representation significantly impacts probe success. When comparing results between the Encoding Probe and Decoding Probe settings, the authors observe that choosing the right feature representation of the features has a larger impact on the E NCODING P ROBE than on the D ECODING P ROBE. To demonstrate this, they test three different ways of representing phonetic information—including one-hot encoding and phone IDs—reporting results in Figure 6.

Direct Correlation Analysis of Identity Features

The authors investigate the direct linear correlations between speaker identity and various linguistic features. When fitting a ridge classifier using phonetic, acoustic, and combined features to predict speaker identity, they find that speaker identity is only weakly decodable from the explicit acoustic and phonetic features with a linear classifier. Furthermore, analysis of local acoustic descriptors reveals that there are almost no speaker characteristics that can be extracted from the local acoustic features.

Extended Model Analysis Across Architectures

The Encoding Probe analysis is extended across an extensive set of speech and text Transformer-based models, including architectures such as HuBERT, WavLM, RoBERTa, and ModernBERT. These runs cover different sizes (12-layer vs. 24-layer) and training objectives (e.g., Automatic Speech Recognition or Speaker Identification). Key observations include:

WavLM follows a broadly similar trend to wav2vec2 and HuBERT base models.
The ASR tuning objective consistently increases the contribution from both lexical and syntactic features in the large variants of both wav2vec2 and HuBERT.
In general, differences across models are attributable to representational differences rather than pipeline changes, as all extended-model runs use a consistent probing setup.

Syntactic and Lexical Feature Encoding

When analyzing static word embeddings, the direct correlation results indicate that syntactic category labels (e.g. partof-speech) are more decodable from the word embeddings than the other syntactic features. Overall, these findings suggest that static word embeddings are not directly correlated with most of the syntactic features we include in this study. Regarding syntax and lexicon generally, the authors confirm a similar trend across model sizes: there are noticeable differences at specific layers (e.g., last three layers for wav2vec2 base and large models).

Improvements for AI systems

1. Enhanced Feature Representation Module (FRM) for Probing:

Improvement: Develop a dynamic, meta-learning module that automatically evaluates the relative importance and optimal transformation type (e.g., one-hot encoding vs. probability distribution vs. raw embedding) of different input feature modalities (acoustic, phonetic, syntactic). This module should move beyond simple comparison by quantifying the synergistic contribution of combined features rather than just individual contributions (Feature A + Feature B vs. Feature A times Feature B).
Improved Capability: The system can generate a comprehensive feature representation heatmap for any given task and model architecture, advising the user not just on which features to use, but how they should be combined mathematically to maximize decodability (e.g., recommending a weighted attention mechanism over simple concatenation).

2. Hierarchical/Multi-Scale Feature Fusion Architecture:

Improvement: Instead of relying solely on localized descriptors (like the 20ms segments mentioned), implement a multi-scale fusion layer that explicitly integrates information across different temporal granularities. This requires building specialized decoders that operate simultaneously on:

High-frequency, short-term acoustic features (e.g., 20ms frames).
Mid-frequency, phonetic/subword units (e.g., 100ms phonemes).
Low-frequency, global context features (e.g., full utterance speaker characteristics).

Improved Capability: The system can perform robust longitudinal decoding of identity and linguistic features, overcoming the limitation that local acoustic descriptors may obscure global speaker or emotional characteristics. This is critical for improving performance in noisy or highly variable acoustic environments.

3. Cross-Domain, Task-Adaptive Probing Framework:

Improvement: Generalize the probing methodology (like the ablative encoding analysis) into a unified framework that can be applied to novel, unseen feature types and tasks without manual re-engineering. This involves creating a modular Probing API that accepts any feature embedding and automatically runs comparative tests across various decoder structures (linear, MLP, attention-based).
Improved Capability: The system can predict the potential decodability of features derived from completely new modalities (e.g., physiological signals like PPGs, or novel sensory inputs) simply by analyzing their structural relationship to known speech embeddings and predicting the optimal probing setup for minimal resource waste.

4. Integrated Model Selection and Fine-Tuning Guidance:

Improvement: Develop a diagnostic layer that analyzes the performance trends across multiple base models (e.g., wav2vec2-base vs. large, HuBERT vs. WavLM) and provides actionable recommendations for pre-training objectives and fine-tuning strategies based on the target task's specific bottleneck (e.g., if syntactic features are weak, recommend fine-tuning with a task that explicitly forces syntactic dependency prediction).
Improved Capability: The system acts as a Model Architect Consultant, reducing the need for extensive hyperparameter search. Given a target performance metric and limited compute budget, it recommends the optimal model size, pre-training objective (PT/ASR/SID), and necessary feature augmentation to achieve near-optimal results.

5. Addressing Low Decodability Limitations:

Improvement: When probing reveals that a feature (like speaker identity) is only weakly decodable from explicit features, the system must pivot its approach from decoding to latent space reconstruction. This involves training an auxiliary generative model (e.g., a Variational Autoencoder or GAN) whose latent dimensions are explicitly constrained by the target feature (e.g., speaker ID).
Improved Capability: Instead of failing due to lack of correlation, the system can synthesize highly effective, discriminative representations for weak signals. It learns to generate synthetic acoustic/phonetic embeddings that contain the missing speaker or syntactic information, effectively boosting data scarcity and improving generalization performance dramatically.