Do Factual Recall Mechanisms Carry over from Text to Speech in Multimodal Language Models?

Luca Modica, Filip Landin, Mehrdad Farahani, Livia Qian, Gabriel Skantze, Richard Johansson · May 21, 2026 · Citations: 0

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What to verify

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Evidence quality

Low

Derived from extracted protocol signals and abstract evidence.

Abstract

In recent years, several Speech Language Models (SLMs) that represent speech and written text jointly have been presented. The question then emerges about how model-internal mechanisms are similar and different when operating in the two modalities. We focus on how these systems encode, store, and retrieve factual knowledge, which has previously been investigated for text-only models. To investigate mechanisms behind the storage and recall of factual association in SLMs, we leverage Causal Mediation Analysis, a technique previously applied to text-based models. Initial results using SpiritLM, a multimodal model integrating discrete speech tokens reveal discrepancies between text-to-text and speech-to-text results, suggesting that the emergent mechanisms for factual recall are only partially carried over from the text to the speech modality. These results advance our understanding of how internal mechanisms encode factual associations in SLMs while contributing insights for improving speech-enabled AI systems.

Abstract-only analysis — low confidence

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The available metadata is too thin to trust this as a primary source.
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Should You Rely On This Paper?

This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.

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Background context only

Use if you need

Background context only.

Main weakness

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Trust level

Low

Usefulness score

0/100 • Low

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Human Feedback Signal

Not explicit in abstract metadata

Evaluation Signal

Weak / implicit signal

Usefulness for eval research

Adjacent candidate

Extraction confidence 20%

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What We Could Verify

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Human Feedback Types

missing

None explicit

No explicit feedback protocol extracted.

"In recent years, several Speech Language Models (SLMs) that represent speech and written text jointly have been presented."

Evaluation Modes

missing

None explicit

Validate eval design from full paper text.

"In recent years, several Speech Language Models (SLMs) that represent speech and written text jointly have been presented."

Quality Controls

missing

Not reported

No explicit QC controls found.

"In recent years, several Speech Language Models (SLMs) that represent speech and written text jointly have been presented."

Benchmarks / Datasets

missing

Not extracted

No benchmark anchors detected.

"In recent years, several Speech Language Models (SLMs) that represent speech and written text jointly have been presented."

Reported Metrics

partial

Recall

Useful for evaluation criteria comparison.

"To investigate mechanisms behind the storage and recall of factual association in SLMs, we leverage Causal Mediation Analysis, a technique previously applied to text-based models."

Human Feedback Details

Uses human feedback: No
Feedback types: None
Rater population: Not reported
Expertise required: General

Evaluation Details

Evaluation modes:
Agentic eval: None
Quality controls: Not reported
Evidence quality: Low
Use this page as: Background context only

Protocol And Measurement Signals

Benchmarks / Datasets

No benchmark or dataset names were extracted from the available abstract.

Reported Metrics

recall

Research Brief

Metadata summary

In recent years, several Speech Language Models (SLMs) that represent speech and written text jointly have been presented.

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key Takeaways

In recent years, several Speech Language Models (SLMs) that represent speech and written text jointly have been presented.
The question then emerges about how model-internal mechanisms are similar and different when operating in the two modalities.
We focus on how these systems encode, store, and retrieve factual knowledge, which has previously been investigated for text-only models.

Researcher Actions

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Caveats

Generated from abstract + metadata only; no PDF parsing.
Signals below are heuristic and may miss details reported outside the abstract.

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Research Summary

Contribution Summary

In recent years, several Speech Language Models (SLMs) that represent speech and written text jointly have been presented.
The question then emerges about how model-internal mechanisms are similar and different when operating in the two modalities.
We focus on how these systems encode, store, and retrieve factual knowledge, which has previously been investigated for text-only models.

Why It Matters For Eval

Abstract shows limited direct human-feedback or evaluation-protocol detail; use as adjacent methodological context.

Researcher Checklist

Gap: Human feedback protocol is explicit

No explicit human feedback protocol detected.
Gap: Evaluation mode is explicit

No clear evaluation mode extracted.
Gap: Quality control reporting appears

No calibration/adjudication/IAA control explicitly detected.
Gap: Benchmark or dataset anchors are present

No benchmark/dataset anchor extracted from abstract.
Pass: Metric reporting is present

Detected: recall

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