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arxiv: 2604.06356 · v1 · submitted 2026-04-07 · 💻 cs.CL · cs.AI

Recognition: 2 theorem links

· Lean Theorem

In-Context Learning in Speech Language Models: Analyzing the Role of Acoustic Features, Linguistic Structure, and Induction Heads

Charlotte Pouw , Hosein Mohebbi , Afra Alishahi , Willem Zuidema
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Pith reviewed 2026-05-10 20:10 UTC · model grok-4.3

classification 💻 cs.CL cs.AI
keywords in-context learningspeech language modelsinduction headstext-to-speechacoustic featuresablationsspeaking rate
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The pith

Induction heads causally enable in-context learning in speech language models, with speaking rate as the dominant acoustic feature that gets both inferred and reproduced.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper explores in-context learning in speech language models by using a text-to-speech task to probe how models learn from demonstrations. It separates the effects of acoustic features like speaking rate, pitch, and intensity on both task inference and style mimicry. The results show that speaking rate has a strong effect on performance and is copied in outputs, while pitch range and intensity do not. The central finding is that ablating the top induction heads completely eliminates the in-context learning ability, similar to observations in text models. A sympathetic reader would care because this extends our understanding of ICL mechanisms beyond text to spoken language processing.

Core claim

Using a TTS task, the model must infer the correct spoken content from demonstration examples and also mimic their acoustic properties in its generated speech. Linguistic structure in demonstrations aids task inference, but among acoustic features only speaking rate strongly influences both accuracy and reproduction in outputs. Ablating the model's top-k induction heads removes its ability to perform in-context learning altogether.

What carries the argument

Induction heads, which are specific attention heads that detect and replicate patterns from in-context demonstrations, playing a causal role in enabling the model's ICL behavior in speech processing.

If this is right

  • Speech language models rely on the same induction head mechanisms for ICL as text-only models.
  • Prompt design for speech tasks should prioritize controlling speaking rate to improve performance and consistency.
  • Targeted ablation or editing of induction heads could be used to control or disable ICL in speech models.
  • Linguistic structure in demonstrations contributes to task inference independently of acoustic cues.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Similar ablation experiments could be applied to other multimodal models to test if induction heads generalize across modalities.
  • These results point to the possibility of localizing ICL capabilities to specific heads for more controllable speech model behavior.
  • Feature-specific mimicry findings could inform evaluation benchmarks that separately measure task inference and acoustic copying.

Load-bearing premise

The text-to-speech task setup and acoustic feature manipulations accurately isolate in-context learning effects without being confounded by the model's pretraining data or other dataset-specific factors.

What would settle it

An experiment in which ablating the top induction heads leaves the model's ICL performance intact, or where pitch range or intensity manipulations produce strong effects on accuracy and mimicry comparable to speaking rate.

Figures

Figures reproduced from arXiv: 2604.06356 by Afra Alishahi, Charlotte Pouw, Hosein Mohebbi, Willem Zuidema.

Figure 1
Figure 1. Figure 1: Our experimental setup: we construct ICL prompts and apply different linguistic [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: ICL performance of SpiritLM given demonstrations synthesized by KokoroTTS. [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Acoustic features of SpiritLM speech outputs given different demonstration types. [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Identifying and ablating induction heads in SpiritLM. [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Word Error Rate of different Whisper model sizes on various speech types. [PITH_FULL_IMAGE:figures/full_fig_p013_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: ICL performance of SpiritLM given demonstrations synthesized by SpeechT5. [PITH_FULL_IMAGE:figures/full_fig_p013_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Prefix-matching scores for each head in SpiritLM on sequences of random tokens [PITH_FULL_IMAGE:figures/full_fig_p014_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Attention to prefix-matching and non-prefix-matching tokens (speech vs. text) by [PITH_FULL_IMAGE:figures/full_fig_p014_8.png] view at source ↗
read the original abstract

In-Context Learning (ICL) has been extensively studied in text-only Language Models, but remains largely unexplored in the speech domain. Here, we investigate how linguistic and acoustic features affect ICL in Speech Language Models. We focus on the Text-to-Speech (TTS) task, which allows us to analyze ICL from two angles: (1) how accurately the model infers the task from the demonstrations (i.e., generating the correct spoken content), and (2) to what extent the model mimics the acoustic characteristics of the demonstration speech in its output. We find that speaking rate strongly affects ICL performance and is also mimicked in the output, whereas pitch range and intensity have little impact on performance and are not consistently reproduced. Finally, we investigate the role of induction heads in speech-based ICL and show that these heads play a causal role: ablating the top-k induction heads completely removes the model's ICL ability, mirroring findings from text-based ICL.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 3 minor

Summary. The paper claims that in-context learning (ICL) in speech language models can be studied via a text-to-speech (TTS) task, where speaking rate strongly influences both task inference accuracy and acoustic mimicry in outputs, while pitch range and intensity have little effect and are not consistently reproduced. It further claims that induction heads play a causal role in speech-based ICL, as ablating the top-k induction heads completely removes the model's ICL ability, mirroring text-based findings.

Significance. If the results hold with appropriate controls, this work is significant for extending ICL research from text to speech domains and for using the TTS task to separately probe linguistic content inference and acoustic feature reproduction. The experimental use of feature manipulations and targeted ablations provides a falsifiable approach that strengthens the analysis. The parallel to text-based induction head findings, if causally supported, would indicate a modality-general mechanism.

major comments (1)
  1. [induction heads ablation experiments] In the induction heads ablation experiments (the section detailing the causal role of induction heads), the claim that ablating the top-k induction heads completely removes ICL ability lacks necessary controls for specificity. No results are reported for ablating matched numbers of random or non-induction heads, leaving open whether the collapse reflects selective loss of induction or nonspecific degradation of attention circuits and generation quality. This is load-bearing for the central causal conclusion and requires additional ablation baselines to secure the interpretation.
minor comments (3)
  1. [Abstract] The abstract omits details on the specific speech LM, demonstration set sizes, statistical tests, and controls, which limits initial assessment of whether the data support the claims.
  2. [acoustic feature results] The quantification of acoustic mimicry (e.g., how speaking rate or pitch reproduction is measured in outputs) should reference explicit metrics or equations for reproducibility.
  3. [experimental setup] Including zero-shot TTS baselines would help contextualize the reported ICL gains and rule out general task performance effects.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive comments. The major concern regarding controls in the induction heads ablation experiments is valid and will be addressed through additional experiments in the revision.

read point-by-point responses

  1. Referee: In the induction heads ablation experiments (the section detailing the causal role of induction heads), the claim that ablating the top-k induction heads completely removes ICL ability lacks necessary controls for specificity. No results are reported for ablating matched numbers of random or non-induction heads, leaving open whether the collapse reflects selective loss of induction or nonspecific degradation of attention circuits and generation quality. This is load-bearing for the central causal conclusion and requires additional ablation baselines to secure the interpretation.

    Authors: We agree that the current ablation results lack the necessary specificity controls. The manuscript reports only the effect of ablating the top-k induction heads without baseline comparisons to random heads or non-induction heads. To strengthen the causal claim, we will conduct and report additional experiments ablating matched numbers of randomly selected heads and heads not identified as induction heads. These controls will be added to the revised manuscript to demonstrate that the loss of ICL ability is specific to induction heads rather than a general degradation of model performance. revision: yes

Circularity Check

0 steps flagged

No significant circularity; claims rest on experimental ablations

full rationale

The paper derives its central result—that ablating top-k induction heads removes ICL ability—from direct experimental interventions (feature manipulations in TTS demonstrations and targeted head ablations) rather than from any self-definitional loop, fitted parameter renamed as prediction, or load-bearing self-citation. Induction heads are identified via attention-pattern criteria imported from prior text-ICL literature and then causally tested by ablation; the outcome (loss of content accuracy and acoustic mimicry) is measured independently of the identification step. No equation or claim reduces to its own inputs by construction, and the TTS operationalization of ICL (content accuracy plus acoustic fidelity) is defined externally to the ablation result. This is the standard non-circular pattern for ablation studies.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The study is empirical and does not introduce mathematical free parameters, axioms, or new entities in the provided abstract.

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Reference graph

Works this paper leans on

17 extracted references · 7 canonical work pages · 3 internal anchors

  1. [1]

    doi: 10.18653/v1/2023.acl-long.660

    As- sociation for Computational Linguistics. doi: 10.18653/v1/2023.acl-long.660. URL https://aclanthology.org/2023.acl-long.660/. Paul Boersma and David Weenink. Praat: doing phonetics by computer [Computer pro- gram],

    work page doi:10.18653/v1/2023.acl-long.660 2023

  2. [2]

    URLhttps://hadoop.apache.org. Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Si...

    1901

  3. [3]

    Stephanie Chan, Adam Santoro, Andrew Lampinen, Jane Wang, Aaditya Singh, Pierre Richemond, James McClelland, and Felix Hill

    URL https://proceedings.neurips.cc/paper files/paper/2020/ file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf. Stephanie Chan, Adam Santoro, Andrew Lampinen, Jane Wang, Aaditya Singh, Pierre Richemond, James McClelland, and Felix Hill. Data distributional properties drive emergent in-context learning in transformers.Advances in neural information processing ...

    2020

  4. [4]

    Joy Crosbie and Ekaterina Shutova

    URLhttps://api.semanticscholar.org/CorpusID:1237448. Joy Crosbie and Ekaterina Shutova. Induction heads as an essential mechanism for pattern matching in in-context learning. InFindings of the Association for Computational Linguistics: NAACL 2025, pp. 5034–5096,

    2025

  5. [5]

    ISBN 979-8-89176-251-0

    Association for Computational Linguistics. ISBN 979-8-89176-251-0. doi: 10.18653/v1/2025.acl-long.682. URLhttps://aclanthology.org/2025.acl-long.682/. Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Jingyuan Ma, Rui Li, Heming Xia, Jingjing Xu, Zhiyong Wu, Baobao Chang, et al. A survey on in-context learning. InProceedings of the 2024 conference on empirical m...

    work page doi:10.18653/v1/2025.acl-long.682 2025

  6. [6]

    A primer on the inner workings of transformer-based language models.arXiv preprint arXiv:2405.00208, 2024

    https://transformer- circuits.pub/2021/framework/index.html. Javier Ferrando, Gabriele Sarti, Arianna Bisazza, and Marta Ruiz Costa-juss`a. A primer on the inner workings of transformer-based language models.ArXiv, abs/2405.00208,

    work page arXiv 2021

  7. [7]

    Do language models exhibit human- like structural priming effects? InFindings of the Association for Computational Linguistics: ACL 2024, pp

    Jaap Jumelet, Willem Zuidema, and Arabella Sinclair. Do language models exhibit human- like structural priming effects? InFindings of the Association for Computational Linguistics: ACL 2024, pp. 14727–14742,

    2024

  8. [8]

    Context is all you need? low-resource conversational asr profits from context, coming from the same or from the other speaker

    Julian Linke, Jana Winkler, and Barbara Schuppler. Context is all you need? low-resource conversational asr profits from context, coming from the same or from the other speaker. InProc. Interspeech 2025, pp. 3199–3203,

    2025

  9. [9]

    Jiachang Liu, Dinghan Shen, Yizhe Zhang, William B Dolan, Lawrence Carin, and Weizhu Chen. What makes good in-context examples for gpt-3? InProceedings of Deep Learning Inside Out (DeeLIO 2022): The 3rd workshop on knowledge extraction and integration for deep learning architectures, pp. 100–114,

    2022

  10. [10]

    Sewon Min, Xinxi Lyu, Ari Holtzman, Mikel Artetxe, Mike Lewis, Hannaneh Hajishirzi, and Luke Zettlemoyer. Rethinking the role of demonstrations: What makes in-context learning work? In Yoav Goldberg, Zornitsa Kozareva, and Yue Zhang (eds.),Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pp. 11048– 11064, Abu Dhabi, ...

    2022

  11. [11]

    Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?

    Association for Computational Linguistics. doi: 10.18653/v1/2022.emnlp-main.759. URL https://aclanthology.org/ 2022.emnlp-main.759/. Tu Anh Nguyen, Benjamin Muller, Bokai Yu, Marta R. Costa-jussa, Maha Elbayad, Sravya Popuri, Christophe Ropers, Paul-Ambroise Duquenne, Robin Algayres, Ruslan Mavlyu- tov, Itai Gat, Mary Williamson, Gabriel Synnaeve, Juan Pi...

    work page doi:10.18653/v1/2022.emnlp-main.759 2022

  12. [12]

    Lost in the Middle: How Language Models Use Long Contexts

    doi: 10.1162/tacl a 00728. URL https://aclanthology.org/2025.tacl-1.2/. Catherine Olsson, Nelson Elhage, Neel Nanda, Nicholas Joseph, Nova DasSarma, Tom Henighan, Ben Mann, Amanda Askell, Yuntao Bai, Anna Chen, et al. In-context learning and induction heads.arXiv preprint arXiv:2209.11895,

    work page internal anchor Pith review doi:10.1162/tacl 2025

  13. [13]

    Robust Speech Recognition via Large-Scale Weak Supervision

    URL https://arxiv.org/abs/2212.04356. Nathan Roll, Calbert Graham, Yuka Tatsumi, Kim Tien Nguyen, Meghan Sumner, and Dan Jurafsky. In-context learning boosts speech recognition via human-like adaptation to speakers and language varieties. InProceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pp. 4412–4426,

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  14. [14]

    LLaMA: Open and Efficient Foundation Language Models

    11 Preprint. Under review. Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timoth´ee Lacroix, Baptiste Rozi `ere, Naman Goyal, Eric Hambro, Faisal Azhar, et al. Llama: Open and efficient foundation language models.arXiv preprint arXiv:2302.13971,

    work page internal anchor Pith review Pith/arXiv arXiv

  15. [15]

    Can whisper perform speech-based in-context learning? InICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp

    Siyin Wang, Chao-Han Yang, Ji Wu, and Chao Zhang. Can whisper perform speech-based in-context learning? InICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 13421–13425. IEEE,

    2024

  16. [16]

    Is in-context learning a type of error-driven learning? evidence from the inverse frequency effect in structural priming

    Zhenghao Zhou, Robert Frank, and R Thomas McCoy. Is in-context learning a type of error-driven learning? evidence from the inverse frequency effect in structural priming. InProceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: human language technologies (volume 1: long papers), pp. 117...

    2025

  17. [17]

    spirit-lm-base-7b

    A Appendix A.1 SpiritLM Generation Setup •Model: –Spiritlm("spirit-lm-base-7b") •Generation configuration: –output modality = "speech" –max new tokens = 50 –do sample = False –seed = 42 A.2 Performance of Whisper Model Sizes across Speech Types Figure 5 shows the performance of different sizes of Whisper across speech types. We evaluated models on the dem...

    2022