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arxiv: 2509.13316 · v4 · pith:ZGLP37XFnew · submitted 2025-09-16 · 💻 cs.CL · cs.LG

Do Activation Verbalization Methods Convey Privileged Information?

Millicent Li , Alberto Mario Ceballos Arroyo , Giordano Rogers , Naomi Saphra , Byron C. Wallace This is my paper

Pith reviewed 2026-05-18 15:36 UTC · model grok-4.3

classification 💻 cs.CL cs.LG
keywords activation verbalizationLLM interpretabilityprivileged informationverbalizer LLMtarget model internalsbenchmarkscontrolled experiments
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The pith

Activation verbalization methods typically convey the verbalizer LLM's parametric knowledge rather than privileged information about the target model's internals.

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

The paper investigates whether translating LLM activations into natural language using a second verbalizer model provides unique insights into the target model's internal representations. It evaluates popular methods and datasets from prior work and shows that strong benchmark performance is achievable without any access to target model activations. Controlled experiments demonstrate that the resulting verbalizations more often reflect the verbalizer LLM's own encoded knowledge. These findings point to the need for improved benchmarks and controls when assessing whether such methods reveal how target models actually operate.

Core claim

Activation verbalization approaches do not necessarily convey privileged knowledge about the internal workings of the target LLM. Instead, verbalizations frequently reflect the parametric knowledge of the verbalizer LLM that generates them, and existing benchmarks can be solved without using the target model's activations at all.

What carries the argument

Controlled experiments that isolate verbalizer parametric knowledge and input features from target model activations to measure their separate contributions to verbalization outputs.

If this is right

  • Existing benchmarks from prior work do not require target model internals to achieve high performance.
  • Verbalizations often capture the verbalizer LLM's knowledge or input properties instead of target model internals.
  • New benchmarks and experimental controls are needed to test whether verbalization methods yield meaningful insights into target LLM operations.
  • Current performance metrics on verbalization tasks cannot be taken as evidence of access to target model representations.

Where Pith is reading between the lines

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

  • Future verbalization work could include explicit controls that subtract or match the verbalizer's parametric knowledge against the target.
  • The same concern about secondary-model leakage may apply to other interpretability techniques that decode representations with an auxiliary model.
  • Experiments using target and verbalizer models with deliberately mismatched training data could provide a stronger test of the separation.
  • Better isolation of activation information could improve the reliability of these methods for model debugging and analysis.

Load-bearing premise

The datasets and benchmarks from prior work can distinguish information that comes from target model internals from information already available in the inputs or the verbalizer model.

What would settle it

A result in which verbalizations produced with target activations differ in specific, measurable respects from those produced by verbalizer-only or input-only baselines, in ways that track unique target model behaviors.

Figures

Figures reproduced from arXiv: 2509.13316 by Alberto Mario Ceballos Arroyo, Byron C. Wallace, Giordano Rogers, Millicent Li, Naomi Saphra.

Figure 1
Figure 1. Figure 1: Two ways that a verbalizer (M2) might describe an activation. In our preferred scenario (a), the description employs privileged information beyond what is accessible in the input (xinput), so the country of origin for Alice can be determined from the target (M1) model’s activations. Alternatively, (b) verbalization may give no privileged insights into the operations of M1 since M2 may only be accessing inp… view at source ↗
Figure 2
Figure 2. Figure 2: Two ways of verbalizing descriptions of model activations. In (a), [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: We use the following setup to assess whether verbalization techniques communicate priv [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: We show the effect of using an xprompt that is semantically similar or adversarial. We average across all tasks and tested prompts for space; see Appendix Subsection H.4 for the full prompt and task breakdown. Our key finding is shown above. In Appendix [PITH_FULL_IMAGE:figures/full_fig_p033_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: We show the effects of small prompt manipulations. For both LIT and [PITH_FULL_IMAGE:figures/full_fig_p034_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: We show the significant effect of adding prompt distractors, with incorrect labels, to [PITH_FULL_IMAGE:figures/full_fig_p035_6.png] view at source ↗
read the original abstract

Recent interpretability methods have proposed to translate LLM internal representations into natural language descriptions using a second verbalizer LLM. This is intended to illuminate how the target model represents and operates on inputs. But do such activation verbalization approaches actually provide privileged knowledge about the internal workings of the target model, or do they merely convey information about the inputs provided to it? We critically evaluate popular verbalization methods and datasets used in prior work and find that one can perform well on such benchmarks without access to target model internals, suggesting that these datasets are not ideal for evaluating verbalization methods. We then run controlled experiments which reveal that verbalizations often reflect the parametric knowledge of the verbalizer LLM that generated them, rather than the knowledge of the target LLM whose activations are decoded. Taken together, our results indicate a need for targeted benchmarks and experimental controls to rigorously assess whether verbalization methods provide meaningful insights into the operations of LLMs.

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

2 major / 2 minor

Summary. The paper claims that activation verbalization methods, which employ a second LLM to translate target LLM activations into natural language descriptions, do not reliably convey privileged information about the target model's internal representations. Instead, strong performance on existing benchmarks is possible without target activations, and controlled experiments indicate that the verbalizations primarily reflect the verbalizer LLM's parametric knowledge or input information rather than target internals.

Significance. If substantiated, the result would be moderately significant for LLM interpretability research by exposing limitations in current verbalization techniques and motivating the creation of more rigorous benchmarks with better controls for verbalizer knowledge. The manuscript's empirical comparisons to prior benchmarks and use of controlled experiments isolating verbalizer effects are strengths that ground the critique in falsifiable tests.

major comments (2)
  1. [§4] §4, Baselines without target activations: the claim that prior datasets are unsuitable rests on these results showing competitive performance; the section should report exact metrics (e.g., accuracy or F1), number of runs, and statistical significance tests to confirm the baselines truly undermine the benchmarks.
  2. [§5.1] §5.1, Controlled experiments: the finding that verbalizations reflect verbalizer parametric knowledge rather than target knowledge is central; clarify the exact procedure for attributing knowledge sources (e.g., via ablation of activation input or comparison to verbalizer-only prompts) and report effect sizes.
minor comments (2)
  1. [Introduction] Introduction: expand the discussion of specific prior verbalization papers with one additional sentence on their claimed advantages to better frame the critique.
  2. [Table 1] Table 1: ensure column headers explicitly label whether rows include target activations or not for immediate clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback and the recommendation of minor revision. We address each major comment below and will update the manuscript accordingly to improve clarity and rigor.

read point-by-point responses

  1. Referee: [§4] §4, Baselines without target activations: the claim that prior datasets are unsuitable rests on these results showing competitive performance; the section should report exact metrics (e.g., accuracy or F1), number of runs, and statistical significance tests to confirm the baselines truly undermine the benchmarks.

    Authors: We agree that additional reporting details would strengthen the section. While the manuscript already demonstrates competitive performance of baselines without target activations, we will revise §4 to explicitly include the exact accuracy and F1 scores, the number of runs performed, and results from statistical significance tests (such as paired t-tests) to more rigorously support the conclusion that these datasets are unsuitable for evaluating verbalization methods. revision: yes

  2. Referee: [§5.1] §5.1, Controlled experiments: the finding that verbalizations reflect verbalizer parametric knowledge rather than target knowledge is central; clarify the exact procedure for attributing knowledge sources (e.g., via ablation of activation input or comparison to verbalizer-only prompts) and report effect sizes.

    Authors: We appreciate the suggestion to enhance clarity here. The experiments in §5.1 attribute knowledge sources through systematic ablations: we compare verbalizations and task performance when the verbalizer receives target activations versus when activations are replaced by random noise or omitted entirely, and versus verbalizer-only prompts that receive only the raw input text. We will expand the description of this procedure in the revised manuscript and add effect sizes (e.g., Cohen's d for performance differences) to quantify the dominance of verbalizer parametric knowledge. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical evaluation is self-contained

full rationale

The paper advances its claims through direct empirical tests, including baselines that achieve strong performance without target model activations and controlled experiments isolating verbalizer parametric knowledge. These steps rely on comparisons to external prior benchmarks and new experimental controls rather than any derivation, equation, or self-citation that reduces the result to its own inputs by construction. No self-definitional steps, fitted predictions, or load-bearing self-citations appear in the abstract or described methodology; the work is therefore independent of the circularity patterns enumerated in the guidelines.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the domain assumption that existing verbalization benchmarks are meant to measure privileged internal information; no free parameters or invented entities are introduced in the abstract description.

axioms (1)
  • domain assumption Existing verbalization benchmarks and datasets are designed to evaluate whether methods convey privileged information from target model activations.
    The paper's critique of these benchmarks as inadequate depends on this premise about their intended purpose.

pith-pipeline@v0.9.0 · 5693 in / 1117 out tokens · 38894 ms · 2026-05-18T15:36:20.433463+00:00 · methodology

discussion (0)

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Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Masked by Consensus: Disentangling Privileged Knowledge in LLM Correctness

    cs.CL 2026-04 unverdicted novelty 7.0

    LLMs exhibit domain-specific privileged knowledge in hidden states for factual correctness but not math reasoning, visible only on model disagreement subsets.

  2. Shared Lexical Task Representations Explain Behavioral Variability In LLMs

    cs.CL 2026-04 unverdicted novelty 5.0

    LLMs share task-specific attention heads across prompting styles, with activation strength explaining performance differences and failures arising from competing representations.

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