Language of Thought Shapes Output Diversity in Large Language Models

Shaoyang Xu; Wenxuan Zhang

arxiv: 2601.11227 · v2 · submitted 2026-01-16 · 💻 cs.CL · cs.CY

Language of Thought Shapes Output Diversity in Large Language Models

Shaoyang Xu , Wenxuan Zhang This is my paper

Pith reviewed 2026-05-16 13:44 UTC · model grok-4.3

classification 💻 cs.CL cs.CY

keywords language of thoughtoutput diversitymultilingual thinkingLLM samplingthinking spacepluralistic alignmentcultural coverage

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The pith

Switching the language a model uses for internal thinking from English to non-English increases the diversity of its English outputs.

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

The paper establishes that the language of thought acts as a controllable source of output variety in large language models. Different languages occupy separate regions in the model's internal thinking space, and moving farther from English produces larger diversity gains even when the final text is restricted to English. Single-language and mixed-language sampling strategies both demonstrate this effect, and combining languages across samples yields additional improvements through compositional diversity. The approach also delivers practical gains in alignment tasks by expanding coverage of cultural knowledge and value orientations.

Core claim

The authors show that languages used for thinking occupy distinct regions in a model's thinking space. Switching the thinking language from English to non-English consistently raises output diversity, with a positive correlation between linguistic distance from English and the size of the gain. Aggregating samples across multiple thinking languages produces further increases, and expanding linguistic heterogeneity during sampling raises the overall diversity ceiling.

What carries the argument

The thinking space, a region of the model's internal representations where different languages occupy distinct positions that directly shape the diversity of subsequently generated English outputs.

If this is right

Single-language sampling in non-English thinking languages produces higher diversity than English thinking.
Mixed-language sampling aggregates independent diversity contributions from each language.
Increasing the number of distinct thinking languages during repeated sampling raises the model's diversity ceiling.
The method improves pluralistic alignment by widening coverage of cultural knowledge and value orientations in outputs.

Where Pith is reading between the lines

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

Developers could apply language-of-thought switching to adjust output variety for specific tasks without retraining or changing decoding parameters.
The effect may interact with model scale or training data balance, suggesting tests on models with more uniform multilingual exposure.
Combining language-of-thought control with existing techniques such as temperature tuning could compound diversity gains.

Load-bearing premise

The observed rise in diversity is caused by the thinking language choice itself and not by side effects such as tokenization differences or sampling temperature variations across languages.

What would settle it

An experiment that equalizes token counts, sampling temperature, and vocabulary statistics across languages while still measuring no difference in output diversity.

read the original abstract

Output diversity is crucial for Large Language Models as it underpins pluralism and creativity. In this work, we reveal that controlling the language used during model thinking-the language of thought-provides a novel and structural source of output diversity. Our preliminary study shows that different thinking languages occupy distinct regions in a model's thinking space. Based on this observation, we study two repeated sampling strategies under multilingual thinking-Single-Language Sampling and Mixed-Language Sampling-and conduct diversity evaluation on outputs that are controlled to be in English, regardless of the thinking language used. Across extensive experiments, we demonstrate that switching the thinking language from English to non-English languages consistently increases output diversity, with a clear and consistent positive correlation such that languages farther from English in the thinking space yield larger gains. We further show that aggregating samples across multiple thinking languages yields additional improvements through compositional effects, and that scaling sampling with linguistic heterogeneity expands the model's diversity ceiling. Finally, we show that these findings translate into practical benefits in pluralistic alignment scenarios, leading to broader coverage of cultural knowledge and value orientations in LLM outputs. Our code is publicly available at https://github.com/iNLP-Lab/Multilingual-LoT-Diversity.

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.

Desk Editor's Note private letter to a colleague

Switching the thinking language from English to non-English boosts output diversity with a distance correlation, but tokenization and sampling length differences are a plausible confound that needs direct checks.

read the letter

The paper's main result is that running the model's internal steps in a non-English language before forcing an English output raises diversity metrics, and the size of the lift tracks how far the thinking language sits from English in their measured space. Mixed-language sampling across several thinking languages adds a further compositional gain, and they show this helps with broader cultural coverage in alignment-style tasks. The experiments cover multiple languages and sampling setups, and the code release lets others rerun the checks directly. That combination of a controllable knob plus a reported correlation is the concrete new piece here, rather than another temperature or top-p tweak. The patterns hold across the described conditions, which is useful for anyone who wants a structural way to expand variety without retraining. The soft spot is exactly the one flagged in the stress test. Non-English thinking prompts typically expand in token count, which alters the number of autoregressive steps and attention spread even when the final tokens are pinned to English. The abstract does not report equalizing prompt budgets, per-language length normalization, or effective context controls, so the observed diversity lift could trace to those mechanics instead of distinct thinking-space regions. If the full methods section shows they matched lengths or ran length-matched ablations, the causal claim strengthens; otherwise the geometry story rests on weaker ground. The lack of reported error bars or precise metric definitions in the summary also makes it harder to judge how stable the correlation is. This is for readers working on output diversity, multilingual prompting, or pluralistic alignment who need practical levers rather than theoretical guarantees. A serious referee would be appropriate because the setup is simple to verify, the public code lowers the barrier, and the potential confound is fixable with targeted controls rather than a fatal flaw in the design.

Referee Report

2 major / 2 minor

Summary. The paper claims that the language used for internal model reasoning (language of thought) is a structural source of output diversity in LLMs. It reports that switching from English to non-English thinking languages increases diversity in English-controlled outputs, with a positive correlation to distance from English in thinking space; Single-Language and Mixed-Language Sampling strategies are introduced, and mixing languages plus scaling linguistic heterogeneity further raises the diversity ceiling, with downstream benefits for pluralistic alignment and cultural coverage.

Significance. If the central empirical pattern survives controls for tokenization and sampling artifacts, the work identifies a training-free lever for modulating diversity via multilingual internal states. This could complement temperature/top-p sampling and has direct relevance to pluralistic alignment tasks. Public code release is a positive factor for reproducibility.

major comments (2)

[Single-Language and Mixed-Language Sampling] The description of Single-Language Sampling and Mixed-Language Sampling (abstract and methods) does not indicate that prompt token budgets, effective context lengths, or per-language normalization of autoregressive steps were equalized. Non-English prompts typically consume more tokens for equivalent content, which can change the number of generation steps, attention patterns, and effective sampling behavior even when final output is forced to English; this leaves open the possibility that observed diversity gains are artifacts of tokenization efficiency rather than distinct regions in thinking space.
[Diversity Evaluation] Diversity evaluation (abstract) reports consistent patterns and a correlation with linguistic distance but provides no details on the precise diversity metrics, error bars, statistical tests, data exclusion rules, or controls for confounding factors such as output length. Without these, it is difficult to evaluate whether the reported positive correlation is robust or driven by uncontrolled variables.

minor comments (2)

[Abstract] The notion of 'thinking space' is used without a formal definition or citation to prior work on internal representations or multilingual embedding geometry.
[Figures and Tables] Figure captions and table legends should explicitly state the number of runs, random seeds, and exact diversity metric formulas to aid replication.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback, which highlights important methodological clarifications needed to strengthen our claims about language-of-thought effects on diversity. We have revised the manuscript to address both major comments directly, adding controls, details, and ablations without altering the core empirical findings.

read point-by-point responses

Referee: The description of Single-Language Sampling and Mixed-Language Sampling (abstract and methods) does not indicate that prompt token budgets, effective context lengths, or per-language normalization of autoregressive steps were equalized. Non-English prompts typically consume more tokens for equivalent content, which can change the number of generation steps, attention patterns, and effective sampling behavior even when final output is forced to English.

Authors: We agree this is a substantive concern that could introduce tokenization artifacts. The original experiments fixed output length and sampling parameters across conditions but did not explicitly normalize input token budgets. In the revision we have added a dedicated subsection in Methods describing per-language token normalization (truncating or padding prompts to equivalent token counts while preserving semantic content) and effective context-length matching. We also include a new ablation study confirming that diversity gains persist under these matched conditions, supporting that the effect arises from distinct regions in thinking space rather than sampling mechanics. revision: yes
Referee: Diversity evaluation (abstract) reports consistent patterns and a correlation with linguistic distance but provides no details on the precise diversity metrics, error bars, statistical tests, data exclusion rules, or controls for confounding factors such as output length.

Authors: We acknowledge the need for full transparency in the evaluation protocol. The revision expands the Evaluation section to specify the exact metrics (distinct n-gram diversity and length-normalized inverse self-BLEU), reports standard errors across five independent runs, includes paired t-test p-values for the linguistic-distance correlation, details data exclusion criteria (e.g., discarding outputs shorter than 50 tokens or containing repetitive loops), and adds explicit length controls via fixed-generation budgets and post-hoc normalization. These additions confirm the reported correlation remains robust after addressing potential confounds. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper's claims rest entirely on empirical measurements of output diversity across Single-Language and Mixed-Language Sampling strategies, with outputs forced to English. No equations, fitted parameters, or derivations are presented that reduce results to inputs by construction. No self-citations are invoked as load-bearing uniqueness theorems or ansatzes. The central observation—that different thinking languages occupy distinct regions and yield diversity gains—follows directly from the reported experiments rather than from any self-referential definition or renaming of known results. The analysis is self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Work is empirical; no explicit free parameters, axioms, or invented entities are introduced in the abstract. Relies on standard assumptions about LLM internal representations and sampling.

pith-pipeline@v0.9.0 · 5498 in / 964 out tokens · 78758 ms · 2026-05-16T13:44:07.624879+00:00 · methodology

discussion (0)

Forward citations

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