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arxiv: 2605.27649 · v1 · pith:BCIHT6YRnew · submitted 2026-05-26 · 💻 cs.CL · cs.LG

Disentangling Language Roles in Multilingual LLM Task Execution

Qishi Zhan , Minxuan Hu , Seoyeon Jang , Lei Zhao , Ziheng Chen , Man Liang , Xinyue Xiang , Jiaxin Liu
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Guansu Wang Liang He
This is my paper

Pith reviewed 2026-06-29 18:13 UTC · model grok-4.3

classification 💻 cs.CL cs.LG
keywords multilingual LLMslanguage rolesinstruction followingbenchmark designresponse languagemismatch effectstask executioncrossed evaluation
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The pith

The language placed in the response slot drives most performance loss in multilingual LLM tasks, outweighing instruction or content mismatches.

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

The paper builds a benchmark that crosses every combination of three languages across the instruction, content, and response positions in a task. Results show that degradation follows the role a language fills rather than the sheer number of mismatches. Response-language choice produces the largest drops in correctness and adherence, and a lone response mismatch captures most of the effect while total mismatch count does not scale difficulty uniformly. Task families break along separate paths, so semantic scores alone miss the full picture of reliable execution.

Core claim

MTM-Bench defines each instance by the triplet (L_instr, L_content, L_resp) and enumerates all 27 combinations across English, Spanish, and Chinese for 2430 instances per model. Evaluation with decomposed metrics reveals that the response-language role organizes most variation in semantic correctness, target-language adherence, constraint satisfaction, and joint success. A single response-slot mismatch accounts for the bulk of degradation, mismatch count is not a monotonic predictor, model orderings shift with mismatch pattern, and the three task families fail through distinct channels.

What carries the argument

MTM-Bench, the fully crossed triplet (L_instr, L_content, L_resp) evaluated with separate scores for semantic correctness, language adherence, constraint satisfaction, contamination, and joint success.

If this is right

  • Response mismatch produces larger drops than instruction or content mismatch across the tested models.
  • Mismatch count does not predict difficulty in a single direction; model rankings change with which slots mismatch.
  • Semantic correctness alone misses language adherence and constraint failures that vary by task family.
  • Task families degrade through separate mechanisms rather than uniform semantic breakdown.

Where Pith is reading between the lines

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

  • Prompt engineering that prioritizes response-language matching could reduce failures more efficiently than full language alignment.
  • Training that weights response generation differently from instruction parsing might improve multilingual robustness.
  • The role asymmetry suggests that models internally separate language slots rather than treating them symmetrically.
  • Extending the crossed design to additional languages would test whether response dominance holds beyond the three studied here.

Load-bearing premise

Three languages and three task families are enough to expose general role effects without language-specific or task-specific artifacts taking over.

What would settle it

Repeating the design with a fourth language or fourth task family and finding either that response role no longer dominates degradation or that mismatch count becomes a monotonic predictor of difficulty would falsify the central claim.

Figures

Figures reproduced from arXiv: 2605.27649 by Guansu Wang, Jiaxin Liu, Lei Zhao, Liang He, Man Liang, Minxuan Hu, Qishi Zhan, Seoyeon Jang, Xinyue Xiang, Ziheng Chen.

Figure 1
Figure 1. Figure 1: Overview of MTM-Bench: benchmark construction, decomposed evaluation, and scoring validation. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Marginal effects of the three language roles on [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: JOINTSUCCESS by task family and response language, averaged over 20 models. Final-state ex￾traction is the main exception to the overall response￾language degradation pattern. 7.4 Task Families Expose Distinct Bottlenecks The three task families have similar overall JOINTSUCCESS (0.722–0.786; [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 5
Figure 5. Figure 5: Model-level sensitivity to the three language [PITH_FULL_IMAGE:figures/full_fig_p013_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: LANGCORRECT on Chinese-response in￾stances, sorted by model. sal model-level ordering. Across the 20 evaluated models, 12 perform worse on response-only triplets than on full-mismatch triplets, 7 show the opposite ordering, and 1 is tied or nearly tied. The aggregate full-minus-response-only difference is 0.011 un￾der clustered bootstrap resampling over base items. We therefore interpret this result as evi… view at source ↗
Figure 7
Figure 7. Figure 7: JOINTSUCCESS across the 27 triplets, grouped by mismatch class and ordered within each class by mean JOINTSUCCESS. Darker green indicates higher joint success; tick-label colors indicate response language. the main update or status change without contradic￾tion. Minor paraphrastic variation is allowed, and omission of secondary details is acceptable so long as the core update is preserved. E LLM-Assisted S… view at source ↗
Figure 8
Figure 8. Figure 8: Representative human-audit cases comparing automatic labels with adjudicated human labels for common [PITH_FULL_IMAGE:figures/full_fig_p017_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Representative output cases for language-purity tasks. Case 1 shows a Chinese-response item in which [PITH_FULL_IMAGE:figures/full_fig_p019_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Representative output cases for final-state extraction. Case 3 shows correct identification of the final [PITH_FULL_IMAGE:figures/full_fig_p020_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Representative output cases for final-state extraction and semantic reversal. Case 5 shows submission [PITH_FULL_IMAGE:figures/full_fig_p021_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Representative output cases for semantic reversal and full mismatch. Case 7 illustrates code-switching in [PITH_FULL_IMAGE:figures/full_fig_p022_12.png] view at source ↗
read the original abstract

Multilingual LLMs are increasingly used when instruction, source content, and required response languages do not coincide. Existing benchmarks have expanded multilingual instruction-following evaluation, but they rarely isolate these three roles within a fully crossed design. We introduce MTM-Bench, a controlled benchmark for language-conditioned task execution in which each instance is defined by a triplet \((L_{\text{instr}}, L_{\text{content}}, L_{\text{resp}})\). Across English, Spanish, and Chinese, MTM-Bench enumerates all 27 triplets and contains 2{,}430 instances per model across semantic reversal, final-state extraction, and language purity with update realization. We evaluate 20 frontier and open-weight LLMs using decomposed metrics for semantic correctness, target-language adherence, constraint satisfaction, contamination ratio, and joint success, with scoring validated by a targeted human audit. The fully crossed design reveals that degradation is organized by the role a language occupies in the task structure, not merely by mismatch count. The response-language role is the dominant axis of variation, and a single response-slot mismatch accounts for most degradation. The response-only and full-mismatch comparison suggests that mismatch count is not a monotonic predictor of difficulty, with model-level ordering varying across systems. Task families fail through distinct channels, showing that semantic correctness alone does not capture reliable multilingual task execution.

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 introduces MTM-Bench, a controlled benchmark enumerating all 27 (L_instr, L_content, L_resp) triplets across English, Spanish, and Chinese for three task families (semantic reversal, final-state extraction, language purity with update realization). It evaluates 20 LLMs using decomposed metrics (semantic correctness, target-language adherence, constraint satisfaction, contamination ratio, joint success) with human-validated scoring. The central empirical claim is that degradation is organized by language role rather than mismatch count, with the response-language role as the dominant axis and a single response-slot mismatch accounting for most degradation; mismatch count is shown to be non-monotonic, with model orderings varying across systems.

Significance. If the results hold, the work offers a valuable controlled framework for isolating language-role effects in multilingual LLM execution, advancing beyond existing benchmarks that do not fully cross the three roles. The fully crossed design, decomposed metrics, and targeted human audit are clear strengths supporting the reliability of the role-dominance observations. Credit is given for the parameter-free empirical approach and the demonstration that task families fail through distinct channels. This could usefully inform targeted improvements in multilingual model handling of response languages.

major comments (2)
  1. [Abstract] Abstract: The claim that 'the response-language role is the dominant axis of variation' and that 'a single response-slot mismatch accounts for most degradation' is derived from the 27 triplets using only English, Spanish, and Chinese. The representativeness of this language set (all high-resource, with specific script and typological properties) is not directly tested, leaving open whether the observed dominance is a general structural effect or an artifact of the chosen sample; a concrete extension to at least one additional language family would strengthen the generalization.
  2. [Benchmark Construction] Task families description: The three task families may each embed response-language sensitivity by construction (e.g., language purity with update realization explicitly involves response constraints). The manuscript should report whether the response-role dominance pattern is uniform across all three families or driven primarily by one, as this directly affects whether the role effect is shown to be task-general within the benchmark.
minor comments (2)
  1. The abstract reports '2{,}430 instances per model'; standardize the thousands separator to conventional form (2,430) for consistency with the rest of the manuscript.
  2. [Evaluation Metrics] The joint success metric is described but would benefit from an explicit combination rule or pseudocode to ensure exact reproducibility of the reported scores.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the positive evaluation and recommendation of minor revision. We address each major comment below with clarifications and planned revisions.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The claim that 'the response-language role is the dominant axis of variation' and that 'a single response-slot mismatch accounts for most degradation' is derived from the 27 triplets using only English, Spanish, and Chinese. The representativeness of this language set (all high-resource, with specific script and typological properties) is not directly tested, leaving open whether the observed dominance is a general structural effect or an artifact of the chosen sample; a concrete extension to at least one additional language family would strengthen the generalization.

    Authors: We agree that the current language set (English, Spanish, Chinese) limits claims of broad generality, as these are all high-resource languages. The fully crossed design within this set enables rigorous isolation of role effects, but we cannot perform a concrete extension to an additional language family within the scope of a minor revision, as that would require new data collection and evaluation. In the revised manuscript, we will expand the limitations section to explicitly note this constraint and recommend future work testing additional language families (e.g., Arabic or Japanese) to assess robustness of the response-role dominance. revision: partial

  2. Referee: [Benchmark Construction] Task families description: The three task families may each embed response-language sensitivity by construction (e.g., language purity with update realization explicitly involves response constraints). The manuscript should report whether the response-role dominance pattern is uniform across all three families or driven primarily by one, as this directly affects whether the role effect is shown to be task-general within the benchmark.

    Authors: We appreciate this point on ensuring task-generality. The manuscript already notes that 'Task families fail through distinct channels,' but does not explicitly break down response-role dominance per family. In the revision, we will add a new analysis (including a table or figure) reporting the key metrics (e.g., joint success rates by response-language mismatch) separately for each of the three task families to demonstrate that the dominance pattern holds uniformly rather than being driven by any single family. revision: yes

Circularity Check

0 steps flagged

Purely empirical benchmark with no derivations or self-referential reductions

full rationale

The paper introduces MTM-Bench as a controlled empirical evaluation across 27 language triplets and three task families, reporting observed patterns in LLM performance via decomposed metrics. No equations, fitted parameters, or derivations are present that could reduce claims to inputs by construction. Central observations (response-language dominance, non-monotonic mismatch effects) are data-driven comparisons, not self-definitions or renamings. No load-bearing self-citations or uniqueness theorems are invoked. This matches the default expectation of a non-circular empirical study.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

No free parameters or invented entities; relies on standard assumptions about language representativeness and task validity.

axioms (1)
  • domain assumption English, Spanish, and Chinese sufficiently represent cross-lingual role effects without language-family bias.
    Selection of these three languages is presented without further justification for generality.

pith-pipeline@v0.9.1-grok · 5798 in / 1139 out tokens · 37025 ms · 2026-06-29T18:13:33.451841+00:00 · methodology

discussion (0)

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Reference graph

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