Cross-Lingual Representation Alignment by Token-Level Optimal Transport in a Language-Agnostic Space
Taisei Yamamoto, Ryoma Kumon, Danushka Bollegala, Hitomi Yanaka
cs.CL
Quick verdict
CAROT aligns LLM token-level representations via LEACE + optimal transport; steering gains are solid and well-analyzed, training gains modest but honest.
Summary
The paper proposes CAROT, a two-step method for cross-lingual alignment (CLA) of LLM representations. First, LEACE concept erasure, trained on 2,560 parallel sentences per language across 12 languages, decomposes hidden states into language-specific and language-agnostic components. Second, unbalanced optimal transport with Mahalanobis cost aligns token-level language-agnostic representations of a target language to English (the dominant source language), with perplexity-based filtering of unreliable matches and recombination with the preserved language-specific component. As inference-time steering applied to question tokens at one layer, CAROT improves accuracy over the unsteered model on three models (Llama3.1-8B, gemma3-4b, Qwen3-4B) across four benchmarks, with a maximum gain of 11.2 points (Qwen3-4B on KLAR), while largely preserving answer-language fidelity relative to two sentence-level baselines (Lang Vec, MLRS). As a training signal (alignment loss plus SFT on Aya with LoRA), CAROT-trained base models beat LM-only SFT and MidAlign in 11 of 18 settings, though gains are smaller than with steering; the authors attribute this to representation drift (principal angles around 40 degrees) of the frozen LEACE parameters. Limitations include English-only anchoring, linear-only erasure, hyperparameters tuned on GMMLU dev, and no significance testing against baselines; code is released.
Review complete
Assessment at a glance
5
risk flags identified
● high 0
● medium 3
● low 2
5 strengths · 5 weaknesses · 5 author questions
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Key claims under review
- LEACE trained on sentence embeddings isolates linearly encoded language-specific information in LLM hidden states, with partial generalization to OOD languages (Section 5).
- Token-level unbalanced OT alignment of language-agnostic components yields steering targets that raise accuracy by up to 11.2 points (Qwen3-4B/KLAR) while preserving input-output language consistency better than sentence-level baselines (Table 1).
- Training base models to internalize CAROT representations outperforms LM-only SFT and MidAlign in 11 of 18 model-task-ID/OOD settings (Table 3), though gains are smaller than steering due to representation drift of the frozen eraser.
Strengths
Principled decomposition, empirically validated
Section 5 grounds the core assumption: in remaining representations inter-language cosine similarity exceeds intra-language (Fig. 3), while LEACE-erased representations collapse to per-language directions; post-erasure language-classification accuracy drops for ID languages (Fig. 8), with OOD generalization shown qualitatively (Fig. 2b).
Token-level alignment is a real advance over sentence-level steering
The Last-Token ablation (Table 8) loses to full CAROT in 10 of 12 cells, directly supporting the granularity claim against diff-in-means and MLRS-style global interventions; transport plans visualized in Appendix D show semantically corresponding token matches (e.g., '(Tur)quie'–'Turkey').
Unusually honest limitation analysis
Strong reproducibility package
Overhead measured, not asserted
Weaknesses
Ablation undercuts the headline component's accuracy contribution
Thin statistical treatment beyond the unsteered comparison
Hyperparameters tuned on GMMLU dev, a reported benchmark
Headline number comes from the impractical variant
Evaluation noise and fidelity exceptions
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Scope & Claims
The paper targets cross-lingual alignment (CLA) in LLMs: making middle-layer representations share structure across languages so capability transfers from a dominant language (English) to others. It advances three claims: (1) LEACE splits hidden states into language-specific and language-agnostic parts, generalizing partly to OOD languages (Sec. 5); (2) token-level unbalanced optimal transport (OT) in the language-agnostic space produces steering targets raising accuracy by up to 11.2 points (Qwen3-4B on KLAR, Table 1) with better fidelity than sentence-level baselines; (3) using CAROT outputs as an SFT training signal beats LM-only SFT and MidAlign in 11 of 18 cells (Table 3). This is a method paper with broad empirical support: 3 models (Llama3.1-8B, gemma3-4b, Qwen3-4B), 4 benchmarks (KLAR, GMMLU, BELEBELE, XQuAD), steering plus training applications.
The remaining sections cover Methodology Assessment, Statistical Evidence, Results & Reproducibility, Limitations & Risks, and Verdict & Recommendations.
- All 5 strengths & 5 weaknesses, analyzed in detail
- 5 risk flags with severity, section & reviewer questions
- Methods & statistical evidence assessment
- 5 questions a peer reviewer would ask
- Full six-dimension evaluation report & recommendations
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Frequently asked questions
What is CAROT and how does it align LLM representations across languages?
CAROT is a two-step method: it first uses LEACE concept erasure to split a model's hidden states into a language-specific and a language-agnostic part, then uses unbalanced optimal transport with Mahalanobis cost to align token-level language-agnostic representations of a target language to English, before re-adding the preserved language-specific part. The resulting representations serve as inference-time steering targets or as training targets.
Does CAROT require translations at inference time?
The steering variant needs an English translation of the input at inference, which the authors themselves call impractical at scale. The training variant internalizes the alignment during fine-tuning, so translation is only needed for the parallel training data, making it the deployable option.
How much does CAROT improve multilingual performance?
In steering experiments on Llama3.1-8B, gemma3-4b and Qwen3-4B across four benchmarks, the largest reported gain is 11.2 accuracy points (Qwen3-4B on KLAR, from 56.8 to 68.0). In training experiments, CAROT beats LM-only SFT and MidAlign in 11 of 18 model-task-language settings, with smaller gains than steering.
Which models and languages does the paper evaluate on?
Models are Llama3.1-8B, gemma3-4b and Qwen3-4B. LEACE is trained on 12 languages (including English, French, Spanish, Chinese, Japanese, Korean, Hebrew, Bengali, Telugu) with 6 held-out OOD languages (German, Turkish, Dutch, Ukrainian, Indonesian, Swahili); evaluation covers subsets of these on KLAR, Global-MMLU, BELEBELE and XQuAD.
About this review
This is an AI-generated preliminary review by Evolve.Pub, produced from the openly available text of the paper. It is not peer review and does not represent the authors' views. The canonical record of the paper is at the publisher link above.
- Figure panels are not present in the extracted text (only captions); statements about plotted content rest on captions and surrounding prose.
- Mathematical notation (Eqs. 1-17) is partially garbled by PDF extraction; interpretations are based on readable fragments and are consistent with the surrounding text.
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