{"id":"f50cf621-b064-47ed-a823-8e870a95f000","arxiv_id":"2505.21458","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":2,"one_line_summary":"Using a new LLC Score, translation and geo-culture cloze experiments on three small multilingual LLMs show that latent-language consistency does not reliably predict task accuracy, contradicting the paper's initial hypothesis.","lead":"This paper tests whether large language models perform better when they keep a single internal 'thinking language' across their layers. The authors find no consistent link between internal language consistency and task accuracy, and suggest models can compensate by adapting near the output layers.","discovery_kind":"new_method","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Eq. (1) does not implement the stated switch-away-from-v definition: its indicator ignores the current layer's language, and Score(v) is undefined (0/0) for a perfectly consistent v, so all reported correlations inherit an unspecified metric.","rationale":"The reader's weakest assumption correctly identifies the LLC Score as unvalidated, and I agree that the central evidence rests on whether the score measures latent-language consistency. My stress-test goes further: the score is not merely noisy or unvalidated, it is formally ill-defined as written. The indicator in Eq. (1) does not match the prose definition, and the denominator vanishes for a perfectly consistent language, with no stated convention. Because the LLC Score is the independent variable in every correlation and the paper's negative finding is the absence of a stable correlation, this flaw directly undermines the central claim. The positive correlation values in the paper cannot be reproduced or interpreted without knowing how the authors handled the undefined case. I would not permanently reject because the underlying question is meaningful and the analysis could be corrected and rerun, but the current manuscript is unverdictable: the reader's CONDITIONAL verdict assumed the metric was a noisy proxy, whereas the metric's definition itself may be wrong. A concrete reimplementation test on one reported condition would settle whether the correlations survive a corrected definition.","tokens_in":23507,"tokens_out":9439,"duration_ms":104877,"concrete_test":"Take any reported condition with a single dominant latent language at all analyzed layers (e.g., LLM-jp-3 geo-culture En/En in Table 2) and implement Eq. (1) literally: for the dominant language v, the indicator is 0 at every layer and Score(v) = 0/0. Check whether the table entry is finite; then recompute all r values under two minimal fixes: (a) define Score(v)=0 when no switch away from v occurs; (b) replace the indicator with 1(v*_l = v and v*_{l+1} != v). If the correlation signs or magnitudes change materially, the published LLC Score is an artifact of an unspecified division-by-zero convention rather than a measure of latent-language consistency.","verdict_should_be":"UNVERDICTED","load_bearing_attack":"The paper's central claim is that latent-language consistency, as quantified by the LLC Score, does not reliably predict robustness. The LLC Score is therefore load-bearing, and Eqs. (1)-(2) contain a formal flaw. The prose in Sec. 3.2 defines a disruption for v as a switch from v at layer l to a different dominant language at l+1, but the indicator in Eq. (1) is 1(v*_{l+1} != v) with no condition that v*_l = v. A transition from En to Zh is counted as a disruption for Ja as well, so Score(Ja) accumulates KL-weighted mass for transitions in which Ja is not the current layer's language. Conversely, if the model uses the same language v at every analyzed layer, the denominator of Score(v) is 0/0; no convention is stated. The LLC Score then is the minimum over languages that are never the next-layer argmax, which can move with low-probability token statistics independently of genuine consistency. All reported correlations (Tables 2, 6-8; Figs. 2-5) are computed from this undefined or incorrectly specified quantity, so the headline conclusion that consistency is not always necessary is not supported by the numbers as written. This is not a criticism of langid.py; even with perfect language identification and LogitLens, the metric is ill-defined in precisely the regime of interest.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper investigates whether consistency of an LLM's internal 'latent language' across layers correlates with downstream task accuracy. The authors introduce a Latent Language Consistency Score (LLC Score) computed from LogitLens token probabilities and langid.py language identifications, inject adversarial multilingual prompts at varying ratios, and measure robustness (accuracy) on GPT-4o-generated cloze-style translation and geo-culture datasets for three small multilingual models (LLM-jp-3, Qwen2.5, Gemma3). The reported correlations between LLC Score and robustness are inconsistent in sign across models and conditions, leading the authors to conclude that maintaining a consistent latent language is not always necessary for optimal task performance.","tokens_in":23807,"tokens_out":5244,"duration_ms":52739,"significance":"If the central claim were supported, the paper would offer a useful negative result about latent-language consistency as a predictor of robustness, and the proposed LLC Score could serve as a diagnostic tool for multilingual LLMs. The dataset-construction pipeline with Self-BLEU diversity checks and the systematic variation of adversarial-prompt ratios are reasonable first steps. However, the significance is substantially weakened by the lack of external validation of the LLC Score, the use of a model-generated benchmark, and the absence of statistical testing; the contribution is therefore conditional on a corrected and re-analyzed metric.","major_comments":[{"comment":"The indicator function in Eq. (1) is 1(v*_{l+1} != v) with no condition that v*_l = v, so the score for a language v accumulates KL-weighted mass from transitions in which v is not the current layer's dominant language; the prose in Sec. 3.2 defines a disruption as a switch from v at layer l to a different language at l+1. Additionally, if the model uses the same language v at every analyzed layer, both numerator and denominator of Score(v) are zero, leaving 0/0 undefined with no stated convention. Since the LLC Score is the minimum over v of Score(v), the reported correlations in Tables 2, 6-8 and Figures 2-5 are computed from a quantity that is either mis-specified or undefined in exactly the regime of interest (perfectly consistent latent language). The headline conclusion is therefore not supported by the numbers as written; the metric must be redefined and the analysis recomputed.","section":"Sec. 3.2, Eq. (1)-(2)"},{"comment":"The correlations r are computed from only five points (adversarial ratios 0.2, 0.4, 0.6, 0.8, 1.0) with no significance tests, confidence intervals, or error bars; the checklist (item 7) explicitly answers 'No' to statistical significance. Several rows of Table 2 have constant robustness (e.g., Qwen2.5 with accuracy 0.00 across all ratios) and yet report r = 0.21 or 0.83, which are meaningless for a constant dependent variable. The sign of r is inconsistent across near-identical conditions (e.g., Table 2: r = -0.97 vs r = +0.90 for Gemma3 geo-culture with question in Japanese and adversarial Ja), so the qualitative conclusion that 'consistency is not always necessary' rests on noise-level differences. The authors should report scatter plots with all points, compute a proper test (e.g., Spearman with p-values or bootstrap CIs), and state how undefined r values are treated.","section":"Sec. 5-6, Tables 2, 6-8"},{"comment":"Appendix A states that the LLC Score was selected because it showed the strongest correlation with other potential methods (e.g., averaging KL scores). This selection of the metric based on its correlation with alternatives, in a study whose central claim is about the correlation between this metric and task performance, is a form of circularity: the metric is not independently validated as a measure of 'thinking in one language.' To support the construct validity of the LLC Score, the paper should provide an external check (e.g., agreement with human-annotated or dictionary-based language labels of LogitLens tokens, or a controlled setting where the latent language is known by construction) before using the score to draw conclusions about latent-language consistency.","section":"Appendix A"},{"comment":"The pipeline relies on langid.py classifying individual tokens extracted by LogitLens, but single-token language identification is known to be noisy, and the paper does not report accuracy of langid on the actual token distribution (e.g., tokens like 'Washington' or 'Kishida' may be classified inconsistently). In addition, the evaluation datasets are generated and filtered by GPT-4o (Sec. 4.1-4.2), so the robustness numbers are measured on a model-generated benchmark rather than an external one; the conclusions should be scoped accordingly. Without a validation of the token-level language labels or a human-checked dataset, the measured LLC Score may reflect token-level classification artifacts rather than the model's internal reasoning language.","section":"Sec. 3.1 / Sec. 4"}],"minor_comments":[{"comment":"The author name 'Hirki Ouchi' appears to be a typo for 'Hiroki Ouchi'; please correct it.","section":"Author list"},{"comment":"'Appnendix' should read 'Appendix', and 'Mathmetical Approach' in B.2 should read 'Mathematical Approach'.","section":"Appendix B heading"},{"comment":"The caption contains 'Chinse' instead of 'Chinese'; also, 'Figure 5' is referenced as 'Figures 3 and 5' in Section 6, which is inconsistent because Figure 5 is not described in the main text.","section":"Figure 5 caption"},{"comment":"The notation 'N.A.' in correlation columns is not defined; the paper should state whether these rows were excluded from the analysis or treated as undefined, and why.","section":"Tables 2, 6-8"},{"comment":"The claim that 'inserting adversarial prompts reduces model confidence' is based on a visual comparison of two scatter plots; the paper should report a quantitative summary (e.g., mean/median shift in output probability or KL divergence) to support this claim.","section":"Sec. 7.4, Figure 6"},{"comment":"The exclusion of {Ja}-{Zh} and {Zh}-{Ja} translation pairs because 'the token language cannot be clearly identified' is an ad hoc decision that removes the most interesting mismatched-language conditions; this exclusion should be justified and its impact on the conclusions discussed.","section":"Sec. 5.2"}],"recommendation":"major_revision","confidential_remarks":"The formal flaw in Eq. (1)-(2) is serious and affects all reported correlations, so the paper cannot be accepted as is. However, the research question is legitimate and the data pipeline could be salvaged by redefining the LLC Score and re-running the analysis with proper statistical treatment. The Appendix A disclosure that the metric was selected for its correlation with alternatives is a red flag for construct validity and should be addressed head-on in revision. I recommend major revision rather than rejection because the flaw is fixable within the scope of the paper, though the revised results may change the qualitative conclusions."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Two things to know. The paper asks the right question — whether latent-language consistency predicts task performance in multilingual LLMs — and reports a negative result: it usually doesn't. The finding is plausible, but the current numbers don't support it, because the one metric that drives all the correlations is formally broken.\n\nWhat's actually new: the LLC Score, plus the first systematic correlation analysis between latent-language consistency and downstream accuracy across languages, tasks, and adversarial-prompt ratios. That is a real extension of the prior latent-language detection work. Credit where due: the paper is transparent about its own weaknesses — Appendix A admits the metric was selected partly because of its correlation with alternatives, the checklist says no error bars, and the authors ran larger-model checks in the appendix.\n\nThe load-bearing flaw is in Eq. (1). The prose says a disruption for language v is when the model prefers v at layer l and switches to a different dominant language at l+1. The indicator only checks v*_{l+1} ≠ v; it never checks the current layer's language. So a transition from English to Chinese counts as a disruption for Japanese as well. And for a language that is the argmax at every layer, both the numerator and denominator are zero, so Score(v) is undefined. Since the LLC Score is the min over v, the perfectly consistent case — the regime the paper is about — yields an undefined metric. Every r in Tables 2, 6–8 and Figures 2–5 inherits this. The negative finding may survive a corrected metric, but as written the correlations aren't about what they claim to measure.\n\nOther soft spots are real but secondary. Correlations are computed over five points without significance tests or error bars; several rows are N.A. because the LLC Score is constant; and the 'final-layer adaptation' explanation in the abstract is asserted rather than directly measured. Withholding code and data until acceptance doesn't help.\n\nWho this is for: people working on latent language, multilingual prompting, and interpretability. It's an honest negative result that needs a corrected metric and better statistics before it's citable. The fixes are straightforward: redefine the indicator to require v*_l = v, handle the 0/0 case explicitly, re-run, and report bootstrap intervals.\n\nSend it to peer review. The question matters, the design is thoughtful, and the flaw is fixable. A competent referee will catch this and the revision will be much stronger.","headline":"Honest negative result undermined by an ill-defined metric: the LLC Score in Eq. (1) mishandles exactly the consistent-language case it is meant to measure.","tokens_in":24339,"tokens_out":4227,"would_cite":false,"duration_ms":42850,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"The paper claims that consistent latent language is not required for LLM task performance, because models adapt their internal representations in the final layers to the target language.","keywords":["latent language","LLC Score","LogitLens","multilingual LLMs","adversarial prompts","translation task","geo-culture task","robustness"],"falsifier":"Ask a model to verbalize its reasoning in each candidate language while running the LLC measurement on the same inputs; if the LogitLens-detected latent language and the verbalized reasoning language disagree on many items, the score is not measuring thinking in one language.","tokens_in":23325,"feed_emoji":"🌐","tokens_out":9052,"duration_ms":86995,"temperature":0.7,"pith_summary":"Large language models often seem to reason in an internal 'latent language' that differs from the input or output language, and a natural suspicion is that mixing languages inside the model hurts its answers. The paper tests that suspicion directly by defining a Latent Language Consistency (LLC) Score, then injecting adversarial prompts in Japanese, English, or Chinese at various ratios while measuring accuracy on translation and geo-culture cloze questions. The result is a negative finding: consistency in latent language is not always correlated with better performance, and in several settings accuracy holds or changes as consistency changes. The proposed explanation is that models absorb language mismatch by adjusting representations in the final layers, so the consistency of the model's earlier internal language is not the decisive factor. The value is a quantitative way to talk about latent language and evidence against treating it as a simple performance knob.","feed_headline":"LLMs need not think in one language to answer well","feed_subtitle":"Models adapt near their final layers, so latent-language consistency is not a reliable accuracy predictor.","key_machinery":"The central object is the Latent Language Consistency (LLC) Score. For each candidate language in a small set (English, Japanese, Chinese), it averages the KL divergence between adjacent transformer layers, weighted by the model's probability of using that language, but only at transitions where the most probable language moves away from the candidate; the final LLC Score is the minimum over candidate languages. LogitLens is used to read out per-layer token probabilities, and an automatic language detector labels the resulting tokens. The score is meant to quantify how stably the model internally stays in one language, and the paper's evidence consists of correlations between this score and accuracy on the constructed datasets under varying ratios of adversarial prompts.","core_discovery":"The paper's central finding is that the hypothesis 'thinking in a consistent latent language improves downstream accuracy' fails in controlled tests across three multilingual models (Gemma3, Qwen2.5, and LLM-jp-3) on translation and geo-culture cloze tasks. LLC Score and accuracy do not move in lockstep: some conditions show the expected negative correlation (for instance, one LLM-jp-3 English-to-Chinese translation condition gives $r = -1.0$), while other conditions show positive correlations (for instance, a Gemma3 geo-culture condition gives $r = 0.98$), and many conditions show near-zero correlation. The paper interprets this as evidence that models can keep a stable latent language and still lose accuracy, or lose consistency without losing accuracy, because the final layers adapt representations to the target language. The stated conclusion is that maintaining consistency in latent language is not always necessary for optimal downstream task performance, and that translation-like tasks requiring precise linguistic alignment are especially sensitive to adversarial language mismatch.","pith_inferences":["Editorial inference: If final-layer adaptation is the real mechanism, then truncating the network before those final layers (early exiting) should reveal the consistency effect that the full model hides; that is a direct test the paper does not run.","Editorial inference: The LLC Score may actually measure representational stability in token identity rather than the language of reasoning; applying it to tasks where reasoning is verbalized (chain-of-thought in different languages) would separate these two interpretations.","Editorial inference: The same ratio-sweep design could be applied to monolingual adversarial noise, such as scrambled text or inserted unrelated sentences, to test whether the observed effect is language-specific or a general robustness phenomenon."],"forward_implications":["Latent-language consistency, as measured by LLC Score, should not be treated as a proxy for accuracy in multilingual LLM evaluation, since the sign of the correlation varies across models and tasks.","Translation quality degrades most when adversarial prompts come from a language different from the source or target language, so input-language alignment matters even when the latent language stays stable.","Models that keep a stable latent language despite mixed-language inputs can still lose accuracy, which points to final-layer representation adaptation as the main compensatory mechanism.","The constructed cloze-style datasets (2,000 items per language and task) provide a reusable protocol for measuring how internal language consistency and adversarial language mixture interact."],"supporting_citations":[{"why":"Supplies the finding that Llama models use English as an internal latent language, motivating the consistency hypothesis and the LogitLens-based detection approach.","marker":"[1]"},{"why":"Shows that LLM-jp models lean toward Japanese internally, which guides the choice of models expected to have a proficient latent language.","marker":"[2]"},{"why":"Supports the general claim that multilingual LLMs may think in English and grounds the latent language concept used throughout the paper.","marker":"[8]"},{"why":"Provides LogitLens, the method for extracting per-layer token probabilities that the LLC Score is built on.","marker":"[18]"},{"why":"Documents that syntactic and lexical perturbations degrade model accuracy, motivating the adversarial-prompt design.","marker":"[16]"},{"why":"Shows that inserting semantically unrelated sentences can reduce question-answering reliability, the basis for the adversarial prompt injection.","marker":"[17]"},{"why":"Supplies the semi-automatic QA-pair generation methodology adapted for constructing the cloze datasets.","marker":"[6]"},{"why":"Supplies the question-generation-with-filtering pipeline used to keep only single-token-answer items.","marker":"[7]"}],"fun_headline_variants":["Consistent latent language doesn't guarantee better LLM answers","LLMs don't need one thinking language for accuracy","Adaptive final layers let LLMs ignore latent language consistency","Latent language consistency not key to LLM performance","Why LLMs can think in any language and still answer correctly"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The load-bearing premise is that the LLC Score actually measures whether the model is thinking in one language, which requires that the tokens read out layer-by-layer and automatically labeled by language reflect the language used for reasoning rather than just the most likely surface word.","fun_headline_variants_meta":{"raw":{"variants":["Consistent latent language doesn't guarantee better LLM answers","LLMs don't need one thinking language for accuracy","Adaptive final layers let LLMs ignore latent language consistency","Latent language consistency not key to LLM performance","Why LLMs can think in any language and still answer correctly"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000385,"raw_usage":{"total_tokens":2033,"prompt_tokens":943,"completion_tokens":1090,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":559,"completion_tokens_details":{"reasoning_tokens":1010}},"tokens_in":559,"tokens_out":1090,"duration_ms":8417,"temperature":1.0,"reasoning_tokens":1010,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-07T13:27:40.605225+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Ask a model to verbalize its reasoning in each candidate language while running the LLC measurement on the same inputs; if the LogitLens-detected latent language and the verbalized reasoning language disagree on many items, the score is not measuring thinking in one language.","supporting_citations":[{"cited_title":"Do llamas work in English? on the latent language of multilingual transformers","cited_arxiv_id":null,"evidence_quote":"Supplies the finding that Llama models use English as an internal latent language, motivating the consistency hypothesis and the LogitLens-based detection approach."},{"cited_title":"Interpreting gpt: the logit lens","cited_arxiv_id":null,"evidence_quote":"Provides LogitLens, the method for extracting per-layer token probabilities that the LLC Score is built on."},{"cited_title":"Robustness gym: Unifying the NLP evaluation landscape","cited_arxiv_id":null,"evidence_quote":"Documents that syntactic and lexical perturbations degrade model accuracy, motivating the adversarial-prompt design."},{"cited_title":"More bang for your buck: Natural perturbation for robust question answering","cited_arxiv_id":null,"evidence_quote":"Shows that inserting semantically unrelated sentences can reduce question-answering reliability, the basis for the adversarial prompt injection."},{"cited_title":"mCSQA: Multilingual commonsense reasoning dataset with unified creation strategy by language models and humans","cited_arxiv_id":null,"evidence_quote":"Supplies the semi-automatic QA-pair generation methodology adapted for constructing the cloze datasets."},{"cited_title":"BQA: Body Language Question Answering Dataset for Video Large Language Models","cited_arxiv_id":"2410.13206","evidence_quote":"Supplies the question-generation-with-filtering pipeline used to keep only single-token-answer items."}],"review_version":1}