{"id":"9ec62d3e-26e3-4706-a275-c230a8d6f53c","arxiv_id":"2507.10972","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":5.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":5,"one_line_summary":"Fine-tuning an LLM with GPT-4o-generated sign language structure assistance improves sign pose generation over a Progressive Transformer baseline on Phoenix14T and How2Sign.","lead":"This paper proposes TEAM-Sign, a method that fine-tunes a large language model to produce sign language pose sequences from text, using GPT-4o to first generate stepwise 'assistance' sequences that specify word order and relative durations. It reports gains over a 2020 transformer baseline on two datasets, but the evaluation lacks comparisons to recent methods and releases no code or data.","discovery_kind":"new_application","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Stepwise-prompted assistance is the paper's core novelty, but its content is never validated, the ablation gain is small and unrepeated, and the reported gains over Progressive Transformer persist even without assistance.","rationale":"The reader identifies GPT-4o assistance quality as the weakest assumption, which is valid but somewhat under-specified. The more precise issue is causal attribution: the w/o-assistance baseline already beats Progressive Transformer, so assistance is not needed for the empirical headline; the assistance only contributes small deltas that are not tested for significance or causality. This makes the stepwise-prompting contribution load-bearing for the paper's novelty claim, but not for the basic superiority claim. The requested controls, especially shuffled assistance and multi-seed runs, would settle whether the specific sign-language content matters. Since the work is reproducible in principle and the checks are straightforward, a CONDITIONAL verdict remains appropriate; the central novelty claim should be downgraded or heavily qualified until those checks are run. This is not an accusation of misconduct, only a request for evidence that the proposed mechanism is the actual mechanism.","tokens_in":7910,"tokens_out":6224,"duration_ms":76280,"concrete_test":"On Phoenix14T with the LLaMA3-8B configuration from Table 1, fine-tune four conditions with 3 seeds each: (a) intact GPT-4o assistance, (b) assistance with word order randomly permuted while keeping the word set and durations, (c) assistance with durations randomly shuffled, and (d) no assistance. Report mean and standard deviation for DTW-MJE and BLEU. If (b) or (c) is statistically indistinguishable from (a), the specific sign-language structure and duration content is not what drives the gain; if (a) is not significantly better than (d), the stepwise-prompting contribution is unsupported. Additionally, have a sign linguist or deaf signer rate 50 assistance sequences for grammatical acceptability to validate the assumed quality of the assistance.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central novelty is the GPT-4o-generated assistance sequences described in Sec. 3.2, yet the paper provides no example, no human or gold-standard validation, and no consistency analysis of these sequences. The system prompt states that assistance encodes 'the important words, their supposed order and relative duration' of each sign video, but there is no evidence that GPT-4o produces correct ASL/DGS structure or reliable durations. This is not merely a missing analysis: the w/o-assistance rows in Tables 1 and 2 already outperform Progressive Transformer (e.g., LLaMA3 w/o assistance DTW-MJE 0.1204 vs. PT 0.1276 on Phoenix14T; 0.1406 vs. 0.1733 on How2Sign), so the headline superiority claim does not depend on the assistance. The incremental gain from assistance is small (Phoenix LLaMA3 0.1204 to 0.1056; Qwen2 0.1051 to 0.1038; How2Sign LLaMA3 0.1406 to 0.1371; Qwen2 0.1422 to 0.1397) and is reported as a single run with no seeds, error bars, or significance test. Therefore the paper's core claim that 'stepwise prompting' extracts useful sign-language knowledge is unsupported: the small differences could be noise, or the benefit could come from any extra conditioning text rather than from linguistically correct sign-language structure.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper proposes TEAM-Sign, a sign language production method that first tokenizes pose sequences with a VQ-VAE and then fine-tunes an off-the-shelf LLM (LLaMA3-8B or Qwen2-7B) with LoRA to autoregressively predict pose tokens from input text. Its main novelty is a 'stepwise prompting' stage in which GPT-4o is asked to produce an auxiliary sequence containing the important words, their sign-language order, and relative durations; this sequence is fed to the LLM as additional context during fine-tuning and inference. Experiments on Phoenix14T and How2Sign compare the method with Progressive Transformer and a w/o-assistance ablation using DTW-MJE and back-translation BLEU, reporting relative improvements of 18.7% and 20.9% and claiming to be the first use of an off-the-shelf LLM for sign language production.","tokens_in":8194,"tokens_out":8367,"duration_ms":86462,"significance":"If the auxiliary-signal mechanism is validated, the paper would make a useful contribution by showing that LLMs' pretrained text knowledge can be distilled into structured conditioning for sign language production without extra manual annotation. The manuscript has several strengths: it evaluates two LLM backbones on two standard benchmarks, includes a no-assistance ablation, and describes the VQ-VAE+LLM pipeline clearly enough to follow. However, the core evidence for the assistance mechanism is missing, the evaluation is limited to a single 2020 baseline, and no variance or significance testing is reported. The significance of the claimed result therefore depends on additional validation that is not currently in the paper.","major_comments":[{"comment":"The stepwise-prompted assistance is the core novelty, but its content is never validated. The three GPT-4o prompts ask for sign-language sentence structure, a sign-language sentence for the input text, and relative motion durations, yet the manuscript gives no example of an assistance sequence, no human or gold-standard comparison for ASL/DGS word order, and no consistency analysis across repeated prompts. Because the system prompt tells the fine-tuned LLM that the assistance encodes 'the important words, their supposed order and relative duration' (Sec. 3.2), any error in the generated assistance directly corrupts the training signal; the w/o-assistance rows in Tables 1-2 show the model still works without assistance, so the contribution rests on unverified auxiliary information. The authors should show sample assistance sequences, check them against sign-language linguistics or existing sign annotations, and analyze prompt sensitivity and repeatability.","section":"Sec. 3.2"},{"comment":"The incremental gain from assistance is small and not statistically supported. On Phoenix14T, LLaMA3 improves from 0.1204 to 0.1056 DTW-MJE and Qwen2 from 0.1051 to 0.1038; on How2Sign, LLaMA3 improves from 0.1406 to 0.1371 and Qwen2 from 0.1422 to 0.1397. These differences are single runs with no seeds, error bars, or significance tests. Given the small margins, the claim that stepwise prompting extracts useful sign-language knowledge is not established; the gain could be noise, or it could come from any extra conditioning text rather than from linguistically correct ASL/DGS structure. Multiple seeds with variance and a significance or paired-observation test are needed.","section":"Tables 1 and 2"},{"comment":"The evaluation only compares against Progressive Transformer from 2020, despite the Related Work citing more recent direct SLP methods such as T2S-GPT [7] and Neural Sign Actors [6]. Moreover, the w/o-assistance variant already outperforms Progressive Transformer, so the headline '18.7%' and '20.9%' improvements do not establish the stated superiority of the assistance mechanism. The back-translation evaluator is also retrained from Zhou et al. [2] on keypoint data without reporting its translation accuracy or the exact training/evaluation split, making the BLEU scores incomparable to previously published numbers. Recent baselines and a documented back-translation model are required.","section":"Sec. 4.1"},{"comment":"The experimental setup is under-specified. The paper does not report codebook size, token sequence length, LoRA rank, learning rate, number of training steps, optimizer, or hardware, and the VQ-VAE weights α, β, γ in Eqs. (3)-(4) are never given. Without these details, the empirical results cannot be reproduced or compared fairly with future work. A complete implementation section or supplementary hyperparameter table should be added.","section":"Sec. 3 and Sec. 4.1"}],"minor_comments":[{"comment":"The headline improvements are computed from different variants: 18.7% corresponds to Qwen2 on Phoenix14T, while 20.9% corresponds to LLaMA3 on How2Sign; the paper should state exactly which model and metric each relative gain refers to.","section":"Abstract and Contributions"},{"comment":"In Sec. 2, reference [21] is cited as the source for HamNoSys, but [21] is Walsh et al., 'Changing the representation,' which is not the original HamNoSys notation paper; the citation should be corrected to the proper HamNoSys reference.","section":"References"},{"comment":"The notation for kuniq and kfreq in Eq. (4) is not formally defined; the text should state that kuniq is the number of distinct codebook indices in the sequence and kfreq is the maximum count of any single index, and the spacing in 'kuniqand kfreqrepresent' should be fixed.","section":"Eq. (4)"},{"comment":"The assistance example in Fig. 1, '[yesterday (3), store (2), I (1), went(2)],' should be explained in the caption or text: the notation is not defined until the system prompt in Sec. 3.2, and it is unclear whether the numbers denote order or duration.","section":"Fig. 1"}],"recommendation":"major_revision","confidential_remarks":"The paper is a short conference-style submission with an interesting but unvalidated core mechanism. I would condition acceptance on the assistance-quality analysis, variance reporting, and addition of recent SLP baselines; these are substantial but feasible revisions."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Quick take: the paper does something real—fine-tuning an off-the-shelf LLM to emit discrete sign-pose tokens—and the basic pipeline is sound. But the stated novelty is the stepwise-prompted assistance from GPT-4o, and that is the weakest link. The stress-test note is right: the w/o-assistance rows already beat Progressive Transformer on both datasets (e.g., LLaMA3 w/o assistance 0.1204 vs. PT 0.1276 on Phoenix14T; 0.1406 vs. 0.1733 on How2Sign). So the headline gains over the baseline are not driven by the assistance. The incremental gain from assistance is small, especially on How2Sign (0.1406 to 0.1371 for LLaMA3), and it is reported as a single run with no seeds or error bars. That difference could easily be noise, or the benefit could come from any extra conditioning text rather than from linguistically correct sign-language structure. The paper gives no example of an assistance sequence, no human or gold-standard validation, and no consistency analysis. That is a load-bearing omission because the assistance is the claimed contribution.\n\nWhat the paper does well: the VQ-VAE encoding with a repetition loss is a sensible way to handle fine-grained pose tokens. Using LoRA to adapt LLaMA3 and Qwen2 to this token language is clean. The ablation with and without assistance is the right instinct, and the authors should be credited for including it. The writing is clear, and the visual comparison, while qualitative, at least shows the model produces something pose-like.\n\nSoft spots beyond the assistance issue: no comparison with recent SLP methods like T2S-GPT or Neural Sign Actors, even though they are cited. No code or data release. The back-translation evaluator is retrained from Zhou et al., which is fine but adds another layer of variance. The claim of being the first to use an off-the-shelf LLM for sign language production is a bit strong given MotionGPT-style work, though it may hold specifically for SLP. The free parameters in the VQ-VAE loss and LoRA settings are not analyzed for sensitivity.\n\nOverall: this is a reasonable empirical exploration, but the central claim about assistance is unverified. The paper deserves a serious referee because the idea is timely and the experiments are reproducible in structure—but it needs substantial revision: validate or at least show samples of the assistance, add multiple seeds and significance tests, compare with stronger baselines, and release code. I would not cite it yet, but I would bring it to a reading group to discuss evaluation pitfalls in sign language generation.","headline":"Plausible LLM-based sign language production, but the claimed contribution—GPT-4o-generated assistance—is never validated and the main baseline improvements exist even without it.","tokens_in":8746,"tokens_out":1944,"would_cite":false,"duration_ms":24797,"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":"Fine-tuning an off-the-shelf LLM with stepwise sign-language prompts produces pose sequences that beat Progressive Transformer by 18.7% on Phoenix14T and 20.9% on How2Sign.","keywords":["sign language production","large language models","stepwise prompting","VQ-VAE tokenization","LoRA fine-tuning","text-to-video generation","Phoenix14T","How2Sign"],"falsifier":"Retrain with the assistance sequence replaced by permuted word order or uniform durations and compare DTW-MJE and BLEU; if scores stay at the with-assistance level, the stepwise prompting is not the source of the gain.","tokens_in":7678,"feed_emoji":"🤟","tokens_out":8907,"duration_ms":94453,"temperature":0.7,"pith_summary":"This paper tries to establish that sign language production can be treated as another natural language task for an off-the-shelf large language model. Instead of relying on gloss annotations or a separate gloss-to-pose stage, the method fine-tunes an LLM to map spoken text directly to VQ-VAE pose tokens, with the help of stepwise-prompted \"assistance\" sequences that encode sign-language word order and relative motion duration. On Phoenix14T and How2Sign, the approach lowers DTW-MJE by 18.7% and 20.9% relative to Progressive Transformer and improves BLEU back-translation scores. The paper argues this is the first application of an off-the-shelf LLM to the production task of sign language.","feed_headline":"Prompted LLM generates sign language poses, beating baseline ~20%","feed_subtitle":"Stepwise sign-order prompts let a single model skip gloss annotations and produce German and American sign poses directly from text.","key_machinery":"The central object is the assistance sequence: a text-format list, produced by prompting GPT-4o, that gives the important words of the input sentence in sign-language order and their relative motion durations, with the shortest word's duration set to 1. The assistance sequence is concatenated with the source sentence and fed into a decoder-only LLM, fine-tuned with LoRA, which autoregressively predicts VQ-VAE indices representing sign pose frames. The assistance does the work of aligning spoken-language word order with sign-language grammar and providing temporal cues, while a frozen pose decoder turns the predicted tokens back into joint poses.","core_discovery":"The central claim is that a language-only LLM can bridge the distribution gap between spoken-language text and sign pose tokens, provided it is given intermediate assistance sequences that spell out sign-language sentence structure and the relative duration of each word's motion. These sequences are generated by prompting GPT-4o in a stepwise fashion, then fed alongside the text and the VQ-VAE-quantized pose tokens during LoRA fine-tuning of LLaMA3-8B or Qwen2-7B. With assistance, the reported DTW-MJE reaches 0.1056 on Phoenix14T and 0.1371 on How2Sign, compared with 0.1276 and 0.1733 for Progressive Transformer, and the gains exceed the same models trained without assistance. The paper presents this as evidence that an LLM's inherent sign-language knowledge and reasoning ability can be exploited to generate sign language videos directly from text.","pith_inferences":["An implication the paper leaves implicit: the method's ceiling is set by the assistant model's sign-language competence, so a stronger or sign-specialized assistant could improve results without changing the pipeline.","A testable extension would replace the hand-written stepwise prompts with human-verified sign-language grammar annotations, separating the contribution of prompt wording from the LLM's pretrained knowledge.","The repetition loss used to keep fine-grained pose tokens from collapsing into the same codebook entry is a generalizable idea that could transfer to other fine-grained motion-generation tasks."],"forward_implications":["Sign language production no longer has to rely on gloss annotations or a separate gloss-to-pose stage; one fine-tuned LLM can map text to sign pose tokens directly.","The same pipeline works across languages by changing the prompt's language name, as demonstrated on German Sign Language (Phoenix14T) and American Sign Language (How2Sign).","The assistance sequences help more on the smaller Phoenix14T dataset, indicating that LLM prior knowledge can partially compensate for scarce sign-language training data.","The evaluation protocol, combining a retrained back-translation model with DTW-MJE and BLEU scores, offers a consistent way to compare future sign language production models."],"supporting_citations":[{"why":"Supplies the Progressive Transformer baseline that the proposed method compares against and the back-translation evaluation convention.","marker":"[3]"},{"why":"Provides the VQ-VAE discrete tokenization that converts sign pose frames into text-format indices consumable by a language-only LLM.","marker":"[17]"},{"why":"The assistant model prompted to generate the stepwise sign-language assistance sequences.","marker":"[25]"},{"why":"One of the off-the-shelf base LLMs fine-tuned in the main experiments.","marker":"[26]"},{"why":"The second base LLM used to show the method transfers across different off-the-shelf LLMs.","marker":"[27]"},{"why":"The LoRA approach used to fine-tune the base LLM while freezing the pose codec.","marker":"[28]"},{"why":"The German Sign Language dataset used for evaluation.","marker":"[29]"},{"why":"The American Sign Language dataset used for evaluation.","marker":"[30]"},{"why":"The gloss-free sign language translation model retrained to compute BLEU back-translation scores for generated videos.","marker":"[2]"}],"fun_headline_variants":["Stepwise prompting helps LLMs produce sign language poses","LLM learns sign language from text with step-by-step prompts","Sign language generation: LLM beats prior models by ~20%","Language model turns text into sign poses via guided prompts","Stepwise prompt strategy lets LLMs sign directly from text"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The method assumes the assistant LLM's stepwise answers about sign-language sentence structure and relative word durations are accurate and consistent enough to teach the fine-tuned model; the paper does not validate this against human sign-language knowledge.","fun_headline_variants_meta":{"raw":{"variants":["Stepwise prompting helps LLMs produce sign language poses","LLM learns sign language from text with step-by-step prompts","Sign language generation: LLM beats prior models by ~20%","Language model turns text into sign poses via guided prompts","Stepwise prompt strategy lets LLMs sign directly from text"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.00023,"raw_usage":{"total_tokens":1444,"prompt_tokens":869,"completion_tokens":575,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":485,"completion_tokens_details":{"reasoning_tokens":493}},"tokens_in":485,"tokens_out":575,"duration_ms":7048,"temperature":1.0,"reasoning_tokens":493,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-06T17:19:40.102066+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Retrain with the assistance sequence replaced by permuted word order or uniform durations and compare DTW-MJE and BLEU; if scores stay at the with-assistance level, the stepwise prompting is not the source of the gain.","supporting_citations":[{"cited_title":"language","cited_arxiv_id":null,"evidence_quote":"Supplies the Progressive Transformer baseline that the proposed method compares against and the back-translation evaluation convention."},{"cited_title":"Sinc: Spatial composition of 3d human motions for simultaneous action generation,","cited_arxiv_id":null,"evidence_quote":"Provides the VQ-VAE discrete tokenization that converts sign pose frames into text-format indices consumable by a language-only LLM."},{"cited_title":"Ham2pose: Animating sign language notation into pose se- quences,","cited_arxiv_id":null,"evidence_quote":"The assistant model prompted to generate the stepwise sign-language assistance sequences."},{"cited_title":"Changing the Representation: Examining Language Representation for Neural Sign Language Production","cited_arxiv_id":"2210.06312","evidence_quote":"One of the off-the-shelf base LLMs fine-tuned in the main experiments."},{"cited_title":"Motiongpt: Human motion as a foreign language,","cited_arxiv_id":null,"evidence_quote":"The German Sign Language dataset used for evaluation."},{"cited_title":"Two-stage approaches mainly rely on additional anno- tated information, such as gloss, to provide extra supervision during the generation phase","cited_arxiv_id":null,"evidence_quote":"The gloss-free sign language translation model retrained to compute BLEU back-translation scores for generated videos."}],"review_version":1}