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REVIEW 4 major objections 4 minor 6 cited by

Typhoon 2: A Family of Open Text and Multimodal Thai Large Language Models

T0 review · 4 major / 4 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read The paper's central claim is that a single open recipe—curated Thai-English continued pretraining plus staged post-training—brings Thai text, vision, and audio models to the top of Thai-language benchmarks while preserving the base…

desk verdict Open Thai model family worth knowing about, but the headline benchmark story is circular. read the letter →

arxiv 2412.13702 v2 pith:4FALN3SA submitted 2024-12-18 cs.CL cs.AI

Kunat Pipatanakul , Potsawee Manakul , Natapong Nitarach , Warit Sirichotedumrong , Surapon Nonesung , Teetouch Jaknamon , Parinthapat Pengpun , Pittawat Taveekitworachai
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Adisai Na-Thalang Sittipong Sripaisarnmongkol Krisanapong Jirayoot Kasima Tharnpipitchai
This is my paper · ORCID
classification cs.CLcs.AI
keywords Thailanguagemodelscontinualpre-traininginstructiontuningfunctioncallinglongcontextmultimodalLLMspeech-to-speechAIsafetyclassifier
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper reports a family of Thai language models, from 1 to 70 billion parameters, built by continuing to train capable open base models on a mixture of Thai and English text and then applying a staged post-training recipe. The authors claim the resulting text models reach the best published results on Thai exams and Thai instruction-following while keeping English, math, coding, and long-context abilities largely intact. The same project adds a Thai vision model focused on document OCR and chart questions, an end-to-end audio model that can listen to speech and answer in parallel text and speech, and a lightweight safety classifier tuned to Thai cultural sensitivities. The significance, if the evaluations hold, is that a low-resource language can be brought to competitive model quality with an open, reproducible recipe rather than proprietary data.

What carries the argument

The load-bearing mechanism is the Typhoon2 training recipe. It starts with continual pre-training, where an already-trained English-centric base model is trained further on roughly 12 billion high-quality Thai tokens plus a 50 percent English mix to avoid forgetting; the Thai tokens are selected by four filters—a cultural-relevance classifier, a Thai quality fastText classifier, synthetic textbook-style augmentation, and high-educational-content filtering. Post-training then layers general SFT with a 3:7 Thai-to-English ratio, math and code SFT with a Thai-translated subset, long-context data at 15 percent of the mix, function-calling data at 5–10 percent, top-k logits distillation for 1B/3B models, and a DARE+linear merge with a newer instruction model for the 70B model. For audio, the machinery is an encoder-adapter-LLM front end (Whisper-type speech encoder plus audio-event encoder, aligned through a Q-Former) feeding a non-autoregressive speech decoder trained with CTC to output discrete speech units, which a unit vocoder turns into waveform; this lets text and speech be decoded in parallel from the same LLM hidden states.

What would settle it

Write a new Thai national-exam-style test with questions dated after the model's training cutoff, run the released Typhoon2-Text models and their base models on it, and compare; if the Typhoon2 advantage over the base models shrinks to near zero, the benchmark-driven improvement is largely an artifact of overlap with training data.

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Extended reading notes

Core claim

On the paper's own terms, the central claim is that a single adaptable recipe—domain-filtered continual pre-training on a Thai-English mix, followed by general and domain-specific supervised fine-tuning, long-context adaptation, function-calling data, logit distillation for small models, and DARE-plus-linear merging for the 70B model—produces Thai LLMs that outperform their base models and existing Thai-focused baselines across exams (ThaiExam, M3Exam), instruction following (IFEval-TH, MT-Bench-TH), math, coding, and function calling. The authors further claim the same approach transfers to other modalities: a vision model built on Qwen2-VL improves Thai OCR and chart QA without losing general captioning, and an audio model built on the Typhoon2 text model achieves end-to-end speech-to-speech interaction that beats an open baseline and is competitive with a proprietary system on Thai. The conclusion states that evaluation demonstrates superior performance across a majority of evaluated tasks, including math and reasoning.

Load-bearing premise

The load-bearing premise is that the ThaiExam and M3Exam scores used to guide every pre-training and post-training decision are honest measures of Thai language ability; the paper itself notes these scores sit far above typical human performance and may reflect contamination or overfitting, and if that is true the claimed gains may not appear in real Thai text.

Editorial extensions

If this is right

  • Typhoon2-Text models from 1B to 70B improve Thai exam and instruction-following performance over their base models, with the Qwen-based 7B variant reaching the top overall BFCL function-calling score (79.08% English, 75.12% Thai).
  • The 70B model's DARE+linear merge with a newer Llama instruction model raises IFEval and MT-Bench scores beyond the unmerged SFT model.
  • Llama-based Typhoon2 models hold longer contexts up to roughly 90,000 tokens and the Qwen-based model reaches 128,000 tokens.
  • Typhoon2-Vision improves Thai OCR and ChartQA over Qwen2-VL and prior Thai vision models, while the audio model generates text and speech in parallel and performs competitively with a proprietary system on Thai speech-to-speech.
  • Typhoon2-Safety, a small mDeBERTa-based classifier, reaches higher F1 than larger Llama-based guards on Thai-sensitive topics (about 88.5–88.7) and comparable English safety performance.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Editorial inference: if ThaiExam and M3Exam contain contamination—a concern the paper itself raises—then the headline exam gains may overstate real Thai ability; a held-out, post-cutoff Thai exam would settle this.
  • Editorial inference: the same CPT-and-post-training recipe could plausibly transfer to other low-resource Southeast Asian languages, but the paper only demonstrates it for Thai, so the transfer claim remains untested.
  • Editorial inference: the audio architecture's parallel text-and-speech decoding should lower time-to-first-speech-token latency in conversational assistants; the paper does not report this latency directly, making it a measurable but unverified benefit.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 4 minor

Summary. This technical report describes Typhoon 2, a family of Thai-language LLMs built by continual pre-training of Llama and Qwen backbones. It covers Thai corpus construction and data-mixture selection, post-training for instruction following, math/code, long context, function calling, distillation and model merging, a Thai safety classifier, a vision model for Thai documents, and an end-to-end audio/speech model. The central claims are that Typhoon2-Text models reach state-of-the-art Thai exam and instruction-following performance while preserving base-model abilities, that Typhoon2-Safety provides strong Thai-specific content moderation, that Typhoon2-Vision improves Thai OCR and document VQA, and that Typhoon2-Audio enables simultaneous text and speech output in an end-to-end speech-to-speech system. The paper also releases model weights and hyperparameters.

Significance. If the headline results hold, the paper is a valuable resource for Thai NLP: it publicly releases a full model family across 1B-70B sizes, documents data-filtering and post-training recipes in unusual detail, and includes a genuinely interesting small safety classifier with high F1 on Thai-sensitive topics. The audio section is also substantial, with a concrete recipe for adapting an SALMONN-style encoder and Llama-Omni-style decoder to Thai. The authors are candid about several limitations, including possible contamination of ThaiExam/M3Exam and the English weakness of the audio model. However, the central text-model claim is currently supported mainly by benchmarks that the authors themselves used as development signals and that they concede may be contaminated, so the significance will be much stronger once a held-out Thai evaluation is provided.

major comments (4)
  1. [Sections 2.3, 2.5, and Table 2] The primary evidence for Typhoon2-Text superiority is circular. Section 2.3 states that each pretraining data source and mixture was selected by checking improvements on M3Exam and/or ThaiExam, and Section 2.5 explicitly concedes that scores on these datasets are 'highly above the average level of a typical Thai person' and 'can be attributed to contamination and saturation due to overfitting.' Table 2 then uses the same ThaiExam and M3Exam numbers as the headline evidence that Typhoon2-Llama-8B-base (ThaiExam 51.20) beats Llama3.1-8B (45.80). Because the model has been optimized on these exact instruments, these gains cannot be interpreted as general Thai language ability. Please add a genuinely held-out Thai evaluation (e.g., a Thai exam or Thai benchmark not used during development, with an n-gram overlap analysis against the pretraining corpus) and present it alongside Table 2.
  2. [Table 7] The code evaluation table contains a duplicated row that makes the comparison invalid as printed. The rows for Typhoon2-Llama3.1-8B-Instruct and Qwen2.5-7B-Instruct are identical across HumanEval-TH, HumanEval-EN, MBPP-TH, and MBPP-EN (58.5, 68.9, 60.8, 63.0 in each column). This cannot be correct for two different models unless the table was copied from one row to another. Please correct the table, rerun the evaluation if necessary, and report the actual numbers.
  3. [Section 4.5 and Table 25] The claim that 'Typhoon2-Qwen2-VL excels in key areas... compared to other competitive models' is selective and not supported by the full table. On OCRBench, Typhoon2-Qwen2-VL scores ROUGE-L/Accuracy of 64.38/49.60, which is below both Llama-3.2-11B (72.84/51.10) and Typhoon2-Llama-3.2-11B (81.20/71.70). Its average ROUGE-L across the eight benchmarks (62.77) is also lower than the Typhoon2-Llama-3.2 prototype (64.16). The Qwen-based model may still be the right release choice given its size and specific Thai OCR accuracy, but the stated rationale should be revised to name the metrics where it actually wins and to acknowledge the losses on OCRBench and average ROUGE-L.
  4. [Section 6 and Tables 15-16] The conclusion that 'our evaluation demonstrates superior performance across a majority of evaluated tasks' is not supported by the full evaluation tables. In Table 16, Qwen2.5-72B-Instruct outperforms Typhoon2-70B-Instruct on MTBench-EN (9.28 vs 8.85), GSM8K-EN (94.6 vs 93.4), HumanEval-EN (87.2 vs 83.5), MBPP-EN (90.5 vs 84.9), and FC-EN (77.9 vs 65.7). Similar gaps appear at 7-8B scale in Table 15, for example HumanEval-EN 81.1 for Qwen2.5-7B vs 68.9 for Typhoon2-Llama-8B. The conclusion should be scoped to the specific tasks and languages where Typhoon2 actually leads, with the trade-offs stated explicitly.
minor comments (4)
  1. [Table 12] The 'FuncCall' column for the 1B and 3B models is marked with '?' while Tables 8-9 report BFCL results for these sizes. Please clarify whether these models were trained on function-calling data and evaluated, or evaluated without that training stage.
  2. [Section 3.2.2] The Thai math and code test sets are described as translations made with an early Typhoon2 model and GPT-4o, but these translated test sets are not released. Reporting construction details and releasing the sets would improve reproducibility and allow readers to assess translation quality.
  3. [Table 25] The notation 'Accuracy x' is not explained in the table caption, and the 'Average(Accuracy)' row appears to ignore cells marked 'x'. Please clarify how missing values are handled in the average.
  4. [Section 3.1.2] The code-switching metric counts non-Thai characters and would penalize legitimate loanwords or code-mixed Thai-English usage that is natural in context; please state this limitation explicitly.

Circularity Check

1 steps flagged · score 6.0 of 10

ThaiExam/M3Exam are both the optimization target and the headline measurement: Section 2.3 selects data mixtures by these scores, Section 2.5 concedes contamination/overfitting, and Table 2 repurposes the same scores as evidence.

  1. fitted input called prediction [Section 2.3 (Data Mixture), Section 2.5 (Evaluation), Table 2, Section 6 (Conclusions)]
    "We examine this by performing CPT on the 1.5B Qwen2.5 (Yang et al., 2024a) model using a similar recipe to Blakeney et al. (2024) and verify that each of the data sources improves one of the metrics or scores based on M3Exam (Zhang et al., 2023b) and/or ThaiExam (Pipatanakul et al., 2023). ... the scores obtained using these datasets are highly above the average level of a typical Thai person. This can be attributed to contamination and saturation due to overfitting (Fourrier et al., 2024)."

    The data-mixture selection in Section 2.3 is driven by improvements on M3Exam and ThaiExam, and Section 2.5 explicitly calls these scores a 'development signal.' The same two benchmarks are then presented in Table 2 as the main 'Exam in Thai' evidence, and the conclusion claims 'superior performance across a majority of evaluated tasks.' The reported exam gains are therefore not an independent measurement of Thai ability; they are the objective used to choose the pretraining mixture. The paper's own admission of contamination and overfitting makes the circularity explicit. This is partial rather than total circularity because instruction-following, function-calling, math/code, and other evaluations (IFEval, BFCL, GSM8K, MATH, etc.) remain independent of the development signal.

full rationale

The paper contains no formal mathematical derivation, so most of its claims are empirical reports rather than theorems. The main circular step is the use of ThaiExam and M3Exam as both the development signal (Section 2.3) and the headline evaluation evidence (Table 2). Since data sources and mixtures were verified by checking improvements on these exact benchmarks, the later 'superior performance' on those benchmarks is partly forced by the selection procedure, not independently established. The paper itself concedes that scores are 'highly above the average level of a typical Thai person' and attributes this to contamination and overfitting. This does not invalidate the many independent evaluations (IFEval, MT-Bench, BFCL, GSM8K, MATH, HumanEval, MBPP, NIAH, TTS/ASR metrics), which support the model's capabilities on their own terms. It does mean the Thai-exam and general-Thai-language claims are partially circular and should be treated as a selection-on-the-test-set result rather than a validated generalization. The safety evaluation also relies on an in-house Thai Topic test set split from the training distribution, but that is a standard held-out split and is accompanied by external benchmarks, so it adds only mild self-reference, not circularity. Overall score 6 reflects the partial circularity of the central Thai-exam claim while acknowledging the independent content elsewhere.

Assumptions & free parameters 3 free parameters · 2 assumptions · 0 invented entities

The central claims rest on empirical choices rather than derivations. The free parameters are data mixture ratios and thresholds selected by experiments; the axioms are assumptions about base model quality and evaluation validity.

free parameters (3)
  • Thai-to-English SFT data ratio = 3:7
    Chosen empirically in Section 3.1.3 to optimize Thai performance; affects all instruction-tuned models.
  • DARE merge density and weight for Llama-3.3-70B = density 0.2, weight [0.4,0.4,0.0,0.0]
    Manually searched in Section 3.6; used for the 70B instruct model.
  • Harm threshold for safety labeling = 5 (score 1-10)
    Selected in Section 3.10.1 to binarize LLM-judged harm scores; affects the safety classifier training data.
assumptions (2)
  • domain assumption Base open models (Llama 3.1/3.2, Qwen2.5/2-VL) provide a sufficiently strong foundation for Thai adaptation.
    The entire approach builds on these models without validating whether an alternative base would change the conclusions.
  • domain assumption ThaiExam and M3Exam scores are meaningful proxies for Thai language ability.
    Section 2.5 states scores are above human averages and may be affected by contamination, yet they are used as the development signal for pre-training.

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Cite this review

Pith. "Pith review of Typhoon 2: A Family of Open Text and Multimodal Thai Large Language Models." pith.science (2026). https://pith.science/paper/4FALN3SA

@misc{pith2026241213702,
  author       = {Pith},
  title        = {Pith review of: Typhoon 2: A Family of Open Text and Multimodal Thai Large Language Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4FALN3SA}},
  note         = {Machine review of arXiv:2412.13702}
}
read the original abstract

This paper introduces Typhoon 2, a series of text and multimodal large language models optimized for the Thai language. The series includes models for text, vision, and audio. Typhoon2-Text builds on state-of-the-art open models, such as Llama 3 and Qwen2, and we perform continual pre-training on a mixture of English and Thai data. We employ post-training techniques to enhance Thai language performance while preserving the base models' original capabilities. We release text models across a range of sizes, from 1 to 70 billion parameters, available in both base and instruction-tuned variants. To guardrail text generation, we release Typhoon2-Safety, a classifier enhanced for Thai cultures and language. Typhoon2-Vision improves Thai document understanding while retaining general visual capabilities, such as image captioning. Typhoon2-Audio introduces an end-to-end speech-to-speech model architecture capable of processing audio, speech, and text inputs and generating both text and speech outputs.

Figures

Figures reproduced from arXiv: 2412.13702 by the authors.

Figure 1
Figure 1. Thai Pretraining Data Mixture 7 [PITH_FULL_IMAGE:figures/full_fig_p007_1.png] view at source ↗
Figure 2
Figure 2. Evaluation of Typhoon2-Llama3.1-8B-Instruct on Needle-in-a-Haystack for both [PITH_FULL_IMAGE:figures/full_fig_p016_2.png] view at source ↗
Figure 3
Figure 3. Evaluation of Typhoon2-Llama3.1-70B-Instruct on Needle-in-a-Haystack for both [PITH_FULL_IMAGE:figures/full_fig_p016_3.png] view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: Evaluation of Typhoon2-Qwen2.5-7B-Instruct on Needle-in-a-Haystack for both [PITH_FULL_IMAGE:figures/full_fig_p017_4.png]
Figure 5
Figure 5. Figure 5: Pipeline of Thai topic data generation Initially, we investigate direct text classification through LLM prompting (e.g., “classify the following text"). However, this approach is less effective than the scoring method and lacks the flexibility to adjust model behaviors…
Figure 6
Figure 6. Figure 6: Agentic Refine Ground Truth Framework We adhere to the outlined automated design framework specific to agentic systems as docu￾mented in Hu et al. (2024) for structuring each individual agent. Thereafter, our method 30 [PITH_FULL_IMAGE:figures/full_fig_p030_6.png]
Figure 7
Figure 7. Figure 7: An Example of Agentic Refine Ground Truth [PITH_FULL_IMAGE:figures/full_fig_p032_7.png]
Figure 8
Figure 8. Figure 8: Typhoon2-Audio End-to-End Model Architecture [PITH_FULL_IMAGE:figures/full_fig_p035_8.png]
Figure 9
Figure 9. Figure 9: Speech Instruction Following Data Creation Pipeline [PITH_FULL_IMAGE:figures/full_fig_p038_9.png]
Figure 10
Figure 10. Figure 10: Text Generation (left) and Speech Generation (right) [PITH_FULL_IMAGE:figures/full_fig_p041_10.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

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Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.