arxiv: 2604.19782 · v1 · submitted 2026-03-30 · 💻 cs.CL · cs.AI· cs.SD· eess.AS

Recognition: 1 theorem link

· Lean Theorem

KoALa-Bench: Evaluating Large Audio Language Models on Korean Speech Understanding and Faithfulness

Jinyoung Kim , Hyeongsoo Lim , Eunseo Seo , Minho Jang , Keunwoo Choi , Seungyoun Shin , Ji Won Yoon

Authors on Pith no claims yet

Pith reviewed 2026-05-14 22:27 UTC · model grok-4.3

classification 💻 cs.CL cs.AIcs.SDeess.AS

keywords large audio language modelsKorean speech understandingspeech faithfulnessbenchmark evaluationmultilingual speech tasksautomatic speech recognitionspeech translation

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The pith

KoALa-Bench introduces six tasks to evaluate how well large audio language models understand Korean speech and remain faithful to the audio input.

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

The paper introduces KoALa-Bench, a benchmark with six tasks for testing large audio language models on Korean speech. Four tasks check core abilities such as recognizing speech, translating it, answering questions about it, and following spoken instructions. Two tasks specifically check speech faithfulness, because many models ignore audio details and rely on other cues instead. The tasks use material from Korean college entrance exams and cultural topics to match local knowledge needs. The authors test several models and release the benchmark for public use.

Core claim

KoALa-Bench comprises six tasks for Korean speech evaluation in large audio language models. Four tasks assess fundamental understanding through automatic speech recognition, speech translation, speech question answering, and speech instruction following. The remaining two tasks measure speech faithfulness, based on the finding that several models fail to fully leverage the speech modality. The benchmark draws listening questions from the Korean college scholastic ability test and content from Korean cultural domains.

What carries the argument

KoALa-Bench, a set of six tasks that separately measures speech understanding capabilities and speech faithfulness while using Korea-specific exam and cultural materials.

If this is right

Models can be ranked directly on standardized Korean speech tasks that include both understanding and faithfulness checks.
The faithfulness tasks make it possible to quantify how often models bypass the audio input.
Public release of the benchmark and code allows repeated testing as new models appear.
Results on college-exam listening questions can reveal specific gaps in handling academic Korean content.

Where Pith is reading between the lines

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

The same structure of faithfulness tasks could be replicated for other languages to check whether audio-ignoring behavior is widespread.
Models that pass the faithfulness tests may support more reliable spoken interactions in real Korean applications such as voice assistants.
Training data that explicitly rewards use of the speech modality might reduce the need for post-hoc faithfulness checks.

Load-bearing premise

The six chosen tasks together with Korea-specific content from college exams and cultural domains are enough to capture the main challenges of Korean speech understanding and faithfulness.

What would settle it

A demonstration that models achieve high scores on these tasks yet still ignore speech details in new, untested Korean audio situations would show the benchmark misses important cases.

Figures

Figures reproduced from arXiv: 2604.19782 by Eunseo Seo, Hyeongsoo Lim, Jinyoung Kim, Ji Won Yoon, Keunwoo Choi, Minho Jang, Seungyoun Shin.

**Figure 3.** Figure 3: Per-layer EAR for (a) the evidence sentence [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗

**Figure 2.** Figure 2: Model behavior under correct speech contexts [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗

read the original abstract

Recent advances in large audio language models (LALMs) have enabled multilingual speech understanding. However, benchmarks for evaluating LALMs remain scarce for non-English languages, with Korean being one such underexplored case. In this paper, we introduce KoALa-Bench, a comprehensive benchmark for evaluating Korean speech understanding and speech faithfulness of LALMs. In particular, KoALa-Bench comprises six tasks. Four tasks evaluate fundamental speech understanding capabilities, including automatic speech recognition, speech translation, speech question answering, and speech instruction following, while the remaining two tasks evaluate speech faithfulness, motivated by our observation that several LALMs often fail to fully leverage the speech modality. Furthermore, to reflect Korea-specific knowledge, our benchmark incorporates listening questions from the Korean college scholastic ability test as well as content covering Korean cultural domains. We conduct extensive experiments across six models, including both white-box and black-box ones. Our benchmark, evaluation code, and leaderboard are publicly available at https://ksbench.github.io/Korean-Benchmark/.

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.

Desk Editor's Note private letter to a colleague

KoALa-Bench is a practical new Korean benchmark for LALMs, though its faithfulness tasks may not fully isolate speech modality issues.

read the letter

KoALa-Bench introduces the first dedicated benchmark for evaluating large audio language models on Korean speech understanding and faithfulness. It includes six tasks: four covering core capabilities like automatic speech recognition, speech translation, speech question answering, and instruction following, plus two focused on faithfulness. The content draws from Korean college scholastic ability test questions and cultural domains, which helps make the evaluation more relevant. The paper does well in making the benchmark, code, and leaderboard publicly available right away. Running experiments across six models, including both open and closed ones, gives an immediate picture of current performance levels. This kind of release is useful for the community. The softer part is the faithfulness evaluation. The motivation is that some models fail to fully use the speech input, but without clear controls such as paired audio and text versions of the same questions, it is hard to attribute errors specifically to modality neglect rather than general difficulty or missing background knowledge. The Korea-specific material is appropriate but does not resolve that potential confound on its own. This paper is aimed at researchers working on multilingual or non-English audio language models who need better test sets. It fills an underexplored area, so it deserves peer review to let others examine the task details and any additional validation in the full version.

Referee Report

1 major / 2 minor

Summary. The manuscript introduces KoALa-Bench, a benchmark for evaluating large audio language models (LALMs) on Korean speech understanding and faithfulness. It consists of six tasks: four for fundamental capabilities (automatic speech recognition, speech translation, speech question answering, and speech instruction following) and two for speech faithfulness. The benchmark uses Korea-specific content from college scholastic ability tests and cultural domains. Experiments are conducted on six models, with the benchmark, code, and leaderboard made publicly available.

Significance. If the faithfulness tasks are shown to isolate modality reliance, the benchmark would address a clear gap in non-English LALM evaluation by providing Korea-specific tasks and public resources. The release of evaluation code and a leaderboard supports reproducibility and community use, which is a positive contribution for the field.

major comments (1)

[§3.2] §3.2 (Faithfulness task definitions): The motivation states that LALMs 'often fail to fully leverage the speech modality,' yet the task descriptions provide no explicit controls such as paired audio/text inputs on identical content or prompts requiring modality comparison. Without these, it is unclear whether lower performance on the faithfulness tasks reflects failure to use speech input versus general task difficulty or cultural knowledge gaps; this directly affects the central claim that the two tasks evaluate speech faithfulness.

minor comments (2)

[Table 1] Table 1 (task overview): the column headers for input/output modalities could be clarified with an additional row or footnote to distinguish audio-only from text-augmented conditions.
[§4] §4 (Experiments): the model selection criteria for the six evaluated LALMs are not stated; adding a brief justification for including both white-box and black-box models would improve transparency.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback and the recommendation for major revision. We address the single major comment below and will incorporate clarifications and supporting experiments in the revised manuscript to strengthen the presentation of the faithfulness tasks.

read point-by-point responses

Referee: [§3.2] §3.2 (Faithfulness task definitions): The motivation states that LALMs 'often fail to fully leverage the speech modality,' yet the task descriptions provide no explicit controls such as paired audio/text inputs on identical content or prompts requiring modality comparison. Without these, it is unclear whether lower performance on the faithfulness tasks reflects failure to use speech input versus general task difficulty or cultural knowledge gaps; this directly affects the central claim that the two tasks evaluate speech faithfulness.

Authors: We appreciate this observation. The two faithfulness tasks are built from Korean CSAT listening questions and culturally grounded spoken content, where the audio supplies modality-specific cues (intonation, timing, and phonetic details) that are not recoverable from text transcripts alone. This construction is intended to probe whether models rely on the speech signal. We agree, however, that the current manuscript does not explicitly describe paired audio/text controls or modality-comparison prompts. In the revision we will expand §3.2 with a dedicated paragraph on task construction, add text-only baseline results for the same question sets, and include a short analysis showing performance drops when speech is removed. These additions will make the isolation of speech faithfulness clearer without altering the benchmark itself. revision: partial

Circularity Check

0 steps flagged

No circularity: straightforward benchmark release with no derivations or self-referential reductions

full rationale

The paper introduces KoALa-Bench as a new evaluation suite with six explicitly defined tasks (ASR, speech translation, SQA, instruction following, plus two faithfulness tasks) drawn from Korean college exams and cultural content. No equations, fitted parameters, predictions, or uniqueness theorems appear. The motivation for faithfulness tasks is stated as an observation without reducing to any self-citation chain or ansatz. All content is self-contained against external benchmarks and does not rely on prior author work for its core claims.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No free parameters, axioms, or invented entities; the contribution is an empirical benchmark built from existing task types and public Korean exam content.

pith-pipeline@v0.9.0 · 5511 in / 1009 out tokens · 36230 ms · 2026-05-14T22:27:33.496590+00:00 · methodology

discussion (0)

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Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

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unclear
Relation between the paper passage and the cited Recognition theorem.

We propose two novel tasks, SCA-QA and PA-QA, to evaluate the speech faithfulness of LALMs in terms of modality and position... SCF Score... Evidence Attention Ratio (EAR)

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Reference graph

Works this paper leans on

16 extracted references · 16 canonical work pages · 2 internal anchors

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Qwen2-Audio Technical Report

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work page internal anchor Pith review Pith/arXiv arXiv 2023
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Ella Neeman, Roee Aharoni, Or Honovich, Leshem Choshen, Idan Szpektor, and Omri Abend

Efficient multi-prompt evaluation of llms.Ad- vances in Neural Information Processing Systems, 37:22483–22512. Ella Neeman, Roee Aharoni, Or Honovich, Leshem Choshen, Idan Szpektor, and Omri Abend. 2023. DisentQA: Disentangling parametric and contextual knowledge with counterfactual question answering. InProceedings of the 61st Annual Meeting of the Assoc...

work page arXiv 2023
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Qwen3-Omni Technical Report

Qwen3-omni technical report.Preprint, arXiv:2509.17765. Qian Yang, Jin Xu, Wenrui Liu, Yunfei Chu, Ziyue Jiang, Xiaohuan Zhou, Yichong Leng, Yuanjun Lv, Zhou Zhao, Chang Zhou, and 1 others. 2024. Air- bench: Benchmarking large audio-language models via generative comprehension. InProceedings of the 62nd Annual Meeting of the Association for Compu- tationa...

work page internal anchor Pith review Pith/arXiv arXiv 2024
[4]

ᄅ ᅵᄀ ᅳ ᄋ ᅩ ᄇ ᅳ ᄅ ᅦ전ᄃ ᅳ

Halle-control: controlling object hallucina- tion in large multimodal models.arXiv preprint arXiv:2310.01779. Tianyi Zhang*, Varsha Kishore*, Felix Wu*, Kilian Q. Weinberger, and Yoav Artzi. 2020. Bertscore: Eval- uating text generation with bert. InInternational Conference on Learning Representations. Jingming Zhuo, Songyang Zhang, Xinyu Fang, Haodong Du...

work page arXiv 2020
[5]

<One-shot prompt>다음 음성을듣고질문에맞는 답을고르세요

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[6]

<One-shot prompt>주어진음성을듣고 가장적절한답을선택하세요

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[7]

<One-shot prompt>음성을 잘듣고알맞은답을골라주세요

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[8]

<One-shot prompt>아래음성의내용을참고하여올바른답을선택하세요. Answer Suffix:\n답: One-shot prompt: 예시:\n질문:철수는어디에서친구를 만났나요?\n(A)학교(B)공원(C)도서관(D)카페\n답: (B)\n\n SIF (Speech Instruction Following) Objective: Assess the model’s ability to follow spoken instructions. Prompts: 1)음성에서제시된질문을참고해주시기바랍니다. 2)음성의정보를활용하여다음질문에답해주세요. 3)음성을듣고다음질문에답해주세요. 4)음성내용을바탕으로다음질문에답해주세요. SCA-QA (Speech Co...

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[9]

<One-shot example>다음질문에답변하세요

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[10]

<One-shot example>질문에맞는 답을고르세요

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[11]

<One-shot example>아래질문에가장적절한답을선택하세요

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[12]

Prompt (with speech context):

<One-shot example>주어진선택지 중올바른답을고르세요. Prompt (with speech context):

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[13]

<One-shot prompt>음성에서들려준내용만을근거로다음질문에답변하세요

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[14]

<One-shot prompt>음성에서제공된정보에만기반하여질문에맞는 답을고르세요

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[15]

<One-shot prompt>반드시음성에서들은내용을바탕으로아래질문에가장적절한 답을선택하세요

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[16]

네가그런일을해서자랑스럽다

<One-shot prompt>음성의내용만을참고하여주어진선택지 중올바른답을고르세요. One-shot prompt: 예시: 질문:철수는어디에서친구를 만났나요? (A)학교(B)공원(C)도서관(D)카페 답: (B) 17 A.10.2 SCA-QA Dataset Construction Prompts Stage 1: Question and Answer Generation Objective: Generate a factual question and a short answer from the given context. 문단(context)을 읽고,그내용으로답할수있는자세한질문을만든 뒤,정답을한단어(또는짧은구)로써 주세요. -질문:문단에나온사실을묻는...

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