REVIEW 3 major objections 2 minor 2 references
LLMs overgenerate in machine translation by adding text that ranges from risky inventions to helpful clarifications like those a human translator might supply.
Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →
T0 review · grok-4.3
2026-05-10 11:19 UTC
load-bearing objection The paper sketches a spectrum of LLM overgenerations in translation but provides no evidence that the categories can be told apart reliably. the 3 major comments →
Fabricator or dynamic translator?
The pith
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
Large language models used for machine translation produce overgenerations that include self-explanations, risky confabulations, and appropriate explanations; the last category enables the model to function as a human translator would by supplying information that increases target-audience understanding, and commercial detection methods can be applied to distinguish among these categories even though the task remains difficult.
What carries the argument
A taxonomy that separates LLM overgenerations into self-explanations, risky confabulations, and appropriate explanations, paired with commercial detection strategies that attempt to label each instance in running translation output.
Load-bearing premise
The different kinds of overgeneration can be told apart reliably enough in practice that commercial detection tools yield usable results without needing extra validation steps.
What would settle it
A side-by-side review in which several human annotators independently label the same collection of LLM translation outputs as self-explanation, risky confabulation, or appropriate explanation and produce agreement rates below 70 percent.
If this is right
- Translation quality assessment must expand beyond fidelity metrics to judge whether added text harms or helps the reader.
- Commercial MT pipelines can incorporate filters that suppress risky outputs while retaining helpful explanations.
- LLM-based translators may deliver higher effective comprehension than traditional systems when appropriate explanations are preserved.
- Deployment decisions will favor LLMs over older MT engines once reliable separation of the three overgeneration types becomes routine.
Where Pith is reading between the lines
- The spectrum of additions suggests LLMs are moving from literal translators toward interpreters that adapt content for audience needs.
- Future systems could be trained explicitly to produce only the helpful category of explanation, turning overgeneration into a deliberate feature.
- User interfaces for machine translation may need to surface the added explanations with clear labels so readers can judge their value.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The manuscript examines overgeneration in LLM-based machine translation, arguing that these differ from neurobabble in neural MT. The overgenerations are categorized into LLM self-explanations, risky confabulations, and appropriate explanations that allow the LLM to function like a human translator, improving comprehension for the target audience. The authors explore various detection strategies in a commercial context and present results on their application.
Significance. Should the distinctions between overgeneration types prove reliable and the detection methods effective, the work could inform the development of more transparent and user-friendly LLM translation systems by highlighting beneficial explanatory behaviors versus problematic ones.
major comments (3)
- [Abstract] The abstract claims that results on detection strategies were obtained but omits any description of the methods, data used, evaluation metrics, or error analysis, preventing assessment of the reported findings.
- [Categorization of overgenerations] The paper does not provide explicit criteria, annotation guidelines, or reliability metrics (such as inter-annotator agreement) for distinguishing between self-explanations, risky confabulations, and appropriate explanations. This distinction is central to the claim that LLMs can act as dynamic translators.
- [Detection strategies and results] Without details on how the strategies were tested or what the specific results were (e.g., precision, recall, or qualitative examples), the effectiveness claims cannot be verified.
minor comments (2)
- The abstract could benefit from a brief mention of the language pairs or domains considered in the commercial setting.
- Ensure that all terms like 'neurobabble' are defined or referenced upon first use.
Simulated Author's Rebuttal
We thank the referee for their thoughtful and constructive comments, which highlight important areas for improving the clarity and rigor of our manuscript. We address each major comment point by point below, agreeing that additional details are needed, and outline the revisions we will make.
read point-by-point responses
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Referee: [Abstract] The abstract claims that results on detection strategies were obtained but omits any description of the methods, data used, evaluation metrics, or error analysis, preventing assessment of the reported findings.
Authors: We agree that the abstract is overly concise and lacks sufficient detail on methods, data, metrics, or analysis, which hinders evaluation of the claims. While the full manuscript describes the strategies explored in a commercial setting, we will revise the abstract to incorporate a brief overview of the detection approaches, the commercial dataset characteristics, key evaluation metrics (e.g., precision and recall), and a high-level summary of the error analysis performed. revision: yes
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Referee: [Categorization of overgenerations] The paper does not provide explicit criteria, annotation guidelines, or reliability metrics (such as inter-annotator agreement) for distinguishing between self-explanations, risky confabulations, and appropriate explanations. This distinction is central to the claim that LLMs can act as dynamic translators.
Authors: The categorization presented is based on qualitative analysis of LLM outputs. We acknowledge that the absence of explicit criteria, annotation guidelines, and inter-annotator agreement metrics limits the reproducibility and strength of this central distinction. We will add a new subsection detailing the annotation guidelines, providing concrete examples for each category, and reporting inter-annotator agreement scores from our internal annotation process. revision: yes
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Referee: [Detection strategies and results] Without details on how the strategies were tested or what the specific results were (e.g., precision, recall, or qualitative examples), the effectiveness claims cannot be verified.
Authors: The manuscript does outline the strategies and presents results from our commercial application. However, we recognize that more granular information is required for full verification. We will expand the relevant section to describe the testing methodology in detail, report specific quantitative results including precision, recall, and other metrics, and include qualitative examples of the detected overgenerations and their classifications. revision: yes
Circularity Check
No significant circularity: paper contains no derivations, equations, or load-bearing predictions.
full rationale
The manuscript is a qualitative description of LLM overgeneration behaviors in translation, categorizing them as self-explanations, risky confabulations, or appropriate explanations, and reporting commercial detection strategies plus results. No mathematical derivations, fitted parameters presented as predictions, uniqueness theorems, or self-citation chains are present or invoked to support central claims. The distinctions and results are framed as empirical observations from explored strategies rather than any chain that reduces by construction to the paper's own inputs. This is the expected non-finding for a non-formal, non-predictive paper.
Axiom & Free-Parameter Ledger
read the original abstract
LLMs are proving to be adept at machine translation although due to their generative nature they may at times overgenerate in various ways. These overgenerations are different from the neurobabble seen in NMT and range from LLM self-explanations, to risky confabulations, to appropriate explanations, where the LLM is able to act as a human translator would, enabling greater comprehension for the target audience. Detecting and determining the exact nature of the overgenerations is a challenging task. We detail different strategies we have explored for our work in a commercial setting, and present our results.
Figures
Reference graph
Works this paper leans on
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[1]
Halomi: A manually annotated benchmark for multilingual hallucination and omission detection in machine translation.Preprint, arXiv:2305.11746. Zi-Yi Dou and Graham Neubig. 2021. Word alignment by fine-tuning embeddings on parallel corpora. In Conference of the European Chapter of the Associa- tion for Computational Linguistics (EACL). Fangxiaoyu Feng, Yi...
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[2]
Outside, there’s a balcony or terrace offering a view of the sea
Quality estimation with force-decoded atten- tion and cross-lingual embeddings. InProceedings of the Third Conference on Machine Translation: Shared Task Papers, pages 816–821, Belgium, Brus- sels. Association for Computational Linguistics. A Missed minimally detached overgenerations Included below in Table 5 are overgenerations from the minimally detache...
discussion (0)
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