Towards Reliable Machine Translation: Scaling LLMs for Critical Error Detection and Safety

• URL: http://arxiv.org/abs/2602.11444v1
• Date: Wed, 11 Feb 2026 23:47:39 GMT
• Title: Towards Reliable Machine Translation: Scaling LLMs for Critical Error Detection and Safety
• Authors: Muskaan Chopra, Lorenz Sparrenberg, Rafet Sifa,
• Abstract summary: Critical meaning errors can undermine reliability, fairness, and safety of multilingual systems.<n>In this work, we explore the capacity of instruction-tuned Large Language Models (LLMs) to detect such errors.<n>Our findings show that model scaling and adaptation strategies (zero-shot, few-shot, fine-tuning) yield consistent improvements.
• Score: 1.4517170578045737
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine Translation (MT) plays a pivotal role in cross-lingual information access, public policy communication, and equitable knowledge dissemination. However, critical meaning errors, such as factual distortions, intent reversals, or biased translations, can undermine the reliability, fairness, and safety of multilingual systems. In this work, we explore the capacity of instruction-tuned Large Language Models (LLMs) to detect such critical errors, evaluating models across a range of parameters using the publicly accessible data sets. Our findings show that model scaling and adaptation strategies (zero-shot, few-shot, fine-tuning) yield consistent improvements, outperforming encoder-only baselines like XLM-R and ModernBERT. We argue that improving critical error detection in MT contributes to safer, more trustworthy, and socially accountable information systems by reducing the risk of disinformation, miscommunication, and linguistic harm, especially in high-stakes or underrepresented contexts. This work positions error detection not merely as a technical challenge, but as a necessary safeguard in the pursuit of just and responsible multilingual AI. The code will be made available at GitHub.

Related papers

• STEAD: Robust Provably Secure Linguistic Steganography with Diffusion Language Model [71.35577462669856]
  We propose a robust, provably secure linguistic steganography with diffusion language models (DLMs)<n>We introduce error correction strategies, including pseudo-random error correction and neighborhood search correction, during steganographic extraction.
  arXiv  Detail & Related papers  (2026-01-21T08:58:12Z)
• Investigating the Multilingual Calibration Effects of Language Model Instruction-Tuning [58.355275813623685]
  This work looks at a critical gap in the calibration of large language models (LLMs) within multilingual settings.<n>Even in low-resource languages, model confidence can increase significantly after instruction-tuning on high-resource language SFT datasets.<n>However, improvements in accuracy are marginal or non-existent, highlighting a critical shortcoming of standard SFT for multilingual languages.
  arXiv  Detail & Related papers  (2026-01-04T04:29:12Z)
• Evaluating Language Model Reasoning about Confidential Information [95.64687778185703]
  We study whether language models exhibit contextual robustness, or the capability to adhere to context-dependent safety specifications.<n>We develop a benchmark (PasswordEval) that measures whether language models can correctly determine when a user request is authorized.<n>We find that current open- and closed-source models struggle with this seemingly simple task, and that, perhaps surprisingly, reasoning capabilities do not generally improve performance.
  arXiv  Detail & Related papers  (2025-08-27T15:39:46Z)
• OMNIGUARD: An Efficient Approach for AI Safety Moderation Across Modalities [54.152681077418805]
  Current detection approaches are fallible, and are particularly susceptible to attacks that exploit mismatched generalizations of model capabilities.<n>We propose OMNIGUARD, an approach for detecting harmful prompts across languages and modalities.<n>Our approach improves harmful prompt classification accuracy by 11.57% over the strongest baseline in a multilingual setting.
  arXiv  Detail & Related papers  (2025-05-29T05:25:27Z)
• Do We Really Need Curated Malicious Data for Safety Alignment in Multi-modal Large Language Models? [83.53005932513155]
  Multi-modal large language models (MLLMs) have made significant progress, yet their safety alignment remains limited.<n>We propose finetuning MLLMs on a small set of benign instruct-following data with responses replaced by simple, clear rejection sentences.
  arXiv  Detail & Related papers  (2025-04-14T09:03:51Z)
• Benchmarking Adversarial Robustness to Bias Elicitation in Large Language Models: Scalable Automated Assessment with LLM-as-a-Judge [1.1666234644810893]
  Small models outperform larger ones in safety, suggesting that training and architecture may matter more than scale.<n>No model is fully robust to adversarial elicitation, with jailbreak attacks using low-resource languages or refusal suppression proving effective.
  arXiv  Detail & Related papers  (2025-04-10T16:00:59Z)
• Epistemic Integrity in Large Language Models [10.50127599111102]
  Large language models are increasingly relied upon sources of information, but their propensity for false or misleading statements poses high risks for users and society.<n>In this paper, we confront the critical problem of miscalibration where a model's linguistic assertiveness fails to reflect its true internal certainty.<n>We introduce a new human misalignment evaluation and a novel method for measuring the linguistic assertiveness of Large Language Models.
  arXiv  Detail & Related papers  (2024-11-10T17:10:13Z)
• Mitigating the Language Mismatch and Repetition Issues in LLM-based Machine Translation via Model Editing [39.375342978538654]
  We focus on utilizing Large Language Models (LLMs) to perform machine translation. We observe that two patterns of errors frequently occur and drastically affect the translation quality: language mismatch and repetition. We explore the potential for mitigating these two issues by leveraging model editing methods.
  arXiv  Detail & Related papers  (2024-10-09T16:51:21Z)
• Vicinal Risk Minimization for Few-Shot Cross-lingual Transfer in Abusive Language Detection [19.399281609371258]
  Cross-lingual transfer learning from high-resource to medium and low-resource languages has shown encouraging results. We resort to data augmentation and continual pre-training for domain adaptation to improve cross-lingual abusive language detection.
  arXiv  Detail & Related papers  (2023-11-03T16:51:07Z)
• You Can Generate It Again: Data-to-Text Generation with Verification and Correction Prompting [24.738004421537926]
  Small language models like T5 excel in generating high-quality text for data-to-text tasks.<n>They frequently miss keywords, which is considered one of the most severe and common errors in this task.<n>We explore the potential of using feedback systems to enhance semantic fidelity in smaller language models for data-to-text generation tasks.
  arXiv  Detail & Related papers  (2023-06-28T05:34:25Z)
• InfoBERT: Improving Robustness of Language Models from An Information Theoretic Perspective [84.78604733927887]
  Large-scale language models such as BERT have achieved state-of-the-art performance across a wide range of NLP tasks. Recent studies show that such BERT-based models are vulnerable facing the threats of textual adversarial attacks. We propose InfoBERT, a novel learning framework for robust fine-tuning of pre-trained language models.
  arXiv  Detail & Related papers  (2020-10-05T20:49:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.