CompactQE: Interpretable Translation Quality Estimation via Small Open-Weight LLMs

• URL: http://arxiv.org/abs/2605.15763v1
• Date: Fri, 15 May 2026 09:22:55 GMT
• Title: CompactQE: Interpretable Translation Quality Estimation via Small Open-Weight LLMs
• Abstract summary: Current state-of-the-art Quality Estimation in machine translation relies on massive, proprietary LLMs.<n>We demonstrate that smaller, open-source LLMs are a viable, cost-effective and privacy-preserving alternative.
• Score: 0.5599792629509229
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Current state-of-the-art Quality Estimation (QE) in machine translation relies on massive, proprietary LLMs, raising data privacy concerns. We demonstrate that smaller, open-source LLMs (<30B parameters) are a viable, cost-effective and privacy-preserving alternative. Using a single-pass prompting strategy, our models simultaneously generate quality scores, MQM error annotations, suggested error corrections, and full post-editions. Our analysis shows these models achieve highly competitive system-level correlations with human judgments that outperform traditional neural metrics, fine-tuned models, and human inter-annotator agreement, effectively approximating the capabilities of much larger proprietary LLMs.

Related papers

• Human Grounded Evaluation of Large Language Models for Optical Network Automation [1.0709603403936399]
  Large language models (LLMs) are increasingly adopted for network automation, yet their output quality and inference cost can vary substantially.<n>We present HuGLEN, a stepwise evaluation pipeline that uses an LLM-as-a-judge together with a small set of expert ratings.<n>We demonstrate HuGLEN for translating outputs from an explainable artificial intelligence (XAI) model for the optical network quality of transmission (QoT) estimation task into operator-friendly explanations.
  arXiv  Detail & Related papers  (2026-07-20T15:36:00Z)
• Robust LLM Performance Certification via Constrained Maximum Likelihood Estimation [14.465019747508604]
  We propose a new, practical, and efficient approach to LLM failure rate estimation based on constrained maximum-likelihood estimation (MLE)<n>We validate our approach through a comprehensive empirical study, benchmarking it against state-of-the-art baselines like Prediction-Powered Inference (PPI)
  arXiv  Detail & Related papers  (2026-03-11T21:48:40Z)
• Decoupling Perception and Calibration: Label-Efficient Image Quality Assessment Framework [78.58395822978271]
  LEAF is a Label-Efficient Image Quality Assessment Framework.<n>It distills perceptual quality priors from an MLLM teacher into a lightweight student regressor.<n>Our method significantly reduces the need for human annotations while maintaining strong MOS-aligned correlations.
  arXiv  Detail & Related papers  (2026-01-28T15:15:17Z)
• Uncertainty-Aware Collaborative System of Large and Small Models for Multimodal Sentiment Analysis [17.98292973608615]
  We propose a novel Uncertainty-Aware Collaborative System (U-ACS) that orchestrates a powerful MLLM and a lightweight baseline model for multimodal sentiment analysis.<n>Our proposed method achieves state-of-the-art performance, while requiring only a fraction of the computational resources compared to using a standalone MLLM.
  arXiv  Detail & Related papers  (2025-08-27T16:01:58Z)
• Preference Leakage: A Contamination Problem in LLM-as-a-judge [69.96778498636071]
  Large Language Models (LLMs) as judges and LLM-based data synthesis have emerged as two fundamental LLM-driven data annotation methods.<n>In this work, we expose preference leakage, a contamination problem in LLM-as-a-judge caused by the relatedness between the synthetic data generators and LLM-based evaluators.
  arXiv  Detail & Related papers  (2025-02-03T17:13:03Z)
• Rational Tuning of LLM Cascades via Probabilistic Modeling [0.9208007322096532]
  We present a probabilistic model for the joint performance distribution of a sequence of large language models (LLMs)<n>Compared to selecting confidence thresholds using Bayesian optimization, our Markov parametric-copula model yields more favorable error-cost trade-offs.<n>Our framework's inductive assumptions about the interactions between the error rates of different LLMs enhance sample efficiency.
  arXiv  Detail & Related papers  (2025-01-16T07:58:33Z)
• Efficient Self-Improvement in Multimodal Large Language Models: A Model-Level Judge-Free Approach [31.654345704242512]
  This paper introduces a novel, model-level judge-free self-improvement framework.<n>Our approach employs a controlled feedback mechanism while eliminating the need for MLLMs in the verification loop.<n>We achieve superior precision and recall with significantly lower computational demands.
  arXiv  Detail & Related papers  (2024-11-26T00:44:37Z)
• Attribute Controlled Fine-tuning for Large Language Models: A Case Study on Detoxification [76.14641982122696]
  We propose a constraint learning schema for fine-tuning Large Language Models (LLMs) with attribute control. We show that our approach leads to an LLM that produces fewer inappropriate responses while achieving competitive performance on benchmarks and a toxicity detection task.
  arXiv  Detail & Related papers  (2024-10-07T23:38:58Z)
• Large Language Models Must Be Taught to Know What They Don't Know [97.90008709512921]
  We show that fine-tuning on a small dataset of correct and incorrect answers can create an uncertainty estimate with good generalization and small computational overhead.<n>We also investigate the mechanisms that enable reliable uncertainty estimation, finding that many models can be used as general-purpose uncertainty estimators.
  arXiv  Detail & Related papers  (2024-06-12T16:41:31Z)
• Illuminating Blind Spots of Language Models with Targeted Agent-in-the-Loop Synthetic Data [9.982616173090264]
  Language models (LMs) have achieved impressive accuracy across a variety of tasks but remain vulnerable to high-confidence misclassifications (UUs) UUs cluster into blind spots in the feature space, leading to significant risks in high-stakes applications. We propose a novel approach to address blind spot mitigation through the use of intelligent agents as teachers to characterize UU-type errors.
  arXiv  Detail & Related papers  (2024-03-26T16:49:25Z)
• Calibrating Large Language Models with Sample Consistency [76.23956851098598]
  We explore the potential of deriving confidence from the distribution of multiple randomly sampled model generations, via three measures of consistency. Results show that consistency-based calibration methods outperform existing post-hoc approaches. We offer practical guidance on choosing suitable consistency metrics for calibration, tailored to the characteristics of various LMs.
  arXiv  Detail & Related papers  (2024-02-21T16:15:20Z)
• From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning [52.257422715393574]
  We introduce a self-guided methodology for Large Language Models (LLMs) to autonomously discern and select cherry samples from open-source datasets. Our key innovation, the Instruction-Following Difficulty (IFD) metric, emerges as a pivotal metric to identify discrepancies between a model's expected responses and its intrinsic generation capability.
  arXiv  Detail & Related papers  (2023-08-23T09:45:29Z)
• The Devil is in the Errors: Leveraging Large Language Models for Fine-grained Machine Translation Evaluation [93.01964988474755]
  AutoMQM is a prompting technique which asks large language models to identify and categorize errors in translations. We study the impact of labeled data through in-context learning and finetuning. We then evaluate AutoMQM with PaLM-2 models, and we find that it improves performance compared to just prompting for scores.
  arXiv  Detail & Related papers  (2023-08-14T17:17:21Z)

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.