Large Language Models excel in machine translation but face challenges with high computational costs and latency, making knowledge transfer to smaller models crucial. Traditional distillation methods, however, overlook student models’ existing capabilities, leading to redundant training on mastered knowledge and insufficient coverage of novel content. This paper presents WTA-MT, a Weakness-Targeted Augmentation framework for targeted knowledge transfer from LLMs to smaller models. By focusing on the student’s specific weaknesses instead of indiscriminate distillation, and leveraging LLM teachers to generate targeted supplementary data, WTA-MT efficiently enhances performance. Experiments in both general (En → Zh, De → En) and medical (En → De) domains demonstrate that WTA-MT outperforms traditional Sequence Knowledge Distillation (SeqKD) methods, achieving comparable or superior performance with significantly less data. Notably, our method eliminates performance deficiencies in low-scoring samples and while preserving high-quality translations, effectively narrowing the knowledge gap between small and large models.
Multi-domain machine translation remains a challenge when domain labels are unavailable during inference. Prior approaches typically assume a explicit domain from inputs, limiting their applicability in real-world scenarios. We propose a lightweight yet effective LLM-assisted framework that eliminates the requirement for explicit domain labels. Our method explores two paradigms: (1) LLM as Router, where a large language model predicts the domain and routes the input to the corresponding expert model; and (2) LLM as Ranker, where the LLM selects the best output among multiple expert candidates. Experiments on four diverse domains show that the our method achieves an average BLEU score of 43.53, only 1.62 BLEU lower than the upper-bound expert KNN-MT model with gold domain labels (45.15), while requiring no explicit domain labels. Moreover, while expert in-domain models suffer sharply deterioration on out-of-domain inputs, our method maintains strong robustness, effectively mitigating domain shift. The proposed methods combine the cross-domain adaptability of LLMs with the strong in-domain accuracy of expert NMT models, offering a plug-and-play, training-free solution without additional training.
Recent advances in Large Speech-Language Models (LSLMs) demonstrate strong speech understanding and cross-modal interaction abilities. However, the lack of standardized evaluation methods hinders their development. Existing evaluation approaches face three limitations:(1) Inconsistent datasets prevent fair model comparisons; (2) Current benchmarks focus on specific speech tasks but fail to assess responses to direct speech instructions; (3) Critical aspects like security and robustness are overlooked. To address these issues, we propose ISBench, a benchmark for evaluating LSLMs’ instruction-following capability and safety. Our framework introduces acoustic scenario simulations covering speaker characteristics (gender/age/emotion), environmental factors (background noise), and linguistic variations (colloquial expressions). Through comprehensive experiments with seven open-source models, we reveal key findings: LSLMs show performance gaps between speech and text modalities, exhibit weaker performance with children’s voices, and demonstrate significant sensitivity to noise and informal language. IS-Bench provides researchers with a unified evaluation platform to advance LSLM development.
Sentiment analysis in low-resource languages faces several challenges. This paper addresses the challenges of extremely low-resource and methodologically understudied sentiment analysis for Tigrigna, an official language spoken in Eritrea and the Tigray region of Ethiopia. We introduce TIGSEN, the first large-scale, multi-domain benchmark dataset for Tigrigna sentiment analysis, comprising 68,596 annotated text samples from official social media, news, and review forums. Created through a rigorous native-speakers annotation protocol, the dataset is designed to enable robust model training and evaluation. To prove its utility and to establish a strong, reproducible baseline for the community, we propose and evaluate a systematic cross-transfer learning framework. This methodology deliberately leverages annotated data from high-resource to linguistically related languages, English and Amharic, to overcome the limitations of Tigrigna’s small data pool. Our experiments show that models fine-tuned directly on TIGSEN achieved competitive performance. At the same time, the proposed cross-transfer framework yields a significant performance gain, achieving an accuracy of 87.6
While Large Language Models (LLMs) have shown promising capabilities in machine translation, their outputs often lack controllability and the ability to leverage error correction for translation improvement. To address this, we propose a two-stage framework, Diagnose-Then-Optimize (DTO), for structured translation quality enhancement. In the first stage, we fine-tune the large language model using human-annotated error data, enabling it to leverage translation error information for translation correction. In the second stage, we construct a preference dataset using response comparisons evaluated by ChatGPT, focusing on error correctness, correction effectiveness. We apply Direct Preference Optimization (DPO) to refine the model’s output behaviors based on these preferences. Our method demonstrates strong post-editing capabilities, consistently improving translation quality across WMT23 different systems’ outputs. The most significant gains are observed in English-Chinese, highlighting the model’s effectiveness in correcting diverse and complex translation errors. Experiments on WMT23 datasets across English–German, English–Russian, and English–Chinese demonstrate that DTO consistently improves the base LLaMA-3-8B, outperforming large-scale machine translation models such as NLLB_Greedy and Aya-23-35B in COMET scores. Our results highlight the effectiveness of combining structured error supervision with preference-driven fine-tuning, offering a robust and interpretable solution for controllable translation correction.
