Rating Prediction in Brazilian Portuguese: From Classical Features to Large Language Models

MTEB-BR: A Text Embedding Benchmark for Brazilian Portuguese

MTEB-BR is introduced, a benchmark of 22 native Brazilian-Portuguese tasks across seven categories (classification, multilabel classification, pair classification, semantic textual similarity, clustering, retrieval, and reranking), admitting only data created or found in Portuguese and excluding translations by construction.

Beyond Multilingual Averages: MTEB-PT, a Benchmark for Portuguese Sentence Encoders

MTEB-PT is presented, a Portuguese benchmark constructed from a subset of MMTEB, comprising 14 existing datasets across Semantic Textual Similarity (STS), classification, retrieval, and reranking, and shows that language-specific fine-tuning still improves model performance in Portuguese, especially on task types that match the adaptation data most closely.

Comparative Reliability Analysis of Open-Source Large Language Models in Subjective Text Evaluation

NormasTCU --- A Brazilian Portuguese IR Dataset and an Evaluation of LLM-as-a-Judge for Relevance Assessment

Portuguese Information Retrieval (IR) lacks public datasets, and relevance assessment for specialized collections remains costly. While Large Language Models (LLMs) increasingly support relevance assessment, their reliability in non-English specialized domains remains unclear. We introduce NormasTCU (https://huggingface.co/datasets/LeandroRibeiro/NormasTCU), a Brazilian Portuguese IR dataset with 14,469 legal documents, 46 queries, and 3,048 human judgments over 812 query-document pairs. Using NormasTCU, we evaluated LLM-as-a-judge for relevance assessment by prompting three models with two prompt techniques to grade these pairs. We then compared the rankings of 15 IR systems derived from LLM-generated and human reference qrels. LLMs consistently showed a positive scoring bias (mean absolute error: 0.46--0.66 on a 0-2 scale). Furthermore, pair-level agreement with human judgments achieved only fair to moderate levels, with Cohen's kappa ranging from 0.32 to 0.53. Despite this bias, LLM-generated judgments often yielded highly similar system rankings for nDCG@10 and MRR (observed Kendall's tau greater than or equal 0.90, although the bootstrap confidence intervals did not always remain above this threshold), but were less reliable for P@10 and R@10. Notably, LLM-based rankings were sometimes more strongly correlated with the reference ranking than individual human annotations were. As a practical implication, our results suggest that LLMs could effectively support scalable relevance assessment in specialized Portuguese corpora when evaluated using nDCG or MRR (rank-aware metrics), but they should be avoided when relying on precision or recall.

LLM-Assisted Sentiment Analysis for Indonesia's Coretax Policy: A Knowledge Distillation and Human-in-the-Loop Approach

The lack of high-quality labeled datasets remains a major challenge for sentiment analysis in low-resource languages such as Indonesian, particularly in specialized domains like fiscal policy. This study investigates the effectiveness of Large Language Models (LLMs) as automated annotators within a teacher-student knowledge distillation framework. Using social media data from X related to Indonesia's Coretax system, three training scenarios were evaluated: AI-labeled data, human-labeled data, and a hybrid approach. The results show that GPT-4o achieves substantial agreement with human annotators, with a Cohen's Kappa score of 0.61. Furthermore, the student model IndoBERT trained on the combined dataset outperforms other configurations, achieving a Macro F1-score of 0.64 and a Macro ROC-AUC of 0.84. These findings indicate that while LLMs cannot fully replace human judgment, they significantly enhance scalability and enable near real-time policy evaluation in low-resource settings through effective human-AI collaboration.

Assessing Quality of Experience in Natural Language Generation of German Text

The rapid advancement of Natural Language Generation (NLG) has made the reliable evaluation of generated text increasingly critical, as these systems, such as large language models (LLMs), are now widely deployed in real-world applications. However, traditional automatic metrics fail to capture the multifaceted nature of perceived quality. In this paper, we introduce TextQ-German, a novel dataset suite for human-centered evaluation of German NLG from a Quality of Experience (QoE) perspective, covering automatic text summarization and machine translation. Through crowdsourcing studies with German speakers, we collect human quality ratings and identify relevant perceptual quality dimensions for each task. We develop automatic QoE prediction models, including transformer-based, linguistic feature-based, and hybrid approaches. Hybrid models outperform pure transformer baselines in almost all experimental settings, while linguistic features alone can approach the performance of fine-tuned language models. The dataset is extended with LLM-generated outputs annotated with overall QoE scores. Final validation on held-out sets indicates generalization to unseen data. Our work contributes a publicly accessible resource for NLG evaluation and baselines for automatic QoE prediction, providing a foundation for developing NLG systems that better align with human quality perception.