Oct 2026· Brazilian Symposium in Information and Human Language Technology· pp. 375-389· 0 citations· 19 references
Sentiment Analysis and Opinion MiningTopic Modeling
Abstract
The In-Context Learning (ICL) paradigm enables adapting LLMs without parameter tuning. This work evaluates the impact of prompt engineering on 9B models for Brazilian Portuguese sentiment classification, comparing six prompting formats (fewand zero-shot) across three linguistically diverse models (Gemma2-9B-it, Boto-9B-IT, Qwen3.5-9B) and four public datasets, anchored by a fine-tuned encoder (MSA-DistilBERT, ∼0.1B) and a 685B MoE model (DeepSeek-V3.2) under a unified protocol. Results show that all 9B models outperform the encoder and recover 71–101% of the weak-to-strong gap, with Qwen3.5-9B achieving the highest mean coverage (94.5%). Statistical tests indicate that well-defined instructions are the main driver of ICL performance, capturing most of the achievable accuracy even in zero-shot settings, while elaborate formats (roleplay, JSON schema, meta-prompting) add no consistent gain over a minimal instruction-plus-demonstrations prompt, offering practical guidance for deploying SLMs in resource-constrained PT-BR NLP scenarios.
This study systematically evaluates generative pre-trained transformer (GPT)-based large language models for the automated writing evaluation of Italian as a second language (L2). Drawing on 1,832 learner texts, we compare six GPT-based experimental conditions: zero-shot and few-shot prompting with GPT-4.1 and GPT-5, a...
Retrieving semantically similar demonstrations is widely reported to improve in-context learning, but that evidence comes largely from classification and short-form generation, where demonstrations fit comfortably in context. We ask whether the benefit transfers to long-document news summarization, and whether it survi...
Rakha Putra Ristianto, Andria Arisal, D. Kurniasari et al.· International Conference on...· 0 citations
Large language models (LLMs) are increasingly used in education, but evidence on their performance comes mainly from high-resource languages. We evaluated five commercial LLMs in Kazakh in a zero-shot setting: sentiment classification on 300 class-balanced KazSAnDRA reviews and question answering (QA) on the same 300 r...
M. Kemelbek, Z. Nurbekova, A. Sagimbayeva· Information· 0 citations
Startup growth and sustainability are heavily impacted by user comments. This research has done a comparative study between large language models (LLMs), small language models (SLMs), and traditional machine learning (ML) methods in classifying user comments on startups. The performances of bidirectional encoder repres...
Li-Hua Li, Agus Cahyo Nugroho· IAES International Journal o...· 0 citations
It is concluded that Transformer performance depends more on DSPP than multilingual coverage alone, as the latter does not guarantee optimal performance on a highly specialized pragmatic classification task.
Fahad Saud Al Hussen, King Saud University, Riyadh et al.· 0 citations
Related blog posts
MIT News · Artificial Intelligence· news.mit.eduOct 18, 2026
A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.
Microsoft Research Blog· microsoft.comOct 15, 2026
Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and quantum-resistant security. The post Proof-based general-purpose computing with Magnetar appeared first on Microsoft Research.
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
