Granite-4.1-8B is an 8B parameter long-context instruct model from IBM, enhanced through finetuning and alignment for advanced tool calling, instruction following, and chat capabilities. It supports multiple languages and was released in April 2026 under the Apache 2.0 license.
This content addresses the issue of Word Error Rate (WER) penalizing formatting differences in STT evaluations, leading to inaccurate scores. To solve this, the open-source `gladia-normalization` library was released, which normalizes transcripts before WER calculation, ensuring a fairer assessment of recognition quality.
A self-taught user new to fine-tuning seeks advice on choosing between 3B and 7B LLM models for a multi-task reasoning project. The project involves understanding underlying questions, maintaining multiple perspectives, and handling messy inputs.
The content asks for opinions on the suitability of EMNLP workshops for Vision-Language Model (VLM) evaluation work. It also seeks recommendations for other good NLP venues for this type of research.
This paper introduces PromptNCE, a method to estimate pointwise mutual information (PMI) using only LLMs and contrastive estimation prompts, circumventing the need for task-specific critics. It presents a benchmark with human-derived PMI and shows PromptNCE achieves Spearman correlation up to 0.82.
This content describes how AI automation can transform literature reviews, making PDF screening and data extraction more efficient and reproducible. It suggests an iterative approach and combining tools like GROBID and spaCy for a hands-on workflow.
A startup built Ethiopia's first voice AI tutor for Amharic, revealing that simple translation fails due to the language's unique grammatical structures and cultural contexts. The project encountered complexities with the Ge'ez script and limited training data.
This content discusses the common issue of user searches yielding no results due to a lack of semantic understanding. It proposes building semantic search functionality using Transformers.js and sentence embeddings to improve result relevance.
This paper introduces a practical pipeline to convert text corpora into quantitative semantic signals, employing embeddings, logprob-based evaluation, and noise reduction. The case study applies six semantic dimensions to Portuguese news articles about AI, supporting AI engineering tasks such as corpus inspection and monitoring.
TALM (Tool Augmented Language Models) focuses on integrating external tools with large language models to augment their capabilities. This approach allows LLMs to perform complex tasks more effectively by leveraging specialized functions and real-world interactions.
This article describes building an AI system that generates over 1,000 unique personas for Telegram comments, after human teams failed. It details creating persistent personalities with natural behavior and overcoming technical challenges like anti-bot systems and AI-powered spam moderation.
TRACE-KG é um framework multimodal que constrói grafos de conhecimento enriquecidos por contexto e um esquema induzido, superando limitações de métodos baseados em ontologias ou esquemas livres. Ele organiza entidades e relações usando um esquema guiado por dados, mantendo a rastreabilidade e capturando relações condicionais.
India is emerging as a significant AI market opportunity with over 110 apps across 22 languages and 28 sectors, driven by $3.8B in investment and a large internet user base. The nation is actively closing the language access gap with homegrown foundation models and boasts top AI talent concentration.
This article, a re-publication of Rei-AIOS Paper 110, presents a rigorous information-theoretic comparison between the Braille-Unicode × D-FUMT8 encoding and multimodal embedding schemes like CLIP, BERT, and ImageBind. The research explores representing 256 philosophical states within a single 3-byte UTF-8 character.
This article explores three essential spaCy tricks for developers, aiming to maximize processing speed. It focuses on customizing entity recognition for efficient workflows.
Este conteúdo apresenta CGD-PD, uma camada leve para modelos de linguagem grandes (LLMs) que melhora a resposta a perguntas lógicas de três vias (Verdadeiro/Falso/Desconhecido). Ele aborda falhas recorrentes como inconsistência de negação e previsões 'Desconhecido' epistêmicas, utilizando decisões consistentes e desambiguação baseada em prova para maior precisão.
This research introduces a "finite-answer preference stabilization" method to determine when a language model's answer preference becomes stable before its final output. It shows that this stabilization often occurs before the answer is parseable, with a significant lead time.
This paper proposes SENSE (Semantic Embedding Navigation with Soft-gated Evaluation) to enhance Retrieval-based Speculative Decoding (RSD) for LLMs. SENSE addresses RSD's rigid lexical dependencies by using robust semantic alignment and a soft-gated evaluation module to validate semantic equivalence.
QIMMA é uma nova plataforma de avaliação de LLMs em árabe que prioriza a qualidade, realizando validação sistemática de benchmarks. Ela resolve problemas de qualidade em benchmarks existentes através de revisão automatizada e humana, resultando em um conjunto de avaliação reprodutível e multi-tarefa com mais de 52 mil amostras.
This paper introduces a synthetic contrastive reasoning-trace dataset for multi-table question answering (MMQA), addressing the lack of reasoning supervision in existing resources. Open-weight LLMs fine-tuned with Contrastive Preference Optimization (CPO) using this dataset achieved significant performance improvements, highlighting the benefits of heterogeneous trace generators.