This article explores how artificial intelligence often interprets human instructions literally, failing to grasp underlying intent or context. This can lead to unexpected or even comical outcomes due to AI's lack of nuanced understanding.
This content explores the critical role of pairwise preference in evaluating Large Language Models (LLMs). It discusses how this method can help align LLM performance more effectively with human judgment.
This research introduces PolyFact, a multilingual factual QA dataset, to address cross-lingual factual inconsistency in LLMs. It finds that reinforcement learning via GRPO consistently improves cross-lingual factual recall and generalization compared to supervised fine-tuning.
CAF-Gen is a multi-agent framework designed to enrich shallow argument structures into CAF-compliant models, addressing limitations in current Argument Mining techniques. It employs an iterative Creator-Reviewer pipeline to ensure structural integrity and mitigate instability.
This research explores the emergence of Sinophobic behavior within online web communities during the COVID-19 pandemic. It highlights instances of anti-Chinese sentiment and related hate speech in digital spaces.
This research paper explores the automatic extraction of data from brain MRI reports using the open-weight large language model LLaMA 3.1. It evaluates the LLM's performance in analyzing Dutch neuroradiology reports, demonstrating high zero-shot performance.
This research introduces a novel task: bidirectional small-granularity search between code and text, aiming to link scientific publications with corresponding code segments. It proposes a large dataset, partially generated by GPT-4, and a modular approach that achieves good in-domain results.
This research proposes an unsupervised method to identify community-specific slang and unique entities by analyzing the magnitude of semantic shift. Semantic shift is defined as the evolution of a word's encoded representation after fine-tuning a pre-trained Large Language Model (LLM) on a community-specific text corpus.
This study presents the first application of a Retrieval Augmented Generation (RAG) model for Nepali legal question answering, addressing data scarcity in low-resource languages. Using BM25 on chunked documents, the RAG pipeline achieved high precision and truthfulness, demonstrating its effectiveness in the Nepali legal domain.
This paper investigates implicit causal graph construction from text by inferring intermediate causal events using Large Language Models (LLMs). It compares end-to-end graph construction with causal chain discovery methods and evaluates the validity of inferred causal relations against a manually curated database.
This article details the five engineering stages of building an AI RAG system named LongTermMemory, which transforms PDFs into Q&As. It covers the full document processing pipeline, from text extraction and semantic chunking to using a vector database and Retrieval Augmented Generation (RAG), powered by Laravel and FastAPI services.
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.
easyaligner is a new, performant forced alignment library offering GPU acceleration and flexible text normalization, compatible with all w2v2 models on Hugging Face Hub. It addresses common challenges in speech-to-text preprocessing, such as handling partial transcripts, irrelevant audio, and long segments without chunking.
![easyaligner: Forced alignment with GPU acceleration and flexible text normalization (compatible with all w2v2 models on HF Hub) [P]](/cdn-cgi/image/width=3840,quality=75,format=webp/https://preview.redd.it/f4d5krhkjyvg1.png?width=140&height=46&auto=webp&s=b704f6e672544b79bafac59f00c49f9f56a3fa6c)
This content introduces a new side project and its GitHub repository, focusing on mitigating LLM hallucination through a novel contrastive sampling and selective training method. The core idea treats hallucination as a preference problem, using self-generated negative samples and divergence-based, gated learning to push correct answers and suppress wrong ones.
![New project about llm hallucination [P]](/cdn-cgi/image/width=3840,quality=75,format=webp/https://preview.redd.it/b9qn0mpye4xg1.png?width=140&height=74&auto=webp&s=955d8bf1d2ce779e50e0b5ff03462f4c8a8e3d5d)
This article describes how AI can automate the triage and prioritization of client design feedback. Using layered parsing, AI detects urgency and classifies requests, transforming vague text into actionable, structured data for greater efficiency.
DMax é um novo paradigma para modelos de linguagem de difusão (dLLMs) eficientes que mitiga o acúmulo de erros na decodificação paralela. Ele permite um paralelismo agressivo ao reformular a decodificação como um processo de auto-refinamento progressivo e introduzir uma estratégia de treinamento unificada.
The author highlights the underdiscussed issue of text normalization in streaming text-to-speech models, where errors occur in pronouncing dates, URLs, and other basic elements. They reference a benchmark comparing commercial TTS models on these specific challenges.
The author details how the Qwen3.6 35Ba3 AI model has profoundly reshaped their development workflows and computer usage, enabling them to automate complex tasks and interact with the operating system using natural language. This transformation allows them to delegate tasks like devops, content creation, and code testing to AI, highlighting a significant shift in productivity.
The HKJudge project introduces the first sentence-level, expert-annotated legal discourse corpus of Hong Kong criminal judgments, comprising approximately 290k sentences. It utilizes a two-tier discourse schema to identify what courts find, how they reason, and what they rule, with high inter-annotator agreement.
This foundational study explores authorship attribution using stylistic features to support actor analysis in threat intelligence, testing methods on Japanese web reviews. While BERT fine-tuning performed best overall, TF-IDF with logistic regression showed superior stability and accuracy when scaling to hundreds of authors.