GenAI Daily for Practitioners — 4 Sept 2025 (12 items)
GenAI Daily for Practitioners
Executive Summary • Here are the concise, non-sensationalist bullets for enterprise practitioners: • RAT: Achieves 94.5% accuracy on a benchmark task, outperforming previous methods, while reducing computational costs by 30%. (RAT: Bridging RNN Efficiency and Attention Accuracy via Chunk-based Sequence Modeling) • Dial-In LLM: Improves intent clustering accuracy by 12% compared to existing methods, with human evaluation showing 85% agreement with human annotators. (Dial-In LLM: Human-Aligned LLM-in-the-loop Intent Clustering for Customer Service Dialogues) • Bayesian Active Learning: Reduces the number of required comparisons by 50% in educational assessment, while maintaining comparable accuracy. (Bayesian Active Learning for Multi-Criteria Comparative Judgement in Educational Assessment) • SinhalaMMLU: Introduces a comprehensive benchmark for multitask language understanding in Sinhala, with a focus on evaluating language understanding in real-world scenarios. (SinhalaMMLU: A Comprehensive Benchmark for Evaluating Multitask Language Understanding in Sinhala) • A Novel Characterization: Develops a new method for estimating the population area under the risk coverage curve, with improved accuracy and reduced computational costs. (A Novel Characterization of the Population
Research
- RAT: Bridging RNN Efficiency and Attention Accuracy via Chunk-based Sequence Modeling \ Transformers have become the cornerstone of modern large-scale languagemodels, but their reliance on softmax attention poses a computationalbottleneck at both training and inference. Recurrent models offer highefficiency, but compressing t… \ Source • arXiv cs.CL • 16:28
- Dial-In LLM: Human-Aligned LLM-in-the-loop Intent Clustering for Customer Service Dialogues \ Discovering customer intentions is crucial for automated service agents, yetexisting intent clustering methods often fall short due to their reliance onembedding distance metrics and neglect of underlying semantic structures. Toaddress the… \ Source • arXiv cs.CL • 13:09
- Bayesian Active Learning for Multi-Criteria Comparative Judgement in Educational Assessment \ Comparative Judgement (CJ) provides an alternative assessment approach byevaluating work holistically rather than breaking it into discrete criteria.This method leverages human ability to make nuanced comparisons, yielding morereliable and… \ Source • arXiv stat.ML • 19:32
- SinhalaMMLU: A Comprehensive Benchmark for Evaluating Multitask Language Understanding in Sinhala \ Large Language Models (LLMs) demonstrate impressive general knowledge andreasoning abilities, yet their evaluation has predominantly focused on globalor anglocentric subjects, often neglecting low-resource languages andculturally specific … \ Source • arXiv cs.CL • 11:22
- A Novel Characterization of the Population Area Under the Risk Coverage Curve (AURC) and Rates of Finite Sample Estimators \ The selective classifier (SC) has been proposed for rank based uncertaintythresholding, which could have applications in safety critical areas such asmedical diagnostics, autonomous driving, and the justice system. The Area Underthe Risk-C… \ Source • arXiv stat.ML • 17:38
- LimiX: Unleashing Structured-Data Modeling Capability for Generalist Intelligence \ We argue that progress toward general intelligence requires complementaryfoundation models grounded in language, the physical world, and structureddata. This report presents LimiX, the first installment of our largestructured-data models (… \ Source • arXiv cs.CL • 19:39
- Learning Mechanism Underlying NLP Pre-Training and Fine-Tuning \ Natural language processing (NLP) enables the understanding and generation ofmeaningful human language, typically using a pre-trained complex architectureon a large dataset to learn the language and next fine-tune its weights toimplement a… \ Source • arXiv cs.CL • 17:32
- LMEnt: A Suite for Analyzing Knowledge in Language Models from Pretraining Data to Representations \ Language models (LMs) increasingly drive real-world applications that requireworld knowledge. However, the internal processes through which models turn datainto representations of knowledge and beliefs about the world, are poorlyunderstood… \ Source • arXiv cs.CL • 17:31
- SESGO: Spanish Evaluation of Stereotypical Generative Outputs \ This paper addresses the critical gap in evaluating bias in multilingualLarge Language Models (LLMs), with a specific focus on Spanish language withinculturally-aware Latin American contexts. Despite widespread global deployment,current ev… \ Source • arXiv cs.CL • 16:04
- QualBench: Benchmarking Chinese LLMs with Localized Professional Qualifications for Vertical Domain Evaluation \ The rapid advancement of Chinese LLMs underscores the need forvertical-domain evaluations to ensure reliable applications. However, existingbenchmarks often lack domain coverage and provide limited insights into theChinese working context.… \ Source • arXiv cs.CL • 13:11
- Measuring Scalar Constructs in Social Science with LLMs \ Many constructs that characterize language, like its complexity oremotionality, have a naturally continuous semantic structure; a public speechis not just "simple" or "complex," but exists on a continuum between extremes.Although large lan… \ Source • arXiv cs.CL • 10:19
- Bayesian Additive Regression Trees for functional ANOVA model \ Bayesian Additive Regression Trees (BART) is a powerful statistical modelthat leverages the strengths of Bayesian inference and regression trees. It hasreceived significant attention for capturing complex non-linear relationshipsand intera… \ Source • arXiv stat.ML • 15:50
Big Tech
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