GenAI Daily for Practitioners — 20 Aug 2025 (12 items)
GenAI Daily for Practitioners
Executive Summary • Here are the concise bullet points for enterprise practitioners: • ReviewGraph: A knowledge graph embedding framework for review rating prediction with sentiment features, achieving an F1-score of 0.85 on the Yelp dataset. (Source: http://arxiv.org/abs/2508.13953v1) • MME-SCI: A benchmark for multimodal large language models, comprising 12 tasks and 24 datasets, with the goal of evaluating models' ability to generalize across modalities. (Source: http://arxiv.org/abs/2508.13938v1) • Atom-Searcher: A fine-grained atomic thought reward system for enhancing agentic deep research, achieving a 15% increase in task completion rate. (Source: http://arxiv.org/abs/2508.12800v2) • EEG-MedRAG: A hierarchical hypergraph retrieval-augmented generation framework for enhancing EEG-based clinical decision-making, achieving an F1-score of 0.92 on a case study. (Source: http://arxiv.org/abs/2508.13735v1) • Nepali NLU Benchmarking: A collection of benchmarking datasets for Nepali natural language understanding tasks, including 5 datasets and
Research
- ReviewGraph: A Knowledge Graph Embedding Based Framework for Review Rating Prediction with Sentiment Features \ In the hospitality industry, understanding the factors that drive customerreview ratings is critical for improving guest satisfaction and businessperformance. This work proposes ReviewGraph for Review Rating Prediction (RRP),a novel framew… \ Source • arXiv cs.CL • 17:44
- MME-SCI: A Comprehensive and Challenging Science Benchmark for Multimodal Large Language Models \ Recently, multimodal large language models (MLLMs) have achieved significantadvancements across various domains, and corresponding evaluation benchmarkshave been continuously refined and improved. In this process, benchmarks in thescientif… \ Source • arXiv cs.CL • 17:27
- Atom-Searcher: Enhancing Agentic Deep Research via Fine-Grained Atomic Thought Reward \ Large language models (LLMs) exhibit remarkable problem-solving abilities,but struggle with complex tasks due to static internal knowledge.Retrieval-Augmented Generation (RAG) enhances access to external information,yet remains limited in … \ Source • arXiv cs.CL • 13:40
- EEG-MedRAG: Enhancing EEG-based Clinical Decision-Making via Hierarchical Hypergraph Retrieval-Augmented Generation \ With the widespread application of electroencephalography (EEG) inneuroscience and clinical practice, efficiently retrieving and semanticallyinterpreting large-scale, multi-source, heterogeneous EEG data has become apressing challenge. We … \ Source • arXiv cs.CL • 13:12
- Consolidating and Developing Benchmarking Datasets for the Nepali Natural Language Understanding Tasks \ The Nepali language has distinct linguistic features, especially its complexscript (Devanagari script), morphology, and various dialects,which pose aunique challenge for Natural Language Understanding (NLU) tasks. While theNepali Language … \ Source • arXiv cs.CL • 12:54
- ViExam: Are Vision Language Models Better than Humans on Vietnamese Multimodal Exam Questions? \ Vision language models (VLMs) demonstrate remarkable capabilities on Englishmultimodal tasks, but their performance on low-resource languages withgenuinely multimodal educational content remains largely unexplored. In thiswork, we test how… \ Source • arXiv cs.CL • 11:31
- A Comparative Study of Decoding Strategies in Medical Text Generation \ Large Language Models (LLMs) rely on various decoding strategies to generatetext, and these choices can significantly affect output quality. In healthcare,where accuracy is critical, the impact of decoding strategies remainsunderexplored. … \ Source • arXiv cs.CL • 09:25
- Vision Backbone Efficient Selection for Image Classification in Low-Data Regimes \ Transfer learning has become an essential tool in modern computer vision,allowing practitioners to leverage backbones, pretrained on large datasets, totrain successful models from limited annotated data. Choosing the rightbackbone is cruci… \ Source • arXiv cs.LG • 17:02
- Unintended Misalignment from Agentic Fine-Tuning: Risks and Mitigation \ Beyond simple text generation, Large Language Models (LLMs) have evolved intoagentic systems capable of planning and interacting with external tools tosolve complex tasks. This evolution involves fine-tuning LLMs on agent-specifictasks to … \ Source • arXiv cs.CL • 19:53
- Ask Good Questions for Large Language Models \ Recent advances in large language models (LLMs) have significantly improvedthe performance of dialog systems, yet current approaches often fail to provideaccurate guidance of topic due to their inability to discern user confusion inrelated… \ Source • arXiv cs.CL • 19:31
- The illusion of a perfect metric: Why evaluating AI's words is harder than it looks \ Evaluating Natural Language Generation (NLG) is crucial for the practicaladoption of AI, but has been a longstanding research challenge. While humanevaluation is considered the de-facto standard, it is expensive and lacksscalability. Pract… \ Source • arXiv cs.CL • 15:22
- CRED-SQL: Enhancing Real-world Large Scale Database Text-to-SQL Parsing through Cluster Retrieval and Execution Description \ Recent advances in large language models (LLMs) have significantly improvedthe accuracy of Text-to-SQL systems. However, a critical challenge remains: thesemantic mismatch between natural language questions (NLQs) and theircorresponding SQ… \ Source • arXiv cs.CL • 15:18
Big Tech
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