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      Cathay Financial Holdings Manfaatkan Model Bahasa Kecil Sumber-Terbuka Kenal Pasti Niat Pelanggan


      News provided by

      Cathay Financial Holdings

      12 Jun, 2026, 06:36 CST

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      Penalaan Halus Model Bahasa Kecil untuk Lebih Memahami Konteks Perkhidmatan Kewangan Tempatan, Istilah Domain dan Pertanyaan Pelanggan yang Samar-samar

      TAIPEI, 12 Jun 2026 /PRNewswire/ -- Bagi meningkatkan kecekapan operasi dan pengalaman pelanggan, Cathay Financial Holdings (Cathay FHC) terus memajukan aplikasi kecerdasan buatan (AI) generatif dalam perkhidmatan kewangan menerusi rangka kerja teknikal AI generatifnya, GAIA, serta strategi AI-sebagai-Perkhidmatan (AIaaS). Berdasarkan pengesahan pada tahun lalu terhadap model bahasa besar (LLM) untuk aplikasi kewangan, baru-baru ini Cathay FHC membentangkan dapatan penyelidikan AI terkininya di NVIDIA GTC Taipei 2026, menunjukkan bagaimana model bahasa kecil (SLM) sumber terbuka boleh ditala halus untuk pengelasan niat pelanggan dan diaplikasikan dalam senario perkhidmatan kewangan pada masa depan.

      Continue Reading
      Cathay Financial Holdings presented validation results for open-source small language models (SLMs) at NVIDIA GTC Taipei 2026, demonstrating how fine-tuned SLMs can support customer intent classification while enhancing AI's understanding of local financial service contexts, domain-specific terminology, and ambiguous customer queries. (Source: Cathay Financial Holdings)
      Cathay Financial Holdings presented validation results for open-source small language models (SLMs) at NVIDIA GTC Taipei 2026, demonstrating how fine-tuned SLMs can support customer intent classification while enhancing AI's understanding of local financial service contexts, domain-specific terminology, and ambiguous customer queries. (Source: Cathay Financial Holdings)

      Kajian tersebut menilai beberapa model sumber terbuka yang utama daripada Meta, TAIDE, TAME, NVIDIA dan OpenAI. Keputusan awal menunjukkan bahawa, dalam rangka kerja ujian, SLM yang ditala halus boleh mengurangkan pergantungan kepada modul kejuruteraan prompt dan dapatan semula vektor yang kompleks, yang berpotensi memudahkan seni bina sistem sambil mengurangkan kerumitan operasi serta penyelenggaraan pada masa akan datang.

      Dapatan tersebut menunjukkan bahawa apabila digabungkan dengan set data domain-kewangan yang direka dengan teliti serta penalaan halus model bersasar, SLM mampu meningkatkan lagi kestabilan model, kecekapan inferens dan kebolehkawalan pelaksanaan. Bagi pengelasan niat pelanggan, SLM yang telah ditala halus mencapai prestasi yang hampir setanding dengan LLM sumber tertutup arus perdana, malah menghampiri prestasi LLM proprietari terkemuka, sekali gus menyediakan rujukan praktikal kepada perusahaan dalam menilai strategi latihan dan pelaksanaan model AI.

      Dari perspektif tadbir urus data dan privasi, kajian tersebut menggunakan pendekatan data sintetik sepenuhnya bagi memastikan tiada maklumat pelanggan sebenar digunakan semasa proses latihan model. Melalui teknik termasuk pengelompokan fungsi perkhidmatan, reka bentuk set data niat tunggal dan pelbagai niat, penyetempatan konteks Taiwan serta pengembangan kata kunci, Cathay FHC dapat memperkukuh keupayaan model dalam memahami konteks perkhidmatan kewangan tempatan, istilah khusus industri dan pertanyaan pelanggan yang samar-samar.

      Aplikasi berpotensi pada masa depan termasuk pertanyaan baki gadai janji, bantuan pembayaran kad kredit dan pengemudian perkhidmatan cawangan, sekali gus meletakkan asas bagi carian pintar, penghalaan perkhidmatan dan pengalaman interaksi pelanggan generasi akan datang.

      Dari sudut seni bina teknikal, Cathay FHC mengintegrasikan alat AI NVIDIA—termasuk NVIDIA NeMo Customizer, NVIDIA NeMo Curator dan NVIDIA TensorRT-LLM—berserta sumber pengkomputeran seni bina NVIDIA Hopper, bagi menyokong penjanaan data, penalaan halus model, pengoptimuman inferens dan penilaian eksperimen. Dengan memanfaatkan ekosistem NVIDIA AI, Cathay FHC terus memperkukuh keupayaannya dalam pembangunan model domain kewangan, tadbir urus data dan pengesahan aplikasi.

      Sejak beberapa tahun kebelakangan ini, Cathay FHC telah memperluas inovasi AI secara berterusan merentasi pelbagai senario kewangan, membina asas teknologi yang boleh diskalakan meliputi pengoptimuman proses dalaman, peningkatan perkhidmatan pelanggan, pemahaman pengetahuan kewangan dan tadbir urus model. Ketika institusi kewangan mengemudi persekitaran yang semakin dikawal selia, keperluan tadbir urus data yang ketat serta jangkaan pelanggan yang berkembang pantas, Cathay FHC kekal komited untuk memajukan penyelidikan AI dengan cara yang mematuhi peraturan, selamat dan berdaya tahan.

      Memandang ke hadapan, Cathay FHC akan terus meneroka pengelasan konteks panjang, pemahaman dokumen kewangan lanjutan dan aplikasi AI merentas senario. Dengan membangunkan pendekatan latihan dan pelaksanaan model yang disesuaikan dengan sektor kewangan, syarikat berhasrat untuk mempercepat inovasi serta mewujudkan perkhidmatan kewangan yang lebih pintar, cekap dan berpusatkan pelanggan.

      SOURCE Cathay Financial Holdings

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