{"product_id":"advanced-intelligent-computing-technology-and-applications-9789819500130","title":"Advanced Intelligent Computing Technology and Applications","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cstrong\u003e.- Natural Language Processing and Computational Linguistics.\u003cbr\u003e\u003c\/strong\u003e.- Can LLM be a Good Path Planner based on Prompt Engineering? Mitigating  the Hallucination for Path Planning.\u003cbr\u003e.- ModalLogicBench: Unveiling Modal Logic Reasoning Abilities of Large  Language Models.\u003cbr\u003e.- A Source Template-based Data Augmentation Method for Low-Resource  Neural Machine Translation.\u003cbr\u003e.- Exploring Behavior-Driven Development for Code Generation.\u003cbr\u003e.- LLM- Based Data Synthesis and Distillation for High-Quality Text-to-SQL  Training.\u003cbr\u003e.- External Knowledge-Enhanced Semi-supervised Multi-Label Short Text  Classification.\u003cbr\u003e.- Bridging Knowledge Gaps: Fine-Tuned RAG Frameworks for Biomedical  Evidence-Based Question Answering.\u003cbr\u003e.- MTAOS: Aspect-Level Opinion Summarization with Opinion Phrase  Masking.\u003cbr\u003e.- COMLoRA: A chain-based LoRA architecture combined with MoE.\u003cbr\u003e.- Sentence Trunk Fusion for Neural Machine Translation.\u003cbr\u003e.- ProCFD: Towards Robust Multimodal Sentiment Analysis through Prototype  Fusion and Contrastive Feature Decomposition.\u003cbr\u003e.- T3: A Novel Zero-shot Transfer Learning Framework Iteratively Training on  an Assistant Task for a Target Task.\u003cbr\u003e.- ALMP: Automatic Layer-by-layer Mixed-Precision Quantization For Large  Language Models.\u003cbr\u003e.- Can we employ LLM to meta-evaluate LLM-based evaluators? A  Preliminary Study.\u003cbr\u003e.- EmbSpeech: A Unified Framework Towards Low-Resource Zero-Shot  Speech Synthesis.\u003cbr\u003e.- SViQA: A Unified Speech-Vision Multimodal Model for Textless Visual  Question Answering.\u003cbr\u003e.- Event Causality Extraction via Label-Aware Multi-Prompt Generation  Network.\u003cbr\u003e.- Improving Low-Resource Neural Machine Translation with Dependency  Distance-based Self-Attention.\u003cbr\u003e.- Automated Coding Utterances toward Chinese Course Core Competence  with Large Language Models.\u003cbr\u003e.- Introspective Reward Modeling via Inverse Reinforcement Learning for  LLM Alignment.\u003cbr\u003e.- BERTFAN: Multi-Layer Feature Fusion and Data Augmentation for  Sentiment Analysis.\u003cbr\u003e.- Instruction Tuning with Data Augmentation for Event Argument Extraction.\u003cbr\u003e.- EQAA-MAC: Enhancing Question Answering Accuracy via Multi-Agent  Cooperation in IT Operations.\u003cbr\u003e.- Cross-domain Constituency Parsing with Multi-LLM Debate.\u003cbr\u003e.- Unified Option Generation for Zero- and Few-shot Emotion and Cause  Analysis in Dialogues.\u003cbr\u003e.- Open-World Knowledge Augmentation for Zero-Shot Information  Extraction in LLMs.\u003cbr\u003e.- Prompting Large Models for Knowledge and Reasoning Augmentation in  KB-VQA.\u003cbr\u003e.- IterSelectTune: An Iterative Data Selection Framework for Efficient  Instruction Tuning.\u003cbr\u003e.- Utilize unbiased contrastive learning to enhance the key emotional features  in low-resource sentiment analysis.\u003cbr\u003e.- Post-training Performance Boosting Method for Code Large Language  Models via Model Merging.\u003cbr\u003e.- Automated Construction of High-quality Evaluation Datasets Based on  LLMs.\u003cbr\u003e.- Enhancing Code Generation for Large Language Models Using Fine-Grained Distillation.\u003cbr\u003e.- Morphological Recombination-Based Neural Machine Translation with Self Supervised Data Augmentation.\u003cbr\u003e.- From Coarse to Fine: Chinese Spelling Correction Based on LoRA  Technology and Multi-Agent Collaboration.\u003cbr\u003e.- Using External knowledge to Enhanced PLM for Semantic Matching.\u003cbr\u003e.- UnCert-CoT: Uncertainty-Aware Chain-of-Thought for Code Generation  with Large Language Model.\u003cbr\u003e.- Towards Reliable Large Language Models: A Survey on Hallucination  Detection.\u003cbr\u003e.- KPEE: A Two-Stage Proposal-Based Reformulation of Event Extraction.\u003cbr\u003e.- Morphology-Driven Meta-Adapter for Low-Resource Mongolian Sentiment  Analysis.\u003cbr\u003e.- Knowledge Graph Completion Combining Dynamic Learnability and  Contrastive Learning.\u003cbr\u003e.- FlexKG: A Flexible Framework for Enhanced Reasoning over Knowledge  Graph with Large Language Model.\u003cbr\u003e.- Enhancing Code Search Fine-Tuning with Momentum Contrastive Learning  and Cross-Modal Matching.\u003cbr\u003e.- RECODE: Leveraging Reliable Self-Generated Tests and Fine-Grained  Execution Feedback to Enhance LLM-Based Code Generation.\u003cbr\u003e.- Evidence-Augmented Generative Explanation for Health Rumor Detection.\u003c\/p\u003e","brand":"Springer","offers":[{"title":"Default Title","offer_id":53212848062807,"sku":"9789819500130","price":66.49,"currency_code":"GBP","in_stock":true}],"url":"https:\/\/bookcurl.com\/products\/advanced-intelligent-computing-technology-and-applications-9789819500130","provider":"Book Curl","version":"1.0","type":"link"}