Information Retrieval
portes grátis
Information Retrieval
30th China Conference, CCIR 2024, Wuhan, China, October 18-20, 2024, Revised Selected Papers
Ren, Zhaochun; Tang, Ruiming; He, Xiangnan
Springer Nature Switzerland AG
01/2025
152
Mole
9789819617098
Pré-lançamento - envio 15 a 20 dias após a sua edição
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.- Play to Your Strengths: Collaborative Intelligence of Conventional Recommender Models and Large Language Models.
.- A Dual-Aligned Model for Multimodal Recommendation.
.- CASINet: A Context-Aware Social Interaction Rumor Detection Network.
.- A Claim Decomposition Benchmark for Long-form Answer Verification.
.- Dual-granularity Hierarchical Fusion Network for Multimodal Humor Recognition on Memes.
.- Exploring the Potential of Dimension Reduction in Building Efficient Dense Retrieval Systems.
.- Relation Extraction Model Based on Overlap Rules and Abductive Learning.
.- Multi-task Instruction Tuning for Temporal Question Answering over Knowledge Graphs.
.- On the Capacity of Citation Generation by Large Language Models.
.- Are Large Language Models More Honest in Their Probabilistic or Verbalized Confidence?.
.- QUITO: Accelerating Long-Context Reasoning through Query-Guided Context Compression.
.- A Dual-Aligned Model for Multimodal Recommendation.
.- CASINet: A Context-Aware Social Interaction Rumor Detection Network.
.- A Claim Decomposition Benchmark for Long-form Answer Verification.
.- Dual-granularity Hierarchical Fusion Network for Multimodal Humor Recognition on Memes.
.- Exploring the Potential of Dimension Reduction in Building Efficient Dense Retrieval Systems.
.- Relation Extraction Model Based on Overlap Rules and Abductive Learning.
.- Multi-task Instruction Tuning for Temporal Question Answering over Knowledge Graphs.
.- On the Capacity of Citation Generation by Large Language Models.
.- Are Large Language Models More Honest in Their Probabilistic or Verbalized Confidence?.
.- QUITO: Accelerating Long-Context Reasoning through Query-Guided Context Compression.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Information systems;Information retrieval;Retrieval models and ranking;Language models;Learning to rank;Similarity measures;Novelty in information retrieval;Top-k retrieval in databases
.- Play to Your Strengths: Collaborative Intelligence of Conventional Recommender Models and Large Language Models.
.- A Dual-Aligned Model for Multimodal Recommendation.
.- CASINet: A Context-Aware Social Interaction Rumor Detection Network.
.- A Claim Decomposition Benchmark for Long-form Answer Verification.
.- Dual-granularity Hierarchical Fusion Network for Multimodal Humor Recognition on Memes.
.- Exploring the Potential of Dimension Reduction in Building Efficient Dense Retrieval Systems.
.- Relation Extraction Model Based on Overlap Rules and Abductive Learning.
.- Multi-task Instruction Tuning for Temporal Question Answering over Knowledge Graphs.
.- On the Capacity of Citation Generation by Large Language Models.
.- Are Large Language Models More Honest in Their Probabilistic or Verbalized Confidence?.
.- QUITO: Accelerating Long-Context Reasoning through Query-Guided Context Compression.
.- A Dual-Aligned Model for Multimodal Recommendation.
.- CASINet: A Context-Aware Social Interaction Rumor Detection Network.
.- A Claim Decomposition Benchmark for Long-form Answer Verification.
.- Dual-granularity Hierarchical Fusion Network for Multimodal Humor Recognition on Memes.
.- Exploring the Potential of Dimension Reduction in Building Efficient Dense Retrieval Systems.
.- Relation Extraction Model Based on Overlap Rules and Abductive Learning.
.- Multi-task Instruction Tuning for Temporal Question Answering over Knowledge Graphs.
.- On the Capacity of Citation Generation by Large Language Models.
.- Are Large Language Models More Honest in Their Probabilistic or Verbalized Confidence?.
.- QUITO: Accelerating Long-Context Reasoning through Query-Guided Context Compression.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
