{"product_id":"advances-in-information-retrieval-9783031887079","title":"Advances in Information Retrieval","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e.- Crossing the Structure Chasm – Querying Data Without Limits.\u003c\/p\u003e\u003cp\u003e.- Understanding the Interplay between LLMs’ Utilisation of Parametric and Contextual Knowledge.\u003c\/p\u003e\u003cp\u003e.- Knowledge Graphs Are Dead, Long Live Knowledge Graphs.\u003c\/p\u003e\u003cp\u003e.- LIBRA: Measuring Bias of Large Language Model from a Local Context.\u003c\/p\u003e\u003cp\u003e.- Embedding Cultural Diversity in Prototype-based Recommender Systems.\u003c\/p\u003e\u003cp\u003e.- Is Relevance Propagated from Retriever to Generator in RAG?.\u003c\/p\u003e\u003cp\u003e.- Measuring Actual Privacy of Obfuscated Queries in Information Retrieval.\u003c\/p\u003e\u003cp\u003e.- One size doesn’t fit all: Predicting the Number of Examples for In-Context Learning.\u003c\/p\u003e\u003cp\u003e.- MURR: Model Updating with Regularized Replay for Searching a Document Stream.\u003c\/p\u003e\u003cp\u003e.- Token Pruning Optimization for Efficient Dense Retrieval with Multi-Vector Representations.\u003c\/p\u003e\u003cp\u003e.- Advancing Math Formula Search Using Diverse Structural and Symbolic Representations.\u003c\/p\u003e\u003cp\u003e.- Ragnar¨ok: A Reusable RAG Framework and Baselines for TREC 2024 Retrieval-Augmented Generation Track.\u003c\/p\u003e\u003cp\u003e.- Retrieve, Annotate, Evaluate, Repeat: Leveraging Multimodal LLMs for Large-Scale Product Retrieval Evaluation.\u003c\/p\u003e\u003cp\u003e.- Graph Representation of Tables+Text and Compact Subgraph Retrieval for QA Tasks.\u003c\/p\u003e\u003cp\u003e.- Higher Order Knowledge Graph Embeddings.\u003c\/p\u003e\u003cp\u003e.- Improving the Re-Usability of Conversational Search Test Collections.\u003c\/p\u003e\u003cp\u003e.- Repeat-bias-aware Optimization of Beyond-accuracy Metrics for Next Basket Recommendation.\u003c\/p\u003e\u003cp\u003e.- Guiding Retrieval using LLM-based Listwise Rankers.\u003c\/p\u003e\u003cp\u003e.- Lost but Not Only in the Middle: Positional Bias in Retrieval Augmented Generation.\u003c\/p\u003e\u003cp\u003e.- Biased PromptORE: Enhancing Relation Extraction in Gendered Languages and Complex Texts – The Case of Spanish Documents from the XVI Century.\u003c\/p\u003e\u003cp\u003e.- LSTM-based Selective Dense Text Retrieval Guided by Sparse Lexical Retrieval.\u003c\/p\u003e\u003cp\u003e.- Context Example Selection For LLM Generated Relevance Assessments.\u003c\/p\u003e\u003cp\u003e.- Enhancing FEVER-Style Claim Fact-Checking Against Wikipedia: A Diagnostic Taxonomy and Generative Framework.\u003c\/p\u003e\u003cp\u003e.- Evaluating Auto-complete Ranking for Diversity and Relevance.\u003c\/p\u003e\u003cp\u003e.- Semantically Proportioned nDCG for Explaining ColBERT’s Learning Process.\u003c\/p\u003e\u003cp\u003e.- Opt-in Transparent Fairness for Recommender Systems.\u003c\/p\u003e\u003cp\u003e.- Malevolence Attacks Against Pretrained Dialogue Models.\u003c\/p\u003e\u003cp\u003e.- Zero-Shot and Efficient Clarification Need Prediction in Conversational Search.\u003c\/p\u003e\u003cp\u003e.- Decoding the Hierarchy: A Hybrid Approach to Hierarchical Multi-Label Text Classification.\u003c\/p\u003e\u003cp\u003e.- A Multi-modal Recipe for Improved Multi-domain Recommendation.\u003c\/p\u003e\u003cp\u003e.- Towards Identity-Aware Cross-Modal Retrieval: a Dataset and a Baseline.\u003c\/p\u003e\u003cp\u003e.- Corpus Subsampling: Estimating the Effectiveness of Neural Retrieval Models on Large Corpora.\u003c\/p\u003e","brand":"Springer","offers":[{"title":"Default Title","offer_id":53195468210519,"sku":"9783031887079","price":123.49,"currency_code":"GBP","in_stock":true}],"url":"https:\/\/bookcurl.com\/products\/advances-in-information-retrieval-9783031887079","provider":"Book Curl","version":"1.0","type":"link"}