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