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Ad-Hoc Information Retrieval
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Ad-hoc information retrieval refers to the task of returning information resources related to a user query formulated in natural language.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Unify Graph Learning with Text: Unleashing LLM Potentials for Session Search
Bridge the Gap between Past and Future: Siamese Model Optimization for Context-Aware Document Ranking
Beyond Retrieval: Joint Supervision and Multimodal Document Ranking for Textbook Question Answering
A Unified Retrieval Framework with Document Ranking and EDU Filtering for Multi-document Summarization
How do Large Language Models Understand Relevance? A Mechanistic Interpretability Perspective
Beyond Reproducibility: Advancing Zero-shot LLM Reranking Efficiency with Setwise Insertion
Distillation and Refinement of Reasoning in Small Language Models for Document Re-ranking
Graph-Based Re-ranking: Emerging Techniques, Limitations, and Opportunities
On Synthetic Data Strategies for Domain-Specific Generative Retrieval
White Hat Search Engine Optimization using Large Language Models
Beyond Questions: Leveraging ColBERT for Keyphrase Search
KeyB2: Selecting Key Blocks is Also Important for Long Document Ranking with Large Language Models
Multi-Field Adaptive Retrieval
DyVo: Dynamic Vocabularies for Learned Sparse Retrieval with Entities
Query-oriented Data Augmentation for Session Search
Efficient Document Ranking with Learnable Late Interactions
Weighted KL-Divergence for Document Ranking Model Refinement
RE-RAG: Improving Open-Domain QA Performance and Interpretability with Relevance Estimator in Retrieval-Augmented Generation
On the Evaluation of Machine-Generated Reports
PromptReps: Prompting Large Language Models to Generate Dense and Sparse Representations for Zero-Shot Document Retrieval
The Surprising Effectiveness of Rankers Trained on Expanded Queries
RankMamba: Benchmarking Mamba's Document Ranking Performance in the Era of Transformers
High Recall, Small Data: The Challenges of Within-System Evaluation in a Live Legal Search System
Cognitively Biased Users Interacting with Algorithmically Biased Results in Whole-Session Search on Debated Topics
Measuring Bias in a Ranked List using Term-based Representations
Query Augmentation by Decoding Semantics from Brain Signals
Explain then Rank: Scale Calibration of Neural Rankers Using Natural Language Explanations from LLMs
Recency Ranking by Diversification of Result Set
Session-level Normalization and Click-through Data Enhancement for Session-based Evaluation
Data Augmentation for Sample Efficient and Robust Document Ranking
Evaluating Generative Ad Hoc Information Retrieval
Open-source Large Language Models are Strong Zero-shot Query Likelihood Models for Document Ranking
A Setwise Approach for Effective and Highly Efficient Zero-shot Ranking with Large Language Models
Personalized Search Via Neural Contextual Semantic Relevance Ranking
Context Aware Query Rewriting for Text Rankers using LLM
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