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Open-Domain Question Answering
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Open-domain question answering is the task of question answering on open-domain datasets such as Wikipedia.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Efficient Context Selection for Long-Context QA: No Tuning, No Iteration, Just Adaptive-$k$
ECoRAG: Evidentiality-guided Compression for Long Context RAG
GenKI: Enhancing Open-Domain Question Answering with Knowledge Integration and Controllable Generation in Large Language Models
Single LLM, Multiple Roles: A Unified Retrieval-Augmented Generation Framework Using Role-Specific Token Optimization
NOVER: Incentive Training for Language Models via Verifier-Free Reinforcement Learning
Unveiling Knowledge Utilization Mechanisms in LLM-based Retrieval-Augmented Generation
Scaling Reasoning can Improve Factuality in Large Language Models
Benchmarking LLM-based Relevance Judgment Methods
Multilingual Retrieval-Augmented Generation for Knowledge-Intensive Task
CoRAG: Collaborative Retrieval-Augmented Generation
Dense Passage Retrieval in Conversational Search
Knowledge-Aware Iterative Retrieval for Multi-Agent Systems
Optimizing open-domain question answering with graph-based retrieval augmented generation
Beyond Prompting: An Efficient Embedding Framework for Open-Domain Question Answering
WebFAQ: A Multilingual Collection of Natural Q&A Datasets for Dense Retrieval
Few-Shot Multilingual Open-Domain QA from 5 Examples
From Retrieval to Generation: Comparing Different Approaches
RA-MTR: A Retrieval Augmented Multi-Task Reader based Approach for Inspirational Quote Extraction from Long Documents
RoseRAG: Robust Retrieval-augmented Generation with Small-scale LLMs via Margin-aware Preference Optimization
Talk Structurally, Act Hierarchically: A Collaborative Framework for LLM Multi-Agent Systems
ASRank: Zero-Shot Re-Ranking with Answer Scent for Document Retrieval
Passage Segmentation of Documents for Extractive Question Answering
Parallel Key-Value Cache Fusion for Position Invariant RAG
WebWalker: Benchmarking LLMs in Web Traversal
Improving Generated and Retrieved Knowledge Combination Through Zero-shot Generation
Accelerating Manufacturing Scale-Up from Material Discovery Using Agentic Web Navigation and Retrieval-Augmented AI for Process Engineering Schematics Design
DynRank: Improving Passage Retrieval with Dynamic Zero-Shot Prompting Based on Question Classification
Context Awareness Gate For Retrieval Augmented Generation
Do LLMs Understand Ambiguity in Text? A Case Study in Open-world Question Answering
Invar-RAG: Invariant LLM-aligned Retrieval for Better Generation
JudgeRank: Leveraging Large Language Models for Reasoning-Intensive Reranking
Improve Dense Passage Retrieval with Entailment Tuning
Steering Knowledge Selection Behaviours in LLMs via SAE-Based Representation Engineering
BRIEF: Bridging Retrieval and Inference for Multi-hop Reasoning via Compression
Advancing Large Language Model Attribution through Self-Improving
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.