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2개 벤치마크 · 논문 216편 · 이 태스크의 논문 보기 →
Benchmarks
Most implemented
Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering
RealFormer: Transformer Likes Residual Attention
Relevance-guided Supervision for OpenQA with ColBERT
ST-MoE: Designing Stable and Transferable Sparse Expert Models
TempoQR: Temporal Question Reasoning over Knowledge Graphs
Papers
When Should LLMs Abstain? Chain-of-Self-Questioning for Selective Risk Control
Large language models can produce fluent answers when their factual support is weak. This paper introduces Chain-of-Self-Questioning (CoSQ), a prompt-only framework that makes answer commitment conditional on an explicit…
Natural QuestionsSame Agent, Different Answers: A Repeat-Aware Audit of Corpus-Induced Answer Churn in Retrieval-Augmented QA
A retrieval-augmented QA system can return different answers after an index expansion even when its requested model identifier, prompt, retrieval policy, evidence depth, rendering, and exposed generation controls are hel…
Natural QuestionsFrom Retrieved Context to Runtime Control: Adaptive Compression for Edge-based RAG
Retrieval-augmented generation (RAG) improves language-model responses by grounding generation in external passages, which comes with overhead: retrieved context lengthens the prompt, increasing prefill work, KV-cache fo…
Natural QuestionsStatic Pruning Across Sparse Retrieval Regimes: What Transfers, What Breaks, and What Still Helps
Static pruning is widely used to accelerate sparse neural retrieval, yet existing studies each validate their conclusions within a single custom pipeline, leaving it unclear which findings transfer to modern engines with…
Natural QuestionsTabRank: Chain-of-Thought Distillation for Table Re-Rankers
The ability to retrieve relevant tables for answering questions is a key task for structured information retrieval. Multi-stage retrieval systems rely heavily on rerankers to refine candidate lists produced by efficient …
Information RetrievalNatural QuestionsPassage RetrievalTable RetrievalRAGuard: A Layered Defense Framework for Retrieval-Augmented Generation Systems Against Data Poisoning
Retrieval-Augmented Generation (RAG) systems ground large language models (LLMs) in external corpora, but this reliance exposes them to corpus poisoning: maliciously injected passages that manipulate retrieved evidence. …
Natural Questions