https://github.com/deno-v/heterfc
공식 코드Heterogeneous Graph Reasoning for Fact Checking over Texts and Tables
Papers with Code 아카이브
Retrieval
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 11
- 보존된 평가 기록
- 19
A methodology that involves selecting relevant data or examples from a large dataset to support tasks like prediction, learning, or inference. It enhances models by providing context or additional information, often used in systems like retrieval-augmented generation or in-context learning.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/deokhk/dotter
공식 코드Denoising Table-Text Retrieval for Open-Domain Question Answering
https://github.com/dependentsign/EX-FEVER
공식 코드EX-FEVER: A Dataset for Multi-hop Explainable Fact Verification
https://github.com/deriq-qian-dong/iii-retriever
공식 코드https://github.com/deropty/PiT
공식 코드Multi-direction and Multi-scale Pyramid in Transformer for Video-based Pedestrian Retrieval
https://github.com/descriptinc/lyrebird-wav2clip
공식 코드https://github.com/det1999/shapemaker
공식 코드ShapeMatcher: Self-Supervised Joint Shape Canonicalization, Segmentation, Retrieval and Deformation
https://github.com/detectivemb/task-arithmetic-for-zs-ir
공식 코드Investigating Task Arithmetic for Zero-Shot Information Retrieval
https://github.com/dfdazac/blp
공식 코드Inductive Entity Representations from Text via Link Prediction
https://github.com/dheeraj7596/SCDV
공식 코드SCDV : Sparse Composite Document Vectors using soft clustering over distributional representations
https://github.com/di-viner/llm-robustness-to-irrelevant-information
공식 코드How Easily do Irrelevant Inputs Skew the Responses of Large Language Models?
https://github.com/di4ir/dual-score
공식 코드https://github.com/diazf/indri
공식 코드https://github.com/diazf/pref_eval
공식 코드https://github.com/dice-group/nero
공식 코드Learning Permutation-Invariant Embeddings for Description Logic Concepts
https://github.com/digitalharborfoundation/rag-for-math-qa
공식 코드https://github.com/digitalphonetics/conversational-tree-search
공식 코드https://github.com/diku-irlab/A66
공식 코드Evaluation Measures for Relevance and Credibility in Ranked Lists
https://github.com/dingkeyan93/DISTS
공식 코드Image Quality Assessment: Unifying Structure and Texture Similarity
https://github.com/dinhviettoanle/survey-music-nlp
공식 코드https://github.com/dipteshkanojia/challengeCognateFF
공식 코드Harnessing Cross-lingual Features to Improve Cognate Detection for Low-resource Languages
https://github.com/dipteshkanojia/pr-aaai22-sdu-st1-ae
공식 코드An Ensemble Approach to Acronym Extraction using Transformers
https://github.com/dirkiedai/sk-mt
공식 코드https://github.com/disi-unibo-nlp/rag-when-how-chunk
공식 코드Reconstructing Context: Evaluating Advanced Chunking Strategies for Retrieval-Augmented Generation
https://github.com/divanoletto/3d_step_classification
공식 코드CAD 3D Model classification by Graph Neural Networks: A new approach based on STEP format
https://github.com/divraz/TaskSegmentation
공식 코드Language-Conditioned Change-point Detection to Identify Sub-Tasks in Robotics Domains
https://github.com/djbradyAtOpticalSciencesArizona/photonLimitedPhaseRetrieval
공식 코드https://github.com/djbradyatopticalsciencesarizona/scatterptychography
공식 코드https://github.com/dki-lab/few-shot-bioie
공식 코드Thinking about GPT-3 In-Context Learning for Biomedical IE? Think Again
https://github.com/dlawjddn803/info
공식 코드https://github.com/dmg-illc/trade
공식 코드Don't Buy it! Reassessing the Ad Understanding Abilities of Contrastive Multimodal Models
https://github.com/dmis-lab/compact
공식 코드CompAct: Compressing Retrieved Documents Actively for Question Answering
https://github.com/dmis-lab/covidAsk
공식 코드https://github.com/dmis-lab/rag2
공식 코드Rationale-Guided Retrieval Augmented Generation for Medical Question Answering
https://github.com/dmis-lab/self-biorag
공식 코드https://github.com/dmis-lab/tour
공식 코드Optimizing Test-Time Query Representations for Dense Retrieval
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.