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Retrieval
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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/oxai/visogender
공식 코드VisoGender: A dataset for benchmarking gender bias in image-text pronoun resolution
https://github.com/oyar99/ir-ag-reg
공식 코드A Hybrid Approach to Information Retrieval and Answer Generation for Regulatory Texts
https://github.com/p-lambda/jukemir
공식 코드Codified audio language modeling learns useful representations for music information retrieval
https://github.com/padas-lab-de/PersonaRAG
공식 코드PersonaRAG: Enhancing Retrieval-Augmented Generation Systems with User-Centric Agents
https://github.com/paddlepaddle/rocketqa
공식 코드https://github.com/palchenli/TVD
공식 코드PTVD: A Large-Scale Plot-Oriented Multimodal Dataset Based on Television Dramas
https://github.com/palin2018/browsecomp-zh
공식 코드BrowseComp-ZH: Benchmarking Web Browsing Ability of Large Language Models in Chinese
https://github.com/palmagro/gg2vec
공식 코드https://github.com/panchengzhao/lake-red
공식 코드LAKE-RED: Camouflaged Images Generation by Latent Background Knowledge Retrieval-Augmented Diffusion
https://github.com/panruotong/cag
공식 코드Not All Contexts Are Equal: Teaching LLMs Credibility-aware Generation
https://github.com/panuthept/dst-denseretrieval
공식 코드https://github.com/papermsucode/mdmmt
공식 코드MDMMT: Multidomain Multimodal Transformer for Video Retrieval
https://github.com/paramita08/wikidata_bias_websci_2023
공식 코드Diversity matters: Robustness of bias measurements in Wikidata
https://github.com/paranioar/gssf
공식 코드GSSF: Generalized Structural Sparse Function for Deep Cross-modal Metric Learning
https://github.com/parry-parry/adversarialcontext
공식 코드Exploiting Positional Bias for Query-Agnostic Generative Content in Search
https://github.com/parry-parry/ecir24-adversarial-evaluation
공식 코드Analyzing Adversarial Attacks on Sequence-to-Sequence Relevance Models
https://github.com/parry-parry/mechir
공식 코드MechIR: A Mechanistic Interpretability Framework for Information Retrieval
https://github.com/parshwa1999/PeR-ViS
공식 코드PeR-ViS: Person Retrieval in Video Surveillance using Semantic Description
https://github.com/parth166/m3g-clinicaldermatology
공식 코드https://github.com/parthsarthi03/RAPTOR
공식 코드RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval
https://github.com/particlemedia/ragtruth
공식 코드RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models
https://github.com/pascaltribel/pyawd
공식 코드PyAWD: A Library for Generating Large Synthetic Datasets of Acoustic Wave Propagation with Devito
https://github.com/pasquini-dario/llm_neuralexec
공식 코드Neural Exec: Learning (and Learning from) Execution Triggers for Prompt Injection Attacks
https://github.com/passing2961/dialogcc
공식 코드DialogCC: An Automated Pipeline for Creating High-Quality Multi-Modal Dialogue Dataset
https://github.com/pat-jj/KARE
공식 코드Reasoning-Enhanced Healthcare Predictions with Knowledge Graph Community Retrieval
https://github.com/pat-jj/Retrieval-And-Structure
공식 코드RAS: Retrieval-And-Structuring for Knowledge-Intensive LLM Generation
https://github.com/pat-jj/deepretrieval
공식 코드https://github.com/pat-jj/ras
공식 코드RAS: Retrieval-And-Structuring for Knowledge-Intensive LLM Generation
https://github.com/pat-jj/s3
공식 코드s3: You Don't Need That Much Data to Train a Search Agent via RL
https://github.com/patrickjohncyh/fashion-clip
공식 코드Contrastive language and vision learning of general fashion concepts
https://github.com/patrickkollman/deep-learning-final-project
공식 코드Automated Audio Captioning and Language-Based Audio Retrieval
https://github.com/patronus-ai/financebench
공식 코드FinanceBench: A New Benchmark for Financial Question Answering
https://github.com/paul-sheridan/hgt-tfidf
공식 코드The hypergeometric test performs comparably to TF-IDF on standard text analysis tasks
https://github.com/paulalbert31/sncf
공식 코드https://github.com/paulaonta/blind-valm
공식 코드Improving the Efficiency of Visually Augmented Language Models
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