Papers with Code 아카이브
Information Retrieval
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
- 33
- 보존된 평가 기록
- 17
Information retrieval is the task of ranking a list of documents or search results in response to a query
<span style="color:grey; opacity: 0.6">( Image credit: sudhanshumittal )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/lezhang7/rearank
공식 코드REARANK: Reasoning Re-ranking Agent via Reinforcement Learning
https://github.com/lezhang7/retrieval_mugi
공식 코드Exploring the Best Practices of Query Expansion with Large Language Models
https://github.com/li-z-q/url
공식 코드URL: Universal Referential Knowledge Linking via Task-instructed Representation Compression
https://github.com/lihaitao18375278/THUIR-COLIEE2023
공식 코드THUIR@COLIEE 2023: More Parameters and Legal Knowledge for Legal Case Entailment
https://github.com/linhanz/mderank
공식 코드MDERank: A Masked Document Embedding Rank Approach for Unsupervised Keyphrase Extraction
https://github.com/linweizhedragon/retrieval-augmented-visual-question-answering
공식 코드Retrieval Augmented Visual Question Answering with Outside Knowledge
https://github.com/linyueqian/spiral_dataset
공식 코드SpeechPrune: Context-aware Token Pruning for Speech Information Retrieval
https://github.com/lionisakis/reproducibilitiy-lsr-long
공식 코드On the Reproducibility of Learned Sparse Retrieval Adaptations for Long Documents
https://github.com/littleredxh/DREML
공식 코드https://github.com/littlewine/zeco2
공식 코드https://github.com/liubin06/dpl
공식 코드Debiased Pairwise Learning from Positive-Unlabeled Implicit Feedback
https://github.com/liuliuyuan6/Exp4Fuse
공식 코드https://github.com/liuliuyuan6/GOLFer
공식 코드https://github.com/liuqi6777/llm-relevance
공식 코드How do Large Language Models Understand Relevance? A Mechanistic Interpretability Perspective
https://github.com/lixsh6/tencent_wsdm_cup2023
공식 코드Pretraining De-Biased Language Model with Large-scale Click Logs for Document Ranking
https://github.com/ll0ruc/AutoMIR
공식 코드AutoMIR: Effective Zero-Shot Medical Information Retrieval without Relevance Labels
https://github.com/llm-lab-org/CLASP
공식 코드CLASP: Contrastive Language-Speech Pretraining for Multilingual Multimodal Information Retrieval
https://github.com/llm-vlm-gsl/discuss-rag
공식 코드Talk Before You Retrieve: Agent-Led Discussions for Better RAG in Medical QA
https://github.com/llm4eval/LLMJudge
공식 코드https://github.com/lmh0921/keyb
공식 코드The Power of Selecting Key Blocks with Local Pre-ranking for Long Document Information Retrieval
https://github.com/lporcaro/music-diversity-analysis
공식 코드https://github.com/lslland/t-vaccine
공식 코드https://github.com/luckyyysta/awesome-llm-hallucination
공식 코드https://github.com/lukewys/chamber-ensemble-generator
공식 코드The Chamber Ensemble Generator: Limitless High-Quality MIR Data via Generative Modeling
https://github.com/luyug/COIL
공식 코드COIL: Revisit Exact Lexical Match in Information Retrieval with Contextualized Inverted List
https://github.com/luyug/Condenser
공식 코드Unsupervised Corpus Aware Language Model Pre-training for Dense Passage Retrieval
https://github.com/ma787639046/bowdpr
공식 코드Drop your Decoder: Pre-training with Bag-of-Word Prediction for Dense Passage Retrieval
https://github.com/maastrichtlawtech/bsard
공식 코드https://github.com/maastrichtlawtech/gdsr
공식 코드Finding the Law: Enhancing Statutory Article Retrieval via Graph Neural Networks
https://github.com/madelonhulsebos/gittables
공식 코드https://github.com/magkai/CROWN
공식 코드Conversational Question Answering over Passages by Leveraging Word Proximity Networks
https://github.com/mainlp/codeswitchclir
공식 코드Boosting Zero-shot Cross-lingual Retrieval by Training on Artificially Code-Switched Data
https://github.com/mainlp/wikidir
공식 코드Cross-Dialect Information Retrieval: Information Access in Low-Resource and High-Variance Languages
https://github.com/mainsample1234/as-gir
공식 코드ASGIR: Audio Spectrogram Transformer Guided Classification And Information Retrieval For Birds
https://github.com/mangopy/searchlm
공식 코드Iterative Self-Incentivization Empowers Large Language Models as Agentic Searchers
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.