https://github.com/thedatastation/pneuma
공식 코드Pneuma: Leveraging LLMs for Tabular Data Representation and Retrieval in an End-to-End System
Papers with Code 아카이브
Retrieval
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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- 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/thejat/data-driven-assortments
공식 코드Optimizing Revenue while showing Relevant Assortments at Scale
https://github.com/theworldofagents/agentic-reasoning
공식 코드Agentic Reasoning: Reasoning LLMs with Tools for the Deep Research
https://github.com/thibaultdouzon/long-range-document-transformer
공식 코드Long-Range Transformer Architectures for Document Understanding
https://github.com/thijmennijdam/NevIR-reproducibility
공식 코드https://github.com/thisishwang/exit
공식 코드EXIT: Context-Aware Extractive Compression for Enhancing Retrieval-Augmented Generation
https://github.com/thomas-yin/semeval-2023-task1
공식 코드https://github.com/thomas0809/textreact
공식 코드https://github.com/thongnt99/dyvo
공식 코드DyVo: Dynamic Vocabularies for Learned Sparse Retrieval with Entities
https://github.com/thongnt99/learned-sparse-retrieval
공식 코드https://github.com/thongnt99/lsr-long
공식 코드https://github.com/thongnt99/lsr-multimodal
공식 코드Multimodal Learned Sparse Retrieval with Probabilistic Expansion Control
https://github.com/thu-bpm/chef
공식 코드CHEF: A Pilot Chinese Dataset for Evidence-Based Fact-Checking
https://github.com/thu-coai/moralstory
공식 코드https://github.com/thu-keg/probtree
공식 코드Probabilistic Tree-of-thought Reasoning for Answering Knowledge-intensive Complex Questions
https://github.com/thu-keg/r-eval
공식 코드https://github.com/thu-keg/rearag
공식 코드https://github.com/thu-keg/seakr
공식 코드SeaKR: Self-aware Knowledge Retrieval for Adaptive Retrieval Augmented Generation
https://github.com/thu-keg/vta
공식 코드https://github.com/thu-spmi/jsa-krtod
공식 코드Knowledge-Retrieval Task-Oriented Dialog Systems with Semi-Supervision
https://github.com/thudm/p-tuning-v2
공식 코드Parameter-Efficient Prompt Tuning Makes Generalized and Calibrated Neural Text Retrievers
https://github.com/thudm/webglm
공식 코드WebGLM: Towards An Efficient Web-Enhanced Question Answering System with Human Preferences
https://github.com/thudzj/NEigenmaps
공식 코드Neural Eigenfunctions Are Structured Representation Learners
https://github.com/thuir/t2ranking
공식 코드T2Ranking: A large-scale Chinese Benchmark for Passage Ranking
https://github.com/thukelab/ultrawiki
공식 코드UltraWiki: Ultra-fine-grained Entity Set Expansion with Negative Seed Entities
https://github.com/thulawtech/muser
공식 코드https://github.com/thuml/DPH
공식 코드https://github.com/thuml/HashNet
공식 코드https://github.com/thumlp/nli4ct
공식 코드https://github.com/thunlp-mt/pgra
공식 코드Prompt-Guided Retrieval Augmentation for Non-Knowledge-Intensive Tasks
https://github.com/thunlp/ConvDR
공식 코드https://github.com/thunlp/ConversationQueryRewriter
공식 코드https://github.com/thunlp/DANCE
공식 코드https://github.com/thunlp/EntityDuetNeuralRanking
공식 코드https://github.com/thunlp/LegalPLMs
공식 코드Lawformer: A Pre-trained Language Model for Chinese Legal Long Documents
https://github.com/thunlp/MetaAdaptRank
공식 코드Few-Shot Text Ranking with Meta Adapted Synthetic Weak Supervision
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.