https://github.com/opengvlab/internvl
공식 코드InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks
Papers with Code 아카이브
Retrieval
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 3
- 평가표
- 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/opengvlab/mm-niah
공식 코드https://github.com/openmatch/activerag
공식 코드ActiveRAG: Autonomously Knowledge Assimilation and Accommodation through Retrieval-Augmented Agents
https://github.com/openmatch/ance-tele
공식 코드Reduce Catastrophic Forgetting of Dense Retrieval Training with Teleportation Negatives
https://github.com/openmatch/augmentation-adapted-retriever
공식 코드Augmentation-Adapted Retriever Improves Generalization of Language Models as Generic Plug-In
https://github.com/openmatch/coco-dr
공식 코드https://github.com/openmatch/fit5
공식 코드Fusion-in-T5: Unifying Document Ranking Signals for Improved Information Retrieval
https://github.com/openmatch/marvel
공식 코드MARVEL: Unlocking the Multi-Modal Capability of Dense Retrieval via Visual Module Plugin
https://github.com/openmatch/openmatch
공식 코드Structure-Aware Language Model Pretraining Improves Dense Retrieval on Structured Data
https://github.com/openmatch/rag-ddr
공식 코드RAG-DDR: Optimizing Retrieval-Augmented Generation Using Differentiable Data Rewards
https://github.com/openmatch/univl-dr
공식 코드https://github.com/openmatch/web-dro
공식 코드Enhancing Dense Retrievers' Robustness with Group-level Reweighting
https://github.com/opennlplab/scalinglaws
공식 코드https://github.com/openspg/kag
공식 코드KAG: Boosting LLMs in Professional Domains via Knowledge Augmented Generation
https://github.com/optima-cti/intellbot
공식 코드IntellBot: Retrieval Augmented LLM Chatbot for Cyber Threat Knowledge Delivery
https://github.com/optimass/Maximally_Interfered_Retrieval
공식 코드Online Continual Learning with Maximally Interfered Retrieval
https://github.com/optimusprimus/dcase2023_task6b
공식 코드Advancing Natural-Language Based Audio Retrieval with PaSST and Large Audio-Caption Data Sets
https://github.com/optimusprimus/salsa
공식 코드Estimated Audio-Caption Correspondences Improve Language-Based Audio Retrieval
https://github.com/oran-ac/lot-crs
공식 코드Alleviating the Long-Tail Problem in Conversational Recommender Systems
https://github.com/orendv/learning_to_sample
공식 코드https://github.com/oreozhao/cmr
공식 코드https://github.com/orionw/followir
공식 코드FollowIR: Evaluating and Teaching Information Retrieval Models to Follow Instructions
https://github.com/orionw/lm-expansions
공식 코드https://github.com/orionw/nevir
공식 코드https://github.com/orionw/promptriever
공식 코드Promptriever: Instruction-Trained Retrievers Can Be Prompted Like Language Models
https://github.com/orionw/rank1
공식 코드Rank1: Test-Time Compute for Reranking in Information Retrieval
https://github.com/oriram/dense-retrieval-projections
공식 코드What Are You Token About? Dense Retrieval as Distributions Over the Vocabulary
https://github.com/oriram/spider
공식 코드https://github.com/oriyor/ret-robust
공식 코드Making Retrieval-Augmented Language Models Robust to Irrelevant Context
https://github.com/oronnir/vcr
공식 코드https://github.com/osimeoni/DSM
공식 코드https://github.com/ostars/ruie
공식 코드RUIE: Retrieval-based Unified Information Extraction using Large Language Model
https://github.com/osu-nlp-group/hipporag
공식 코드HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models
https://github.com/osu-nlp-group/llm-knowledge-conflict
공식 코드https://github.com/oulu-imeds/adatriplet
공식 코드https://github.com/overwindows/semanticcodesearch
공식 코드Learning Deep Semantic Model for Code Search using CodeSearchNet Corpus
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.