https://github.com/justinhjy1004/fairuselegalbot
공식 코드Incorporating Legal Structure in Retrieval-Augmented Generation: A Case Study on Copyright Fair Use
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/jvladika/ContextRAG
공식 코드On the Influence of Context Size and Model Choice in Retrieval-Augmented Generation Systems
https://github.com/jvladika/healthfc
공식 코드HealthFC: Verifying Health Claims with Evidence-Based Medical Fact-Checking
https://github.com/jvladika/improving-health-qa
공식 코드Improving Health Question Answering with Reliable and Time-Aware Evidence Retrieval
https://github.com/jvladika/nli4ct
공식 코드https://github.com/jwallat/knowledge-acquisition
공식 코드The Effect of Masking Strategies on Knowledge Retention by Language Models
https://github.com/jwoongkim11/qa-rag
공식 코드From RAG to QA-RAG: Integrating Generative AI for Pharmaceutical Regulatory Compliance Process
https://github.com/jxhe/efficient-knnlm
공식 코드https://github.com/jxhe/sparse-text-prototype
공식 코드https://github.com/jyfang6/trace
공식 코드https://github.com/jymchen/sedr
공식 코드SeDR: Segment Representation Learning for Long Documents Dense Retrieval
https://github.com/jzbjyb/flare
공식 코드https://github.com/jzbjyb/omnitab
공식 코드OmniTab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering
https://github.com/jzbjyb/reatt
공식 코드Retrieval as Attention: End-to-end Learning of Retrieval and Reading within a Single Transformer
https://github.com/jzhoubu/hlp
공식 코드Hyperlink-induced Pre-training for Passage Retrieval in Open-domain Question Answering
https://github.com/jzhoubu/vdr
공식 코드https://github.com/kaelsunkiller/vat
공식 코드https://github.com/kaengreg/rusbeir
공식 코드Building Russian Benchmark for Evaluation of Information Retrieval Models
https://github.com/kailv69/udr
공식 코드https://github.com/kaipackhaeuser/cxr-patient-reid
공식 코드https://github.com/kaistai/instructir
공식 코드INSTRUCTIR: A Benchmark for Instruction Following of Information Retrieval Models
https://github.com/kaistai/ktrlf
공식 코드https://github.com/kaiyin97/disaster_ir
공식 코드DisastIR: A Comprehensive Information Retrieval Benchmark for Disaster Management
https://github.com/kakaobrain/noc
공식 코드Noise-aware Learning from Web-crawled Image-Text Data for Image Captioning
https://github.com/kakaoenterprise/AAAI2021_MDR
공식 코드Multi-level Distance Regularization for Deep Metric Learning
https://github.com/kamperh/recipe_semantic_flickraudio
공식 코드Semantic speech retrieval with a visually grounded model of untranscribed speech
https://github.com/kamperh/semantic_flickraudio
공식 코드Semantic speech retrieval with a visually grounded model of untranscribed speech
https://github.com/kamwoh/orthohash
공식 코드One Loss for All: Deep Hashing with a Single Cosine Similarity based Learning Objective
https://github.com/kamwoh/sdc
공식 코드Unsupervised Hashing with Similarity Distribution Calibration
https://github.com/kanak8278/dialogkperm
공식 코드https://github.com/kangmintong/c-rag
공식 코드C-RAG: Certified Generation Risks for Retrieval-Augmented Language Models
https://github.com/kapilw25/Evaluation_generated_images
공식 코드Multimodal Benchmarking and Recommendation of Text-to-Image Generation Models
https://github.com/karimmibrahim/situational_session_generator
공식 코드Exploiting Device and Audio Data to Tag Music with User-Aware Listening Contexts
https://github.com/kathyhaem/combining-static-contextual
공식 코드https://github.com/katzurik/neretrieve
공식 코드NERetrieve: Dataset for Next Generation Named Entity Recognition and Retrieval
https://github.com/kaustubhdhole/natural-dont-know
공식 코드Saying No is An Art: Contextualized Fallback Responses for Unanswerable Dialogue Queries
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.