https://github.com/Alibaba-NLP/HLATR
공식 코드HLATR: Enhance Multi-stage Text Retrieval with Hybrid List Aware Transformer Reranking
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Passage re-ranking is the task of scoring and re-ranking a collection of retrieved documents based on an input query.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
HLATR: Enhance Multi-stage Text Retrieval with Hybrid List Aware Transformer Reranking
A Modern Perspective on Query Likelihood with Deep Generative Retrieval Models
Improving Conversational Passage Re-ranking with View Ensemble
Exploring the Effectiveness of Multi-stage Fine-tuning for Cross-encoder Re-rankers
Fast Passage Re-ranking with Contextualized Exact Term Matching and Efficient Passage Expansion
Dealing with Typos for BERT-based Passage Retrieval and Ranking
Exploiting Sentence-Level Representations for Passage Ranking
Few-shot Reranking for Multi-hop QA via Language Model Prompting
Few-shot Reranking for Multi-hop QA via Language Model Prompting
RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-ranking
Mitigating the Position Bias of Transformer Models in Passage Re-Ranking
T2Ranking: A large-scale Chinese Benchmark for Passage Ranking
Rank-DistiLLM: Closing the Effectiveness Gap Between Cross-Encoders and LLMs for Passage Re-Ranking
Dealing with Typos for BERT-based Passage Retrieval and Ranking
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.