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Sentence-BERT Siamese Sentence Embedding Framework (Reimers & Gurevych, 2019)
Sentence-BERT (SBERT) is a sentence-embedding framework introduced by Reimers and Gurevych (EMNLP 2019). It fine-tunes a pretrained Transformer (e.g., BERT or a distilled variant) inside a siamese / triplet network: two copies of the encoder share weights, each encodes one sentence into a fixed-length pooled vector (typically mean-pooled token embeddings), and the network is trained with classification, regression, or triplet objectives so that semantically similar sentences are close under cosine similarity. The framework is released as the open-source sentence-transformers library and is the standard recipe for turning Transformer encoders into reusable, single-vector sentence embeddings suitable for semantic search, clustering, and retrieval.
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Reference: What Should I Learn First: Introducing LectureBank for NLP Education and Prerequisite Chain Learning
Reference: R-VGAE: Relational-variational Graph Autoencoder for Unsupervised Prerequisite Chain Learning
Reference: ojs.aaai.org
Reference: Prerequisite Relation Learning for Concepts in MOOCs
Reference: Course Prerequisite Relation (MOOC prerequisite dataset release page)
Reference: MOOCCube: A Large-scale Data Repository for NLP Applications in MOOCs
Reference: QASC: A Dataset for Question Answering via Sentence Composition
Reference: QASC: A Dataset for Question Answering via Sentence Composition (arXiv preprint)
Reference: ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction
Reference: ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT
Reference: arxiv.org
Reference: arxiv.org
Reference: Introduction to Information Retrieval
Reference: Evaluation measures (information retrieval)
Reference: REPLUG: Retrieval-Augmented Black-Box Language Models
Reference: arxiv.org
Reference: HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering
Reference: HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering (arXiv preprint)
Reference: HotpotQA Official Dataset and Leaderboard
Reference: Dense Passage Retrieval for Open-Domain Question Answering
Reference: Dense Passage Retrieval for Open-Domain Question Answering (arXiv preprint)
LectureBank Dataset
MOOC-CS Prerequisite Benchmark
QASC Question Answering Benchmark
Late-Interaction Neural Retrieval
Recall@k Retrieval Metric
RePlug Retrieval-Augmented Black-Box Language Model
HotpotQA Multi-Hop QA Benchmark
Single-Vector Dense Passage Retrieval
Reference: Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
Reference: How Significant Are the Real Performance Gains? An Unbiased Evaluation Framework for GraphRAG
Reference: arxiv.org
Unbiased GraphRAG Evaluation Framework (Zeng et al., 2025)
Reference: RAG vs. GraphRAG: A Systematic Evaluation and Key Insights
Reference: arxiv.org
RAG vs Graph-RAG Controlled Comparison (Han et al., 2025)
Reference: Controlled Retrieval-augmented Context Evaluation for Long-form RAG
Reference: Controlled Retrieval-augmented Context Evaluation for Long-form RAG (ACL Anthology)
CRUX Controlled RAG Context Evaluation (Ju et al., 2025)
Reference: Anytime Heuristic Search
Reference: The Anatomy of a Large-Scale Hypertextual Web Search Engine