Research Papers Topics
Methods for turning text into dense vectors for retrieval, similarity, and search, including using LLMs as encoders.
Text Embeddings · Sentence-BERT vs SimCSE vs E5
Sentence-BERT cut 10k-sentence similarity from 65 hours to 5 seconds; SimCSE turned dropout into free training pairs for 76.25 unsupervised and 81.57 supervised STS; E5 beat BM25 zero-shot across 56 retrieval datasets.
Text Embeddings · Microsoft Research
E5 turns general-purpose text embeddings into a concrete research object, with evidence anchors, method tradeoffs, and limits for practical use.
Text Embeddings · Independent Researcher
Sentence-BERT turns sentence embeddings for semantic similarity into a concrete research object, with evidence anchors, method tradeoffs, and limits for practical use.
Text Embeddings · Princeton University
SimCSE turns contrastive sentence embedding learning into a concrete research object, with evidence anchors, method tradeoffs, and limits for practical use.
AI Agents · University of Waterloo
Direct Corpus Interaction (DCI) lets a search agent grep the raw corpus instead of calling a retriever. On BrowseComp-Plus it lifts accuracy from 69.0% to 80.0% while cutting cost 29.4%.
MulTaBench is a 40-dataset benchmark (20 image-tabular, 20 text-tabular) where each task needs both the table and the image or text. Its finding: tuning embeddings to the target beats frozen embeddings on every learner.
Text Embeddings · Renmin University of China
EmbFilter reads the LLM unembedding matrix as a lens, strips the subspace that ties text embeddings to high-frequency junk tokens, and lifts zero-shot retrieval while shrinking dimensions.