ALEE generates AMR-based English minimal pairs with fine-grained semantic shifts, translates them, and evaluates embedding models on 275+ languages to expose cross-lingual gaps linked to training data and tokenization.
hub Mixed citations
Multilingual E5 Text Embeddings: A Technical Report
Mixed citation behavior. Most common role is method (43%).
71
Pith papers citing it
Method
43% of classified citations
abstract
This technical report presents the training methodology and evaluation results of the open-source multilingual E5 text embedding models, released in mid-2023. Three embedding models of different sizes (small / base / large) are provided, offering a balance between the inference efficiency and embedding quality. The training procedure adheres to the English E5 model recipe, involving contrastive pre-training on 1 billion multilingual text pairs, followed by fine-tuning on a combination of labeled datasets. Additionally, we introduce a new instruction-tuned embedding model, whose performance is on par with state-of-the-art, English-only models of similar sizes. Information regarding the model release can be found at https://github.com/microsoft/unilm/tree/master/e5 .
hub tools
citation-role summary
method 6
baseline 4
background 2
dataset 1
other 1
citation-polarity summary
representative citing papers
SEA-Embedding is a fully open text embedding pipeline for Southeast Asian languages that achieves state-of-the-art performance on the SEA-BED benchmark by analyzing data composition, training objectives, and base encoder choices.
HTEB introduces dynamic, multi-axis evaluation of text embedding robustness using LLM transformations, finding decoupled profiles across models and that scaling does not close all robustness gaps.
IdioLink introduces a benchmark dataset and evaluation showing that strong embedding models struggle to retrieve equivalent meanings across idiomatic and literal forms, relying on shallow cues instead.
Co-citation predictability for statute retrieval decays over 20 years in Ukrainian court data, dropping 33-47% in MRR with non-uniform patterns across legal domains.
DAPRO provides the first dynamic, theoretically guaranteed way to allocate interaction budgets across test cases for bounding time-to-event in multi-turn LLM evaluations, achieving tighter coverage than static conformal survival methods.
EPIC trains LLMs to treat continuous embeddings as in-context prompts, yielding state-of-the-art text embedding performance on MTEB with or without prompts at inference and lower compute.
Defines ATIR task and benchmark for mixed audio-text queries; MLLM model with token compression shows substantial gains over strong baselines.
Code-switching creates a fundamental performance bottleneck for multilingual retrievers, causing drops of up to 27% on new benchmarks CSR-L and CS-MTEB, with embedding divergence as the key cause and vocabulary expansion insufficient to fix it.
Claim2Vec is a contrastively fine-tuned multilingual encoder that improves claim clustering performance and embedding space structure on multilingual fact-check datasets.
LMEB benchmark shows that embedding models' performance on traditional retrieval does not transfer to long-horizon memory tasks, larger models do not always perform better, and LMEB measures capabilities orthogonal to MTEB.
SQuTR aggregates 37k queries from six text retrieval datasets, synthesizes speech from 200 speakers, adds 17 noise categories at varying SNR, and shows that even large retrieval models degrade sharply under extreme acoustic noise.
MultiSynt/MT supplies 4.8 trillion translated tokens in 36 languages from 100B English tokens, letting LLMs match native-data baselines with 72% fewer tokens and beat them by 15% at equal budget.
A Feedback Network model is developed showing online semantic exploration is more concentrated than physical mobility, with stable retail-business linkages and greater COVID disruption to spatial than cognitive routines, as a step toward hybrid digital twins of society.
A multi-dimensional taxonomy filtering approach recovers high-performing data from deprioritized web corpora, with filtered low-tier subsets outperforming unfiltered top-tier data on reasoning and coding benchmarks.
FIGMA proposes a multi-view contrastive architecture plus the FGMCaps dataset to retrieve music from fine-grained textual descriptions of musical attributes, reporting up to 73.3% relative gains over CLAP baselines.
StoryVideoQA provides the largest auto-generated deep video understanding dataset to date with 363K QAs across TV and movies, paired with the PlotTree agent for hierarchical plot-based reasoning that existing VideoQA models struggle to match.
RISC reformulates self-consistency answer selection as a ranking task solved by a lightweight LambdaRank model with five hand-designed features, yielding better accuracy-efficiency trade-offs than majority voting on QA benchmarks.
MIMO is a two-stage distillation-plus-contrastive framework that anchors multilingual embeddings to a monolingual English space and outperforms prior cross-lingual baselines on MLIR and multi-monolingual benchmarks.
Meta-study of MTEB rankings introduces dataset-composition and ranking-scheme robustness indicators and finds only a small subset of models stay consistently strong across tasks, languages, and evaluation variations.
Single-prompt evaluations of instruction-tuned embedding models misrepresent performance and allow any model to be ranked first by favorable prompt choice.
Embedding model performance on MTEB tasks correlates strongly with nearest-neighbor overlap and ICA magnitude differences in their embedding spaces.
Introduces the MUSA benchmark and evaluates LALMs showing that strong single-speaker performance fails to ensure robust selective attention under multilingual interference, with errors from source confusion and unresolved attribution after separation.
An extended annotation scheme with new categories and attributes plus a Gemma-300M-based multi-head classifier achieves 81.6% macro F1 on personal fact classification, outperforming few-shot LLM baselines by nearly 9 points with lower compute.
citing papers explorer
No citing papers match the current filters.