SOTAVerified

Semantic Similarity

The main objective Semantic Similarity is to measure the distance between the semantic meanings of a pair of words, phrases, sentences, or documents. For example, the word “car” is more similar to “bus” than it is to “cat”. The two main approaches to measuring Semantic Similarity are knowledge-based approaches and corpus-based, distributional methods.

Source: Visual and Semantic Knowledge Transfer for Large Scale Semi-supervised Object Detection

Papers

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Showing 1–10 of 1564 papers

TitleStatusHype
SemCSE: Semantic Contrastive Sentence Embeddings Using LLM-Generated Summaries For Scientific Abstracts—0
SARA: Selective and Adaptive Retrieval-augmented Generation with Context Compression—0
FA: Forced Prompt Learning of Vision-Language Models for Out-of-Distribution DetectionCode0
LineRetriever: Planning-Aware Observation Reduction for Web Agents—0
DALR: Dual-level Alignment Learning for Multimodal Sentence Representation Learning—0
Enhancing Automatic Term Extraction with Large Language Models via Syntactic Retrieval—0
Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models—0
Intrinsic vs. Extrinsic Evaluation of Czech Sentence Embeddings: Semantic Relevance Doesn't Help with MT Evaluation—0
PrivacyXray: Detecting Privacy Breaches in LLMs through Semantic Consistency and Probability Certainty—0
Semantic similarity estimation for domain specific data using BERT and other techniques—0
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All datasetsAnnotated corpus for semantic similarity of clinical trial outcomes (expanded corpus)Annotated corpus for semantic similarity of clinical trial outcomes (original corpus)SICKBIOSSESCHIP-STSClinicalSTSMedSTS

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MacBERT-largeMacro F185.6—Unverified