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Semantic Similarity
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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
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
SemCSE: Semantic Contrastive Sentence Embeddings Using LLM-Generated Summaries For Scientific Abstracts
SARA: Selective and Adaptive Retrieval-augmented Generation with Context Compression
FA: Forced Prompt Learning of Vision-Language Models for Out-of-Distribution Detection
LineRetriever: Planning-Aware Observation Reduction for Web Agents
DALR: Dual-level Alignment Learning for Multimodal Sentence Representation Learning
Enhancing Automatic Term Extraction with Large Language Models via Syntactic Retrieval
Intrinsic vs. Extrinsic Evaluation of Czech Sentence Embeddings: Semantic Relevance Doesn't Help with MT Evaluation
Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models
PrivacyXray: Detecting Privacy Breaches in LLMs through Semantic Consistency and Probability Certainty
Semantic similarity estimation for domain specific data using BERT and other techniques
InsertRank: LLMs can reason over BM25 scores to Improve Listwise Reranking
GrFormer: A Novel Transformer on Grassmann Manifold for Infrared and Visible Image Fusion
ImpliRet: Benchmarking the Implicit Fact Retrieval Challenge
Similarity = Value? Consultation Value Assessment and Alignment for Personalized Search
FindMeIfYouCan: Bringing Open Set metrics to $\textit{near} $, $ \textit{far} $ and $\textit{farther}$ Out-of-Distribution Object Detection
Inv-Entropy: A Fully Probabilistic Framework for Uncertainty Quantification in Language Models
Hierarchical Scoring with 3D Gaussian Splatting for Instance Image-Goal Navigation
Trend-Aware Fashion Recommendation with Visual Segmentation and Semantic Similarity
Statistical Hypothesis Testing for Auditing Robustness in Language Models
Conservative Bias in Large Language Models: Measuring Relation Predictions
KNN-Defense: Defense against 3D Adversarial Point Clouds using Nearest-Neighbor Search
Denoising Programming Knowledge Tracing with a Code Graph-based Tuning Adaptor
Plugging Schema Graph into Multi-Table QA: A Human-Guided Framework for Reducing LLM Reliance
IRT-Router: Effective and Interpretable Multi-LLM Routing via Item Response Theory
MCP-Zero: Active Tool Discovery for Autonomous LLM Agents
Category-aware EEG image generation based on wavelet transform and contrast semantic loss
VUDG: A Dataset for Video Understanding Domain Generalization
GATE: General Arabic Text Embedding for Enhanced Semantic Textual Similarity with Matryoshka Representation Learning and Hybrid Loss Training
PRISM: A Framework for Producing Interpretable Political Bias Embeddings with Political-Aware Cross-Encoder
Label-Guided In-Context Learning for Named Entity Recognition
Improving Brain-to-Image Reconstruction via Fine-Grained Text Bridging
Document Valuation in LLM Summaries: A Cluster Shapley Approach
LLMs as Better Recommenders with Natural Language Collaborative Signals: A Self-Assessing Retrieval Approach
The Avengers: A Simple Recipe for Uniting Smaller Language Models to Challenge Proprietary Giants
Position: Mechanistic Interpretability Should Prioritize Feature Consistency in SAEs
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.