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Semantic Textual Similarity

Semantic textual similarity deals with determining how similar two pieces of texts are. This can take the form of assigning a score from 1 to 5. Related tasks are paraphrase or duplicate identification.

Image source: Learning Semantic Textual Similarity from Conversations

Papers

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Showing 301–350 of 2381 papers

TitleStatusHype
JailbreakHunter: A Visual Analytics Approach for Jailbreak Prompts Discovery from Large-Scale Human-LLM Conversational Datasets—0
How to Learn in a Noisy World? Self-Correcting the Real-World Data Noise on Machine Translation—0
Dynamic Few-Shot Learning for Knowledge Graph Question Answering—0
GenderBias-VL: Benchmarking Gender Bias in Vision Language Models via Counterfactual Probing—0
One Prompt is not Enough: Automated Construction of a Mixture-of-Expert PromptsCode1
ProtoGMM: Multi-prototype Gaussian-Mixture-based Domain Adaptation Model for Semantic Segmentation—0
Generating Is Believing: Membership Inference Attacks against Retrieval-Augmented Generation—0
A Text is Worth Several Tokens: Text Embedding from LLMs Secretly Aligns Well with The Key Tokens—0
Multimodal Physiological Signals Representation Learning via Multiscale Contrasting for Depression Recognition—0
Cross-lingual paraphrase identification—0
Semantic Enhanced Few-shot Object Detection—0
Knowledge Tagging System on Math Questions via LLMs with Flexible Demonstration Retriever—0
Saliency Attention and Semantic Similarity-Driven Adversarial Perturbation—0
Discovering Elementary Discourse Units in Textual Data Using Canonical Correlation Analysis—0
Revisiting Cosine Similarity via Normalized ICA-transformed EmbeddingsCode0
Large Language Models for Automatic Milestone Detection in Group Discussions—0
Multi-User Semantic Fusion for Semantic Communications over Degraded Broadcast Channels—0
Exploring the Correlation between Human and Machine Evaluation of Simultaneous Speech Translation—0
Pcc-tuning: Breaking the Contrastive Learning Ceiling in Semantic Textual SimilarityCode0
3D-AVS: LiDAR-based 3D Auto-Vocabulary SegmentationCode1
Self-Supervised Speech Representations are More Phonetic than SemanticCode0
Do LLMs Exhibit Human-Like Reasoning? Evaluating Theory of Mind in LLMs for Open-Ended Responses—0
Advancing Semantic Textual Similarity Modeling: A Regression Framework with Translated ReLU and Smooth K2 LossCode0
User Intent Recognition and Semantic Cache Optimization-Based Query Processing Framework using CFLIS and MGR-LAU—0
TexIm FAST: Text-to-Image Representation for Semantic Similarity Evaluation using Transformers—0
Repurposing Language Models into Embedding Models: Finding the Compute-Optimal RecipeCode0
Semantic Similarity Score for Measuring Visual Similarity at Semantic Level—0
Linguistically Conditioned Semantic Textual SimilarityCode0
Space Decomposition for Sentence EmbeddingCode0
CSS: Contrastive Semantic Similarity for Uncertainty Quantification of LLMsCode0
Guiding and Diversifying LLM-Based Story Generation via Answer Set ProgrammingCode0
Just Rewrite It Again: A Post-Processing Method for Enhanced Semantic Similarity and Privacy Preservation of Differentially Private Rewritten Text—0
Unifying Demonstration Selection and Compression for In-Context Learning—0
NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models—0
A Neurosymbolic Framework for Bias Correction in Convolutional Neural Networks—0
DEMO: A Statistical Perspective for Efficient Image-Text Matching—0
Factual Serialization Enhancement: A Key Innovation for Chest X-ray Report GenerationCode1
Words Blending Boxes. Obfuscating Queries in Information Retrieval using Differential Privacy—0
MetaReflection: Learning Instructions for Language Agents using Past Reflections—0
InsightNet: Structured Insight Mining from Customer Feedback—0
Explaining Text Similarity in Transformer ModelsCode0
Is the House Ready For Sleeptime? Generating and Evaluating Situational Queries for Embodied Question Answering—0
Adapting Dual-encoder Vision-language Models for Paraphrased Retrieval—0
Unsupervised Flow Discovery from Task-oriented Dialogues—0
NLU-STR at SemEval-2024 Task 1: Generative-based Augmentation and Encoder-based Scoring for Semantic Textual Relatedness—0
Saliency Suppressed, Semantics Surfaced: Visual Transformations in Neural Networks and the BrainCode0
Examining the robustness of LLM evaluation to the distributional assumptions of benchmarks—0
TopoLedgerBERT: Topological Learning of Ledger Description Embeddings using Siamese BERT-Networks—0
Learning Object Semantic Similarity with Self-Supervision—0
ParaFusion: A Large-Scale LLM-Driven English Paraphrase Dataset Infused with High-Quality Lexical and Syntactic Diversity—0
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All datasetsSTS BenchmarkMTEBMRPCSICKSTS13STS14STS12STS15STS16MRPC DevSentEvalSICK-R

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SMARTRoBERTaDev Pearson Correlation92.8—Unverified
2DeBERTa (large)Accuracy92.5—Unverified
3SMART-BERTDev Pearson Correlation90—Unverified
4MT-DNN-SMARTPearson Correlation0.93—Unverified
5StructBERTRoBERTa ensemblePearson Correlation0.93—Unverified
6Mnet-SimPearson Correlation0.93—Unverified
7XLNet (single model)Pearson Correlation0.93—Unverified
8ALBERTPearson Correlation0.93—Unverified
9T5-11BPearson Correlation0.93—Unverified
10RoBERTaPearson Correlation0.92—Unverified
#ModelMetricClaimedVerifiedStatus
1AnglE-UAESpearman Correlation84.54—Unverified
2ST5-XXLSpearman Correlation82.63—Unverified
3ST5-LargeSpearman Correlation81.83—Unverified
4ST5-XLSpearman Correlation81.66—Unverified
5ST5-BaseSpearman Correlation81.14—Unverified
6MPNet-multilingualSpearman Correlation80.73—Unverified
7SGPT-5.8B-nliSpearman Correlation80.53—Unverified
8MPNetSpearman Correlation80.28—Unverified
9MiniLM-L12Spearman Correlation79.8—Unverified
10SimCSE-BERT-supSpearman Correlation79.12—Unverified
#ModelMetricClaimedVerifiedStatus
1MT-DNN-SMARTAccuracy93.7—Unverified
2ALBERTAccuracy93.4—Unverified
3RoBERTa (ensemble)Accuracy92.3—Unverified
4BigBirdF191.5—Unverified
5StructBERTRoBERTa ensembleAccuracy91.5—Unverified
6FLOATER-largeAccuracy91.4—Unverified
7SMARTAccuracy91.3—Unverified
8RoBERTa-large 355M (MLP quantized vector-wise, fine-tuned)Accuracy91—Unverified
9RoBERTa-large 355M + Entailment as Few-shot LearnerF191—Unverified
10SpanBERTAccuracy90.9—Unverified
#ModelMetricClaimedVerifiedStatus
1PromCSE-RoBERTa-large (0.355B)Spearman Correlation0.82—Unverified
2PromptEOL+CSE+LLaMA-30BSpearman Correlation0.82—Unverified
3PromptEOL+CSE+OPT-13BSpearman Correlation0.82—Unverified
4SimCSE-RoBERTalargeSpearman Correlation0.82—Unverified
5PromptEOL+CSE+OPT-2.7BSpearman Correlation0.81—Unverified
6SentenceBERTSpearman Correlation0.75—Unverified
7SRoBERTa-NLI-baseSpearman Correlation0.74—Unverified
8SRoBERTa-NLI-largeSpearman Correlation0.74—Unverified
9Dino (STS/̄🦕)Spearman Correlation0.74—Unverified
10SBERT-NLI-largeSpearman Correlation0.74—Unverified
#ModelMetricClaimedVerifiedStatus
1AnglE-LLaMA-7BSpearman Correlation0.91—Unverified
2AnglE-LLaMA-7B-v2Spearman Correlation0.91—Unverified
3PromptEOL+CSE+LLaMA-30BSpearman Correlation0.9—Unverified
4PromptEOL+CSE+OPT-13BSpearman Correlation0.9—Unverified
5PromptEOL+CSE+OPT-2.7BSpearman Correlation0.9—Unverified
6PromCSE-RoBERTa-large (0.355B)Spearman Correlation0.89—Unverified
7Trans-Encoder-BERT-large-bi (unsup.)Spearman Correlation0.89—Unverified
8Trans-Encoder-BERT-large-cross (unsup.)Spearman Correlation0.88—Unverified
9Trans-Encoder-RoBERTa-large-cross (unsup.)Spearman Correlation0.88—Unverified
10SimCSE-RoBERTa-largeSpearman Correlation0.87—Unverified