Papers Sentence Embeddings
“Sentence Embeddings” 태그가 달린 논문 615편 · 필터 해제
From Neurons to Semantics: Evaluating Cross-Linguistic Alignment Capabilities of Large Language Models via Neurons Alignment
Large language models (LLMs) have demonstrated remarkable multilingual capabilities, however, how to evaluate cross-lingual alignment remains underexplored. Existing alignment benchmarks primarily focus on sentence embed…
SentenceSentence EmbeddingsSemCSE: Semantic Contrastive Sentence Embeddings Using LLM-Generated Summaries For Scientific Abstracts
We introduce SemCSE, an unsupervised method for learning semantic embeddings of scientific texts. Building on recent advances in contrastive learning for text embeddings, our approach leverages LLM-generated summaries of…
Contrastive LearningSemantic SimilaritySemantic Textual SimilaritySentence+1Intrinsic vs. Extrinsic Evaluation of Czech Sentence Embeddings: Semantic Relevance Doesn't Help with MT Evaluation
In this paper, we compare Czech-specific and multilingual sentence embedding models through intrinsic and extrinsic evaluation paradigms. For intrinsic evaluation, we employ Costra, a complex sentence transformation data…
Machine TranslationSemantic SimilaritySemantic Textual SimilaritySentence+5Do We Talk to Robots Like Therapists, and Do They Respond Accordingly? Language Alignment in AI Emotional Support
As conversational agents increasingly engage in emotionally supportive dialogue, it is important to understand how closely their interactions resemble those in traditional therapy settings. This study investigates whethe…
Large Language ModelSentence EmbeddingsMechanistic Decomposition of Sentence Representations
Sentence embeddings are central to modern NLP and AI systems, yet little is known about their internal structure. While we can compare these embeddings using measures such as cosine similarity, the contributing features …
Dictionary LearningSentenceSentence EmbeddingSentence-Embedding+1Rethinking the Understanding Ability across LLMs through Mutual Information
Recent advances in large language models (LLMs) have revolutionized natural language processing, yet evaluating their intrinsic linguistic understanding remains challenging. Moving beyond specialized evaluation tasks, we…
SentenceSentence EmbeddingSentence-EmbeddingSentence EmbeddingsLLMs Are Not Scorers: Rethinking MT Evaluation with Generation-Based Methods
Recent studies have applied large language models (LLMs) to machine translation quality estimation (MTQE) by prompting models to assign numeric scores. Nonetheless, these direct scoring methods tend to show low segment-l…
DecoderMachine TranslationSemantic SimilaritySemantic Textual Similarity+2Contrastive Prompting Enhances Sentence Embeddings in LLMs through Inference-Time Steering
Extracting sentence embeddings from large language models (LLMs) is a practical direction, as it requires neither additional data nor fine-tuning. Previous studies usually focus on prompt engineering to guide LLMs to enc…
Prompt EngineeringSemantic Textual SimilaritySentenceSentence Embedding+3NBF at SemEval-2025 Task 5: Light-Burst Attention Enhanced System for Multilingual Subject Recommendation
We present our system submission for SemEval 2025 Task 5, which focuses on cross-lingual subject classification in the English and German academic domains. Our approach leverages bilingual data during training, employing…
GPURetrievalSentenceSentence EmbeddingsSentence Embeddings as an intermediate target in end-to-end summarisation
Current neural network-based methods to the problem of document summarisation struggle when applied to datasets containing large inputs. In this paper we propose a new approach to the challenge of content-selection when …
SentenceSentence EmbeddingsJTCSE: Joint Tensor-Modulus Constraints and Cross-Attention for Unsupervised Contrastive Learning of Sentence Embeddings
Unsupervised contrastive learning has become a hot research topic in natural language processing. Existing works usually aim at constraining the orientation distribution of the representations of positive and negative sa…
Contrastive LearningSentenceSentence Embeddingstext similarityScalable Unit Harmonization in Medical Informatics Using Bi-directional Transformers and Bayesian-Optimized BM25 and Sentence Embedding Retrieval
Objective: To develop and evaluate a scalable methodology for harmonizing inconsistent units in large-scale clinical datasets, addressing a key barrier to data interoperability. Materials and Methods: We designed a novel…
Bayesian OptimizationInformation RetrievalRe-RankingRetrieval+4Information Leakage of Sentence Embeddings via Generative Embedding Inversion Attacks
Text data are often encoded as dense vectors, known as embeddings, which capture semantic, syntactic, contextual, and domain-specific information. These embeddings, widely adopted in various applications, inherently cont…
SentenceSentence EmbeddingSentence-EmbeddingSentence EmbeddingsLeveraging Language Models for Automated Patient Record Linkage
Objective: Healthcare data fragmentation presents a major challenge for linking patient data, necessitating robust record linkage to integrate patient records from diverse sources. This study investigates the feasibility…
BlockingData IntegrationSentence EmbeddingsA Framework for Lightweight Responsible Prompting Recommendation
Computer Science and Design practitioners have been researching and proposing alternatives for a dearth of recommendations, standards, or best practices in user interfaces for decades. Now, with the advent of generative …
SentenceSentence EmbeddingsM2D2: Exploring General-purpose Audio-Language Representations Beyond CLAP
Contrastive language-audio pre-training (CLAP) has addressed audio-language tasks such as audio-text retrieval by aligning audio and text in a common feature space. While CLAP addresses general audio-language tasks, its …
Audio captioningAudio ClassificationAudio TaggingAudio to Text Retrieval+13A Retrieval-Based Approach to Medical Procedure Matching in Romanian
Accurately mapping medical procedure names from healthcare providers to standardized terminology used by insurance companies is a crucial yet complex task. Inconsistencies in naming conventions lead to missclasified proc…
Medical ProcedureRetrievalSentenceSentence EmbeddingsSCI-IDEA: Context-Aware Scientific Ideation Using Token and Sentence Embeddings
Every scientific discovery starts with an idea inspired by prior work, interdisciplinary concepts, and emerging challenges. Recent advancements in large language models (LLMs) trained on scientific corpora have driven in…
scientific discoverySentenceSentence EmbeddingsCASE -- Condition-Aware Sentence Embeddings for Conditional Semantic Textual Similarity Measurement
The meaning conveyed by a sentence often depends on the context in which it appears. Despite the progress of sentence embedding methods, it remains unclear how to best modify a sentence embedding conditioned on its conte…
Dimensionality ReductionLanguage ModelingLanguage ModellingLarge Language Model+7Text Compression for Efficient Language Generation
We challenge the prevailing assumption that LLMs must rely fully on sub-word tokens for high-quality text generation. To this end, we propose the "Generative Pretrained Thoughtformer" (GPTHF), a hierarchical transformer …
Language ModelingLanguage ModellingSentenceSentence Embeddings+2