Papers Paraphrase Generation
“Paraphrase Generation” 태그가 달린 논문 213편 · 필터 해제
Risk-Constrained Freshness-Aware Semantic Caching for Open-Web Retrieval-Augmented LLMs
Semantic caching reduces the latency and cost of retrieval-augmented generation (RAG) by serving cached answers to semantically similar queries, but most existing methods do not model the time-varying freshness of open-w…
Paraphrase GenerationLanguage Modeling and Understanding Through Paraphrase Generation and Detection
Language enables humans to share knowledge, reason about the world, and pass on strategies for survival and innovation across generations. At the heart of this process is not just the ability to communicate but also the …
Paraphrase GenerationSemPA: Improving Sentence Embeddings of Large Language Models through Semantic Preference Alignment
Traditional sentence embedding methods employ token-level contrastive learning on non-generative pre-trained models. Recently, there have emerged embedding methods based on generative large language models (LLMs). These …
Semantic Textual SimilarityParaphrase GenerationContrastive LearningSMCLM: Semantically Meaningful Causal Language Modeling for Autoregressive Paraphrase Generation
This article introduces semantically meaningful causal language modeling (SMCLM), a selfsupervised method of training autoregressive models to generate semantically equivalent text. Our approach involves using semantical…
Paraphrase GenerationEnhancing Paraphrase Type Generation: The Impact of DPO and RLHF Evaluated with Human-Ranked Data
Paraphrasing re-expresses meaning to enhance applications like text simplification, machine translation, and question-answering. Specific paraphrase types facilitate accurate semantic analysis and robust language models.…
Machine TranslationParaphrase GenerationParaphrase IdentificationQuestion Answering+2A Large-Scale Benchmark for Vietnamese Sentence Paraphrases
This paper presents ViSP, a high-quality Vietnamese dataset for sentence paraphrasing, consisting of 1.2M original-paraphrase pairs collected from various domains. The dataset was constructed using a hybrid approach that…
Paraphrase GenerationSentenceSMAB: MAB based word Sensitivity Estimation Framework and its Applications in Adversarial Text Generation
To understand the complexity of sequence classification tasks, Hahn et al. (2021) proposed sensitivity as the number of disjoint subsets of the input sequence that can each be individually changed to change the output. T…
Adversarial TextParaphrase GenerationSensitivitySentence+2Learning to Adapt to Low-Resource Paraphrase Generation
Paraphrase generation is a longstanding NLP task and achieves great success with the aid of large corpora. However, transferring a paraphrasing model to another domain encounters the problem of domain shifting especially…
Meta-LearningParaphrase GenerationLatent Paraphrasing: Perturbation on Layers Improves Knowledge Injection in Language Models
As Large Language Models (LLMs) are increasingly deployed in specialized domains with continuously evolving knowledge, the need for timely and precise knowledge injection has become essential. Fine-tuning with paraphrase…
DiversityParaphrase GenerationQuestion AnsweringMulti-Attribute Linguistic Tuning for Controlled Paraphrase Generation
We present a novel approach to paraphrase generation that enables precise control and fine-tuning of 40 linguistic attributes for English. Our model is an encoder-decoder architecture that takes as input a source sentenc…
AttributeDecoderParaphrase GenerationSentenceRetrieve, Generate, Evaluate: A Case Study for Medical Paraphrases Generation with Small Language Models
Recent surge in the accessibility of large language models (LLMs) to the general population can lead to untrackable use of such models for medical-related recommendations. Language generation via LLMs models has two key …
HallucinationParaphrase GenerationRetrievalRetrieval-augmented Generation+1Analyzing Persuasive Strategies in Meme Texts: A Fusion of Language Models with Paraphrase Enrichment
This paper describes our approach to hierarchical multi-label detection of persuasion techniques in meme texts. Our model, developed as a part of the recent SemEval task, is based on fine-tuning individual language model…
Data AugmentationDiversityParaphrase GenerationAction Controlled Paraphrasing
Recent studies have demonstrated the potential to control paraphrase generation, such as through syntax, which has broad applications in various downstream tasks. However, these methods often require detailed parse trees…
Paraphrase GenerationRepresentation LearningParameter Efficient Diverse Paraphrase Generation Using Sequence-Level Knowledge Distillation
Over the past year, the field of Natural Language Generation (NLG) has experienced an exponential surge, largely due to the introduction of Large Language Models (LLMs). These models have exhibited the most effective per…
DiversityKnowledge DistillationParaphrase GenerationText GenerationParaFusion: A Large-Scale LLM-Driven English Paraphrase Dataset Infused with High-Quality Lexical and Syntactic Diversity
Paraphrase generation is a pivotal task in natural language processing (NLP). Existing datasets in the domain lack syntactic and lexical diversity, resulting in paraphrases that closely resemble the source sentences. Mor…
DiversityParaphrase GenerationSemantic SimilaritySemantic Textual SimilarityImproved Paraphrase Generation via Controllable Latent Diffusion
Paraphrase generation strives to generate high-quality and diverse expressions of a given text, a domain where diffusion models excel. Though SOTA diffusion generation reconciles generation quality and diversity, textual…
DiversityParaphrase GenerationSLPL SHROOM at SemEval2024 Task 06: A comprehensive study on models ability to detect hallucination
Language models, particularly generative models, are susceptible to hallucinations, generating outputs that contradict factual knowledge or the source text. This study explores methods for detecting hallucinations in thr…
HallucinationMachine TranslationParaphrase GenerationSemantic Similarity+1SemEval-2024 Shared Task 6: SHROOM, a Shared-task on Hallucinations and Related Observable Overgeneration Mistakes
This paper presents the results of the SHROOM, a shared task focused on detecting hallucinations: outputs from natural language generation (NLG) systems that are fluent, yet inaccurate. Such cases of overgeneration put i…
Machine TranslationParaphrase GenerationText GenerationFine-tuning CLIP Text Encoders with Two-step Paraphrasing
Contrastive language-image pre-training (CLIP) models have demonstrated considerable success across various vision-language tasks, such as text-to-image retrieval, where the model is required to effectively process natur…
Image CaptioningImage RetrievalParaphrase GenerationRetrieval+1Neural Machine Translation for Malayalam Paraphrase Generation
This study explores four methods of generating paraphrases in Malayalam, utilizing resources available for English paraphrasing and pre-trained Neural Machine Translation (NMT) models. We evaluate the resulting paraphras…
Machine TranslationNMTParaphrase GenerationTranslation