Papers Negation
“Negation” 태그가 달린 논문 608편 · 필터 해제
Modeling (Deontic) Modal Operators With the s(CASP) Goal-directed Predicate Answer Set Programming System
We consider the problem of implementing deontic modal logic. We show how (deontic) modal operators can be expressed elegantly using default negation (negation-as-failure) and strong negation present in answer set program…
NegationStep-by-Step Video-to-Audio Synthesis via Negative Audio Guidance
We propose a novel step-by-step video-to-audio generation method that sequentially produces individual audio tracks, each corresponding to a specific sound event in the video. Our approach mirrors traditional Foley workf…
Audio GenerationAudio SynthesisNegationThunder-NUBench: A Benchmark for LLMs' Sentence-Level Negation Understanding
Negation is a fundamental linguistic phenomenon that poses persistent challenges for Large Language Models (LLMs), particularly in tasks requiring deep semantic understanding. Existing benchmarks often treat negation as …
Multiple-choiceNatural Language InferenceNegationSentencePropositional Logic for Probing Generalization in Neural Networks
The extent to which neural networks are able to acquire and represent symbolic rules remains a key topic of research and debate. Much current work focuses on the impressive capabilities of large language models, as well …
NegationAntithetic Noise in Diffusion Models
We initiate a systematic study of antithetic initial noise in diffusion models. Across unconditional models trained on diverse datasets, text-conditioned latent-diffusion models, and diffusion-posterior samplers, we find…
DiversityNegationUncertainty QuantificationReXVQA: A Large-scale Visual Question Answering Benchmark for Generalist Chest X-ray Understanding
We present ReXVQA, the largest and most comprehensive benchmark for visual question answering (VQA) in chest radiology, comprising approximately 696,000 questions paired with 160,000 chest X-rays studies across training,…
NegationNegation DetectionQuestion AnsweringVisual Question Answering+1Probing the Geometry of Truth: Consistency and Generalization of Truth Directions in LLMs Across Logical Transformations and Question Answering Tasks
Large language models (LLMs) are trained on extensive datasets that encapsulate substantial world knowledge. However, their outputs often include confidently stated inaccuracies. Earlier works suggest that LLMs encode tr…
In-Context LearningNegationQuestion AnsweringWorld KnowledgeThree Kinds of Negation in Knowledge and Their Mathematical Foundations
In the field of artificial intelligence, understanding, distinguishing, expressing, and computing the negation in knowledge is a fundamental issue in knowledge processing and research. In this paper, we examine and analy…
NegationPhilosophyOn Fairness of Task Arithmetic: The Role of Task Vectors
Model editing techniques, particularly task arithmetic using task vectors, have shown promise in efficiently modifying pre-trained models through arithmetic operations like task addition and negation. Despite computation…
FairnessHate Speech DetectionModel EditingNegation+2NegVQA: Can Vision Language Models Understand Negation?
Negation is a fundamental linguistic phenomenon that can entirely reverse the meaning of a sentence. As vision language models (VLMs) continue to advance and are deployed in high-stakes applications, assessing their abil…
NegationQuestion AnsweringSentenceVisual Question Answering+1What Has Been Lost with Synthetic Evaluation?
Large language models (LLMs) are increasingly used for data generation. However, creating evaluation benchmarks raises the bar for this emerging paradigm. Benchmarks must target specific phenomena, penalize exploiting sh…
NegationReading ComprehensionvalidTNG-CLIP:Training-Time Negation Data Generation for Negation Awareness of CLIP
Vision-language models (VLMs), such as CLIP, have demonstrated strong performance across a range of downstream tasks. However, CLIP is still limited in negation understanding: the ability to recognize the absence or excl…
Image CaptioningImage GenerationImage RetrievalImage-text matching+6CROC: Evaluating and Training T2I Metrics with Pseudo- and Human-Labeled Contrastive Robustness Checks
The assessment of evaluation metrics (meta-evaluation) is crucial for determining the suitability of existing metrics in text-to-image (T2I) generation tasks. Human-based meta-evaluation is costly and time-intensive, and…
NegationA New Tractable Description Logic under Categorical Semantics
Biomedical ontologies contain numerous concept or role names involving negative knowledge such as lacks_part, absence_of. Such a representation with labels rather than logical constructors would not allow a reasoner to i…
NegationFormalMATH: Benchmarking Formal Mathematical Reasoning of Large Language Models
Formal mathematical reasoning remains a critical challenge for artificial intelligence, hindered by limitations of existing benchmarks in scope and scale. To address this, we present FormalMATH, a large-scale Lean4 bench…
BenchmarkingMathematical ReasoningNegationHigh-Fidelity Pseudo-label Generation by Large Language Models for Training Robust Radiology Report Classifiers
Automated labeling of chest X-ray reports is essential for enabling downstream tasks such as training image-based diagnostic models, population health studies, and clinical decision support. However, the high variability…
DiagnosticKnowledge DistillationNegationPseudo LabelTransformers for Complex Query Answering over Knowledge Hypergraphs
Complex Query Answering (CQA) has been extensively studied in recent years. In order to model data that is closer to real-world distribution, knowledge graphs with different modalities have been introduced. Triple KGs, a…
Complex Query AnsweringKnowledge GraphsNegationDisentangling Linguistic Features with Dimension-Wise Analysis of Vector Embeddings
Understanding the inner workings of neural embeddings, particularly in models such as BERT, remains a challenge because of their high-dimensional and opaque nature. This paper proposes a framework for uncovering the spec…
NegationSentenceWhen is Task Vector Provably Effective for Model Editing? A Generalization Analysis of Nonlinear Transformers
Task arithmetic refers to editing the pre-trained model by adding a weighted sum of task vectors, each of which is the weight update from the pre-trained model to fine-tuned models for certain tasks. This approach recent…
Binary ClassificationDomain GeneralizationLarge Language ModelMachine Unlearning+4negativas: a prototype for searching and classifying sentential negation in speech data
Negation is a universal feature of natural languages. In Brazilian Portuguese, the most commonly used negation particle is n\~ao, which can scope over nouns or verbs. When it scopes over a verb, n\~ao can occur in three …
Negation