Papers Natural Language Inference
“Natural Language Inference” 태그가 달린 논문 2,100편 · 필터 해제
A Comparative Explainability Framework for DeBERTa-v3 in Zero-Shot Medical Abstract Classification
A comparative explainability framework is presented to audit DeBERTa-v3 under zero-shot classification of medical abstracts. The work addresses the disagreement problem in Explainable Artificial Intelligence, where diffe…
Natural Language InferenceText ClassificationAn Empirical Study of Counterfactual Self-Explanations in LLMs
Large language models can easily generate explanations for their own outputs, but such self-explanations are not necessarily faithful to the model's behavior. We study this issue through counterfactual self-explanations,…
Natural Language InferenceSentiment AnalysisCan We Do Interpretable NLI with Graphs Based on Atomic Propositions?
While Large Language Model (LLM)-based Natural Language Inference (NLI) systems achieve high accuracy, their decision-making processes lack auditable structures. This paper explores whether NLI can be performed using onl…
Natural Language InferenceCascaded Batch Prompting
Although batch prompting makes large language model inference more efficient by processing multiple instances simultaneously, it suffers from unpredictable downstream task performance. We propose cascaded batch prompting…
Natural Language InferenceQuestion AnsweringTrustworthy RAG: An Evaluation Agent for Detecting Misinformation and Knowledge Poisoning in Generative AI Systems
Retrieval-Augmented Generation (RAG) grounds Large Language Model (LLM) outputs in external knowledge, but RAG systems usually trust whatever they retrieve, creating a Security-Reliability Gap: high semantic relevance do…
Natural Language InferenceStructure, Association, and Decision Value: Representation-Based Difficulty Estimation for Adaptive Inference in African-Language NLI
We ask whether internal representation statistics can provide useful example-level difficulty signals for adaptive inference in multilingual African NLP, and find that they cannot in this setting. Studying natural langua…
Natural Language InferenceConsensus Measures for Unstructured Biomedical Text Annotations
Biomedical literature is increasingly mined for knowledge beyond the questions it was written to answer. Because the target concepts are not known in advance, annotators prefer open-ended labels, whose agreement is hard …
Natural Language InferenceReference-Free Evaluation of Reasoning in Open-Ended Question Answering
AI-generated answers in high-stakes domains are often fluent but difficult to verify, especially when they contain multi-step reasoning rather than a single final answer. We propose a reasoning-based, reference-free fram…
Natural Language InferenceMathematical ReasoningQuestion AnsweringHow Much Human Label Variation Does Formal Semantic Structure Explain?: Group-Level Effects and Item-Level Ceilings in NLI
Human label variation in natural language inference is increasingly treated as signal rather than noise, but how much of it formal semantic structure explains has not been measured directly. We measure it on the 3,113 SN…
Natural Language InferenceTranslation as a Computationally Efficient Bridge: Feasibility of English BERT for Low-Resource Languages
BERT models have revolutionised Natural Language Processing (NLP) through their ability to process unstructured text across diverse domains. However, developing high-quality BERT models for non-English languages remains …
Natural Language InferencePart-Of-Speech TaggingHate Speech DetectionQuestion AnsweringDetecting Hallucinations in Retrieval-Augmented Generation through Grounding-Aware Sensitivity by Perturbation (GASP)
Retrieval-augmented generation (RAG) reduces but does not eliminate hallucination, and existing detectors return a single answer-level score that does not indicate which sentence is unsupported, or why. To close this gap…
Natural Language InferenceQuestion AnsweringWhere do LLMs Fall Short in CBT-Guided Affective Reasoning?
Cognitive Behavioral Therapy (CBT) provides a structured framework for understanding a user's mental state by examining the interaction between cognitive and behavioral factors. However, out-of-the-box LLMs respond fluen…
Natural Language InferenceWhat LLM Agents Say When No One Is Watching: Social Structure and Latent Objective Emergence in Multi-Agent Debates
LLM agents will increasingly act in socially structured settings where role, audience, and relational context can shape what is advantageous or costly to say. We study whether such social structure, without any explicit …
Natural Language InferenceSemantic SimilarityHow Far Can You Get Without a GPU? A Systematic Benchmark of Lightweight Hallucination Detection Across Question Answering, Dialogue, and Summarisation
Hallucination detection has become a pressing requirement for trustworthy AI deployment at scale. The most accurate detection methods depend on GPU-intensive inference, proprietary API calls, or white-box access to the g…
Natural Language InferenceSemantic SimilarityQuestion AnsweringMixedPEFT: Combining Multiple PEFT Methods with Mixed Objectives for Unsupervised Domain Adaptation
Pre-trained language models struggle when applied to new domains, as full fine-tuning is computationally expensive and prone to catastrophic forgetting. This study addresses this challenge by presenting a novel parameter…
Unsupervised Domain AdaptationNatural Language InferenceEvaluating LLM Personalization via Semantic Constraint Verification
Current evaluation paradigms for Large Language Model (LLM) personalization rely heavily on brittle surface-matching metrics or computationally expensive LLM-as-a-judge protocols, both of which lack interpretability. To …
Natural Language InferenceCan News Predict the Market? Limits of Zero-Shot Financial NLP and the Role of Explainable AI
Can financial news reliably predict short-term stock movements? Despite advances in large language models, this question remains unresolved. We revisit this problem using a zero-shot natural language processing framework…
Natural Language InferenceTruthSplit: Operationalizing Conditional Validity in Arguments Through Multi-Perspective Reasoning
We present TruthSplit, an interactive system for multi-perspective argument analysis. Existing argumentation tools typically analyze properties of the argument itself, such as structure, quality, stance, or persuasivenes…
Natural Language InferenceSHALA-LLM: Smartly Handling Ambiguous Labels in Aligning LLMs
Many human-centered tasks, including natural language inference (NLI) and emotion recognition (ER), have multiple plausible interpretations, leading to label ambiguity and challenging disagreements across human annotator…
Natural Language InferenceReinforcement LearningEmotion RecognitionSample-Size Scaling of the African Languages NLI Evaluation
African languages have very little labelled data, and it is unclear if augmenting the quantity of annotation data reliably enhances downstream performance. The study is a systematic sample-size scaling study of natural l…
Natural Language Inference