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Top Conference Papers
Award-winning papers from top conferences such as CVPR, NeurIPS, ICML and ACL
papers

SQuAD: 100,000+ Questions for Machine Comprehension of Text
























Neural Machine Translation of Rare Words with Subword Units
Hateful Symbols or Hateful People? Predictive Features for Hate Speech Detection on Twitter
A Probabilistic Earley Parser as a Psycholinguistic Model
ViLL-E: Video LLM Embeddings for Retrieval
Systematicity between Forms and Meanings across Languages Supports Efficient Communication
Mind the (DH) Gap! A Contrast in Risky Choices Between Reasoning and Conversational LLMs
Maximizing Local Entropy Where It Matters: Prefix-Aware Localized LLM Unlearning
MAUBERT: Universal Phonetic Inductive Biases for Few-Shot Acoustic Units Discovery
Massively Multilingual Joint Segmentation and Glossing
Lying with Truths: Open-Channel Multi-Agent Collusion for Belief Manipulation via Generative Montage
Hierarchical Intelligent Acoustic-Semantic Modeling: Modality Separation and Alignment for Full-Duplex SLMs
GeoRA: Geometry-Aware Low-Rank Adaptation for RLVR
From Local to Global: Revisiting Structured Pruning Paradigms for Large Language Models
Evolutionary Guided Decoding: Iterative Value Refinement for LLMs
CxMP: A Linguistic Minimal-Pair Benchmark for Evaluating Constructional Understanding in Language Models
CIG: Measuring Conversational Information Gain in Deliberative Dialogues with Semantic Memory Dynamics
CAR-bench: Evaluating the Consistency and Limit-Awareness of LLM Agents under Real-World Uncertaint
The Imperfective Paradox in Large Language Models
Memory efficiency and resource-rational encoding in sentence processing
Characterizing the Expressivity of Local Attention in Transformers
MediEval: A Unified Medical Benchmark for Patient-Contextual and Knowledge-Grounded Reasoning in LLMs
Beyond the Final Actor: Modeling the Dual Roles of Creator and Editor for Fine-Grained LLM-Generated Text Detection
Rethinking Entropy Interventions in RLVR: An Entropy Change Perspective
CURE: Critique-Driven Unified Reinforcement Learning for Test-Time Self-Improvement