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TabSTAR: A Foundation Tabular Model With Semantically Target-Aware Representations
Paper • 2505.18125 • Published • 112 -
On-Policy RL with Optimal Reward Baseline
Paper • 2505.23585 • Published • 14 -
Satori-SWE: Evolutionary Test-Time Scaling for Sample-Efficient Software Engineering
Paper • 2505.23604 • Published • 23 -
Are Reasoning Models More Prone to Hallucination?
Paper • 2505.23646 • Published • 24
Collections
Discover the best community collections!
Collections including paper arxiv:2505.23646
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Do I Know This Entity? Knowledge Awareness and Hallucinations in Language Models
Paper • 2411.14257 • Published • 14 -
Distinguishing Ignorance from Error in LLM Hallucinations
Paper • 2410.22071 • Published -
DeCoRe: Decoding by Contrasting Retrieval Heads to Mitigate Hallucinations
Paper • 2410.18860 • Published • 11 -
MLLM can see? Dynamic Correction Decoding for Hallucination Mitigation
Paper • 2410.11779 • Published • 26
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WebRL: Training LLM Web Agents via Self-Evolving Online Curriculum Reinforcement Learning
Paper • 2411.02337 • Published • 36 -
Mixture-of-Transformers: A Sparse and Scalable Architecture for Multi-Modal Foundation Models
Paper • 2411.04996 • Published • 51 -
Large Language Models Orchestrating Structured Reasoning Achieve Kaggle Grandmaster Level
Paper • 2411.03562 • Published • 68 -
StructRAG: Boosting Knowledge Intensive Reasoning of LLMs via Inference-time Hybrid Information Structurization
Paper • 2410.08815 • Published • 47
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EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters
Paper • 2402.04252 • Published • 29 -
Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation Models
Paper • 2402.03749 • Published • 14 -
ScreenAI: A Vision-Language Model for UI and Infographics Understanding
Paper • 2402.04615 • Published • 44 -
EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss
Paper • 2402.05008 • Published • 23
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Linear Correlation in LM's Compositional Generalization and Hallucination
Paper • 2502.04520 • Published • 10 -
How to Steer LLM Latents for Hallucination Detection?
Paper • 2503.01917 • Published • 11 -
Are Reasoning Models More Prone to Hallucination?
Paper • 2505.23646 • Published • 24 -
Why Language Models Hallucinate
Paper • 2509.04664 • Published • 193
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OpenAI o1 System Card
Paper • 2412.16720 • Published • 36 -
LearnLM: Improving Gemini for Learning
Paper • 2412.16429 • Published • 22 -
NILE: Internal Consistency Alignment in Large Language Models
Paper • 2412.16686 • Published • 8 -
Offline Reinforcement Learning for LLM Multi-Step Reasoning
Paper • 2412.16145 • Published • 38
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InfinityMATH: A Scalable Instruction Tuning Dataset in Programmatic Mathematical Reasoning
Paper • 2408.07089 • Published • 14 -
HelloBench: Evaluating Long Text Generation Capabilities of Large Language Models
Paper • 2409.16191 • Published • 42 -
Training Language Models to Self-Correct via Reinforcement Learning
Paper • 2409.12917 • Published • 140 -
Self-Boosting Large Language Models with Synthetic Preference Data
Paper • 2410.06961 • Published • 16
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Chain-of-Verification Reduces Hallucination in Large Language Models
Paper • 2309.11495 • Published • 39 -
Hallucination Detox: Sensitive Neuron Dropout (SeND) for Large Language Model Training
Paper • 2410.15460 • Published • 1 -
DeCoRe: Decoding by Contrasting Retrieval Heads to Mitigate Hallucinations
Paper • 2410.18860 • Published • 11 -
Do I Know This Entity? Knowledge Awareness and Hallucinations in Language Models
Paper • 2411.14257 • Published • 14
-
TabSTAR: A Foundation Tabular Model With Semantically Target-Aware Representations
Paper • 2505.18125 • Published • 112 -
On-Policy RL with Optimal Reward Baseline
Paper • 2505.23585 • Published • 14 -
Satori-SWE: Evolutionary Test-Time Scaling for Sample-Efficient Software Engineering
Paper • 2505.23604 • Published • 23 -
Are Reasoning Models More Prone to Hallucination?
Paper • 2505.23646 • Published • 24
-
Linear Correlation in LM's Compositional Generalization and Hallucination
Paper • 2502.04520 • Published • 10 -
How to Steer LLM Latents for Hallucination Detection?
Paper • 2503.01917 • Published • 11 -
Are Reasoning Models More Prone to Hallucination?
Paper • 2505.23646 • Published • 24 -
Why Language Models Hallucinate
Paper • 2509.04664 • Published • 193
-
Do I Know This Entity? Knowledge Awareness and Hallucinations in Language Models
Paper • 2411.14257 • Published • 14 -
Distinguishing Ignorance from Error in LLM Hallucinations
Paper • 2410.22071 • Published -
DeCoRe: Decoding by Contrasting Retrieval Heads to Mitigate Hallucinations
Paper • 2410.18860 • Published • 11 -
MLLM can see? Dynamic Correction Decoding for Hallucination Mitigation
Paper • 2410.11779 • Published • 26
-
OpenAI o1 System Card
Paper • 2412.16720 • Published • 36 -
LearnLM: Improving Gemini for Learning
Paper • 2412.16429 • Published • 22 -
NILE: Internal Consistency Alignment in Large Language Models
Paper • 2412.16686 • Published • 8 -
Offline Reinforcement Learning for LLM Multi-Step Reasoning
Paper • 2412.16145 • Published • 38
-
WebRL: Training LLM Web Agents via Self-Evolving Online Curriculum Reinforcement Learning
Paper • 2411.02337 • Published • 36 -
Mixture-of-Transformers: A Sparse and Scalable Architecture for Multi-Modal Foundation Models
Paper • 2411.04996 • Published • 51 -
Large Language Models Orchestrating Structured Reasoning Achieve Kaggle Grandmaster Level
Paper • 2411.03562 • Published • 68 -
StructRAG: Boosting Knowledge Intensive Reasoning of LLMs via Inference-time Hybrid Information Structurization
Paper • 2410.08815 • Published • 47
-
InfinityMATH: A Scalable Instruction Tuning Dataset in Programmatic Mathematical Reasoning
Paper • 2408.07089 • Published • 14 -
HelloBench: Evaluating Long Text Generation Capabilities of Large Language Models
Paper • 2409.16191 • Published • 42 -
Training Language Models to Self-Correct via Reinforcement Learning
Paper • 2409.12917 • Published • 140 -
Self-Boosting Large Language Models with Synthetic Preference Data
Paper • 2410.06961 • Published • 16
-
EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters
Paper • 2402.04252 • Published • 29 -
Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation Models
Paper • 2402.03749 • Published • 14 -
ScreenAI: A Vision-Language Model for UI and Infographics Understanding
Paper • 2402.04615 • Published • 44 -
EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss
Paper • 2402.05008 • Published • 23
-
Chain-of-Verification Reduces Hallucination in Large Language Models
Paper • 2309.11495 • Published • 39 -
Hallucination Detox: Sensitive Neuron Dropout (SeND) for Large Language Model Training
Paper • 2410.15460 • Published • 1 -
DeCoRe: Decoding by Contrasting Retrieval Heads to Mitigate Hallucinations
Paper • 2410.18860 • Published • 11 -
Do I Know This Entity? Knowledge Awareness and Hallucinations in Language Models
Paper • 2411.14257 • Published • 14