Saved in:
| Main Authors: | Snel, Jakob, Oh, Seong Joon |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.20836 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
DISCO: Diversifying Sample Condensation for Efficient Model Evaluation
by: Rubinstein, Alexander, et al.
Published: (2025)
by: Rubinstein, Alexander, et al.
Published: (2025)
Do Deep Neural Network Solutions Form a Star Domain?
by: Sonthalia, Ankit, et al.
Published: (2024)
by: Sonthalia, Ankit, et al.
Published: (2024)
Dynamics Reveals Structure: Challenging the Linear Propagation Assumption
by: Chang, Hoyeon, et al.
Published: (2026)
by: Chang, Hoyeon, et al.
Published: (2026)
CPR: Mitigating Large Language Model Hallucinations with Curative Prompt Refinement
by: Shim, Jung-Woo, et al.
Published: (2025)
by: Shim, Jung-Woo, et al.
Published: (2025)
Multi-stage Prompt Refinement for Mitigating Hallucinations in Large Language Models
by: Shim, Jung-Woo, et al.
Published: (2025)
by: Shim, Jung-Woo, et al.
Published: (2025)
Scaling Up Membership Inference: When and How Attacks Succeed on Large Language Models
by: Puerto, Haritz, et al.
Published: (2024)
by: Puerto, Haritz, et al.
Published: (2024)
Universal Algorithm-Implicit Learning
by: Woerner, Stefano, et al.
Published: (2026)
by: Woerner, Stefano, et al.
Published: (2026)
LLM generation novelty through the lens of semantic similarity
by: Davydov, Philipp, et al.
Published: (2025)
by: Davydov, Philipp, et al.
Published: (2025)
Dr.LLM: Dynamic Layer Routing in LLMs
by: Heakl, Ahmed, et al.
Published: (2025)
by: Heakl, Ahmed, et al.
Published: (2025)
Calibrating Large Language Models Using Their Generations Only
by: Ulmer, Dennis, et al.
Published: (2024)
by: Ulmer, Dennis, et al.
Published: (2024)
Are We Done with Object-Centric Learning?
by: Rubinstein, Alexander, et al.
Published: (2025)
by: Rubinstein, Alexander, et al.
Published: (2025)
Scalable Ensemble Diversification for OOD Generalization and Detection
by: Rubinstein, Alexander, et al.
Published: (2024)
by: Rubinstein, Alexander, et al.
Published: (2024)
OrthoRank: Token Selection via Sink Token Orthogonality for Efficient LLM inference
by: Shin, Seungjun, et al.
Published: (2025)
by: Shin, Seungjun, et al.
Published: (2025)
Squish and Release: Exposing Hidden Hallucinations by Making Them Surface as Safety Signals
by: Oh, Nathaniel, et al.
Published: (2026)
by: Oh, Nathaniel, et al.
Published: (2026)
Towards User-Focused Research in Training Data Attribution for Human-Centered Explainable AI
by: Nguyen, Elisa, et al.
Published: (2024)
by: Nguyen, Elisa, et al.
Published: (2024)
Towards Dynamic Trend Filtering through Trend Point Detection with Reinforcement Learning
by: Seong, Jihyeon, et al.
Published: (2024)
by: Seong, Jihyeon, et al.
Published: (2024)
From Generator to Embedder: Harnessing Innate Abilities of Multimodal LLMs via Building Zero-Shot Discriminative Embedding Model
by: Ju, Yeong-Joon, et al.
Published: (2025)
by: Ju, Yeong-Joon, et al.
Published: (2025)
TRAP: Targeted Random Adversarial Prompt Honeypot for Black-Box Identification
by: Gubri, Martin, et al.
Published: (2024)
by: Gubri, Martin, et al.
Published: (2024)
Mitigating Shortcut Learning with Diffusion Counterfactuals and Diverse Ensembles
by: Scimeca, Luca, et al.
Published: (2023)
by: Scimeca, Luca, et al.
Published: (2023)
MASEval: Extending Multi-Agent Evaluation from Models to Systems
by: Emde, Cornelius, et al.
Published: (2026)
by: Emde, Cornelius, et al.
Published: (2026)
Energy-Efficient Wireless LLM Inference via Uncertainty and Importance-Aware Speculative Decoding
by: Park, Jihoon, et al.
Published: (2025)
by: Park, Jihoon, et al.
Published: (2025)
Scratching Visual Transformer's Back with Uniform Attention
by: Hyeon-Woo, Nam, et al.
Published: (2022)
by: Hyeon-Woo, Nam, et al.
Published: (2022)
SelfReflect: Can LLMs Communicate Their Internal Answer Distribution?
by: Kirchhof, Michael, et al.
Published: (2025)
by: Kirchhof, Michael, et al.
Published: (2025)
Two Heads Are Better than One: Simulating Large Transformers with Small Ones
by: Yu, Hantao, et al.
Published: (2025)
by: Yu, Hantao, et al.
Published: (2025)
What Makes Looped Transformers Perform Better Than Non-Recursive Ones
by: Gong, Zixuan, et al.
Published: (2025)
by: Gong, Zixuan, et al.
Published: (2025)
Scalable Token-Level Hallucination Detection in Large Language Models
by: Min, Rui, et al.
Published: (2026)
by: Min, Rui, et al.
Published: (2026)
Qubit-centric Transformer for Surface Code Decoding
by: Park, Seong-Joon, et al.
Published: (2025)
by: Park, Seong-Joon, et al.
Published: (2025)
Performance Control in Early Exiting to Deploy Large Models at the Same Cost of Smaller Ones
by: Mofakhami, Mehrnaz, et al.
Published: (2024)
by: Mofakhami, Mehrnaz, et al.
Published: (2024)
Abstraction for Offline Goal-Conditioned Reinforcement Learning
by: Wibault, Clarisse, et al.
Published: (2026)
by: Wibault, Clarisse, et al.
Published: (2026)
It Takes Two: Complementary Self-Distillation for Contextual Integrity in LLMs
by: Park, Sangwoo, et al.
Published: (2026)
by: Park, Sangwoo, et al.
Published: (2026)
NBDI: A Simple and Effective Termination Condition for Skill Extraction from Task-Agnostic Demonstrations
by: Kim, Myunsoo, et al.
Published: (2025)
by: Kim, Myunsoo, et al.
Published: (2025)
Efficient Contrastive Decoding with Probabilistic Hallucination Detection - Mitigating Hallucinations in Large Vision Language Models -
by: Fieback, Laura, et al.
Published: (2025)
by: Fieback, Laura, et al.
Published: (2025)
Design Conditions for Intra-Group Learning of Sequence-Level Rewards: Token Gradient Cancellation
by: Ding, Fei, et al.
Published: (2026)
by: Ding, Fei, et al.
Published: (2026)
Explicit Diversity Conditions for Effective Question Answer Generation with Large Language Models
by: Yadav, Vikas, et al.
Published: (2024)
by: Yadav, Vikas, et al.
Published: (2024)
Goal-Conditioned Agents that Learn Everything All at Once
by: Matthews, Michael, et al.
Published: (2026)
by: Matthews, Michael, et al.
Published: (2026)
ERBench: An Entity-Relationship based Automatically Verifiable Hallucination Benchmark for Large Language Models
by: Oh, Jio, et al.
Published: (2024)
by: Oh, Jio, et al.
Published: (2024)
Let Me Think! A Long Chain-of-Thought Can Be Worth Exponentially Many Short Ones
by: Mirtaheri, Parsa, et al.
Published: (2025)
by: Mirtaheri, Parsa, et al.
Published: (2025)
Unsupervised Feature Selection to Identify Important ICD-10 Codes for Machine Learning: A Case Study on a Coronary Artery Disease Patient Cohort
by: Ghasemi, Peyman, et al.
Published: (2023)
by: Ghasemi, Peyman, et al.
Published: (2023)
Query-Conditioned Test-Time Self-Training for Large Language Models
by: Song, Chaehee, et al.
Published: (2026)
by: Song, Chaehee, et al.
Published: (2026)
Detecting Hallucinations in Large Language Model Generation: A Token Probability Approach
by: Quevedo, Ernesto, et al.
Published: (2024)
by: Quevedo, Ernesto, et al.
Published: (2024)
Similar Items
-
DISCO: Diversifying Sample Condensation for Efficient Model Evaluation
by: Rubinstein, Alexander, et al.
Published: (2025) -
Do Deep Neural Network Solutions Form a Star Domain?
by: Sonthalia, Ankit, et al.
Published: (2024) -
Dynamics Reveals Structure: Challenging the Linear Propagation Assumption
by: Chang, Hoyeon, et al.
Published: (2026) -
CPR: Mitigating Large Language Model Hallucinations with Curative Prompt Refinement
by: Shim, Jung-Woo, et al.
Published: (2025) -
Multi-stage Prompt Refinement for Mitigating Hallucinations in Large Language Models
by: Shim, Jung-Woo, et al.
Published: (2025)