Copertina

THiNK: Can Large Language Models Think-aloud?

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Yu, Yongan, Wu, Mengqian, Lin, Yiran, Lobczowski, Nikki G.
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Computation and Language Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

From Recall to Reasoning: Automated Question Generation for Deeper Math Learning through Large Language Models
di: Yu, Yongan, et al.
Pubblicazione: (2025)

Can Vision Language Models Be Adaptive in Mathematics Education? A Learner Model-based Rubric Study
di: Gao, Jie, et al.
Pubblicazione: (2026)

Scaling up the think-aloud method
di: Wurgaft, Daniel, et al.
Pubblicazione: (2025)

WXImpactBench: A Disruptive Weather Impact Understanding Benchmark for Evaluating Large Language Models
di: Yu, Yongan, et al.
Pubblicazione: (2025)

Multi-Persona Thinking for Bias Mitigation in Large Language Models
di: Chen, Yuxing, et al.
Pubblicazione: (2026)

Cognitive Decision Routing in Large Language Models: When to Think Fast, When to Think Slow
di: Du, Y., et al.
Pubblicazione: (2025)

AdaptThink: Reasoning Models Can Learn When to Think
di: Zhang, Jiajie, et al.
Pubblicazione: (2025)

Can Large Language Models do Analytical Reasoning?
di: Hu, Yebowen, et al.
Pubblicazione: (2024)

Reasoning Models Can Be Effective Without Thinking
di: Ma, Wenjie, et al.
Pubblicazione: (2025)

Modeling Hierarchical Thinking in Large Reasoning Models
di: Shahariar, G M, et al.
Pubblicazione: (2025)

DynamicMind: A Tri-Mode Thinking System for Large Language Models
di: Li, Wei, et al.
Pubblicazione: (2025)

The Cost of Thinking: Increased Jailbreak Risk in Large Language Models
di: Yang, Fan
Pubblicazione: (2025)

When Can Large Reasoning Models Save Thinking? Mechanistic Analysis of Behavioral Divergence in Reasoning
di: Zhu, Rongzhi, et al.
Pubblicazione: (2025)

Think$^{2}$: Grounded Metacognitive Reasoning in Large Language Models
di: Elenjical, Abraham Paul, et al.
Pubblicazione: (2026)

Missed Connections: Lateral Thinking Puzzles for Large Language Models
di: Todd, Graham, et al.
Pubblicazione: (2024)

How do Large Language Models Handle Multilingualism?
di: Zhao, Yiran, et al.
Pubblicazione: (2024)

Can Large Language Models Express Uncertainty Like Human?
di: Tao, Linwei, et al.
Pubblicazione: (2025)

What are Models Thinking about? Understanding Large Language Model Hallucinations "Psychology" through Model Inner State Analysis
di: Wang, Peiran, et al.
Pubblicazione: (2025)

Large Language Models Can Self-Improve in Long-context Reasoning
di: Li, Siheng, et al.
Pubblicazione: (2024)

Fast-Slow-Thinking: Complex Task Solving with Large Language Models
di: Sun, Yiliu, et al.
Pubblicazione: (2025)

Incentivizing Dual Process Thinking for Efficient Large Language Model Reasoning
di: Cheng, Xiaoxue, et al.
Pubblicazione: (2025)

Enhancing Creativity in Large Language Models through Associative Thinking Strategies
di: Mehrotra, Pronita, et al.
Pubblicazione: (2024)

Unsupervised Real-Time Hallucination Detection based on the Internal States of Large Language Models
di: Su, Weihang, et al.
Pubblicazione: (2024)

Thinking Tokens for Language Modeling
di: Herel, David, et al.
Pubblicazione: (2024)

Chronological Thinking in Full-Duplex Spoken Dialogue Language Models
di: Wu, Donghang, et al.
Pubblicazione: (2025)

Ontology-Guided Reverse Thinking Makes Large Language Models Stronger on Knowledge Graph Question Answering
di: Liu, Runxuan, et al.
Pubblicazione: (2025)

Can Large Language Models Discern Evidence for Scientific Hypotheses? Case Studies in the Social Sciences
di: Koneru, Sai, et al.
Pubblicazione: (2023)

Thinking Before Constraining: A Unified Decoding Framework for Large Language Models
di: Nguyen, Ngoc Trinh Hung, et al.
Pubblicazione: (2026)

To Think or Not To Think, That is The Question for Large Reasoning Models in Theory of Mind Tasks
di: Gong, Nanxu, et al.
Pubblicazione: (2026)

Can Reasoning Help Large Language Models Capture Human Annotator Disagreement?
di: Ni, Jingwei, et al.
Pubblicazione: (2025)

To Think or Not to Think: Exploring the Unthinking Vulnerability in Large Reasoning Models
di: Zhu, Zihao, et al.
Pubblicazione: (2025)

DeepRAG: Thinking to Retrieve Step by Step for Large Language Models
di: Guan, Xinyan, et al.
Pubblicazione: (2025)

Position: Multimodal Large Language Models Can Significantly Advance Scientific Reasoning
di: Yan, Yibo, et al.
Pubblicazione: (2025)

LightReasoner: Can Small Language Models Teach Large Language Models Reasoning?
di: Wang, Jingyuan, et al.
Pubblicazione: (2025)

GLoRE: Evaluating Logical Reasoning of Large Language Models
di: liu, Hanmeng, et al.
Pubblicazione: (2023)

Thinking About Thinking: Evaluating Reasoning in Post-Trained Language Models
di: Singla, Pratham, et al.
Pubblicazione: (2025)

Language Agents Mirror Human Causal Reasoning Biases. How Can We Help Them Think Like Scientists?
di: GX-Chen, Anthony, et al.
Pubblicazione: (2025)

ItD: Large Language Models Can Teach Themselves Induction through Deduction
di: Sun, Wangtao, et al.
Pubblicazione: (2024)

Can Large Language Models Predict the Outcome of Judicial Decisions?
di: Kmainasi, Mohamed Bayan, et al.
Pubblicazione: (2025)

Break the Chain: Large Language Models Can be Shortcut Reasoners
di: Ding, Mengru, et al.
Pubblicazione: (2024)