Who Do LLMs Trust? Human Experts Matter More Than Other LLMs
Fuente:
arXiv
Guardado en:
| Autores principales: | Bajaj, Anooshka, Tiganj, Zoran |
|---|---|
| Formato: | Preprint |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Beyond Semantics: How Temporal Biases Shape Retrieval in Transformer and State-Space Models
por: Bajaj, Anooshka, et al.
Publicado: (2025)
por: Bajaj, Anooshka, et al.
Publicado: (2025)
Emergence of Episodic Memory in Transformers: Characterizing Changes in Temporal Structure of Attention Scores During Training
por: Mistry, Deven Mahesh, et al.
Publicado: (2025)
por: Mistry, Deven Mahesh, et al.
Publicado: (2025)
Temporal Dependencies in In-Context Learning: The Role of Induction Heads
por: Bajaj, Anooshka, et al.
Publicado: (2026)
por: Bajaj, Anooshka, et al.
Publicado: (2026)
High Volatility and Action Bias Distinguish LLMs from Humans in Group Coordination
por: Maini, Sahaj Singh, et al.
Publicado: (2026)
por: Maini, Sahaj Singh, et al.
Publicado: (2026)
Gradual Forgetting: Logarithmic Compression for Extending Transformer Context Windows
por: Dickson, Billy, et al.
Publicado: (2025)
por: Dickson, Billy, et al.
Publicado: (2025)
Different Paths to Harmful Compliance: Behavioral Side Effects and Mechanistic Divergence Across LLM Jailbreaks
por: Kabir, Md Rysul, et al.
Publicado: (2026)
por: Kabir, Md Rysul, et al.
Publicado: (2026)
Deep reinforcement learning with time-scale invariant memory
por: Kabir, Md Rysul, et al.
Publicado: (2024)
por: Kabir, Md Rysul, et al.
Publicado: (2024)
LLMs Struggle with Abstract Meaning Comprehension More Than Expected
por: Alhazmi, Hamoud, et al.
Publicado: (2026)
por: Alhazmi, Hamoud, et al.
Publicado: (2026)
Trust & Safety of LLMs and LLMs in Trust & Safety
por: You, Doohee, et al.
Publicado: (2024)
por: You, Doohee, et al.
Publicado: (2024)
Label Over Logic? How Source Cues Bias Human Fallacy Judgments More Than LLMs
por: Nahar, Mahjabin, et al.
Publicado: (2026)
por: Nahar, Mahjabin, et al.
Publicado: (2026)
Many LLMs Are More Utilitarian Than One
por: Keshmirian, Anita, et al.
Publicado: (2025)
por: Keshmirian, Anita, et al.
Publicado: (2025)
LLMs Position Themselves as More Rational Than Humans: Emergence of AI Self-Awareness Measured Through Game Theory
por: Kim, Kyung-Hoon
Publicado: (2025)
por: Kim, Kyung-Hoon
Publicado: (2025)
LLMs Know More Than Words: A Genre Study with Syntax, Metaphor & Phonetics
por: Shi, Weiye, et al.
Publicado: (2025)
por: Shi, Weiye, et al.
Publicado: (2025)
When Models Know More Than They Say: Probing Analogical Reasoning in LLMs
por: McGovern, Hope, et al.
Publicado: (2026)
por: McGovern, Hope, et al.
Publicado: (2026)
LLMs Know More Than They Show: On the Intrinsic Representation of LLM Hallucinations
por: Orgad, Hadas, et al.
Publicado: (2024)
por: Orgad, Hadas, et al.
Publicado: (2024)
Analysis of LLMs vs Human Experts in Requirements Engineering
por: Hymel, Cory, et al.
Publicado: (2025)
por: Hymel, Cory, et al.
Publicado: (2025)
LLMs Do Not Grade Essays Like Humans
por: Mathew, Jerin George, et al.
Publicado: (2026)
por: Mathew, Jerin George, et al.
Publicado: (2026)
Diffusion LLMs Can Do Faster-Than-AR Inference via Discrete Diffusion Forcing
por: Wang, Xu, et al.
Publicado: (2025)
por: Wang, Xu, et al.
Publicado: (2025)
Human aversion? Do AI Agents Judge Identity More Harshly Than Performance
por: Feng, Yuanjun, et al.
Publicado: (2025)
por: Feng, Yuanjun, et al.
Publicado: (2025)
Product of Experts with LLMs: Boosting Performance on ARC Is a Matter of Perspective
por: Franzen, Daniel, et al.
Publicado: (2025)
por: Franzen, Daniel, et al.
Publicado: (2025)
Who is More Bayesian: Humans or ChatGPT?
por: Mu, Tianshi, et al.
Publicado: (2025)
por: Mu, Tianshi, et al.
Publicado: (2025)
Do LLMs Exhibit Human-Like Reasoning? Evaluating Theory of Mind in LLMs for Open-Ended Responses
por: Amirizaniani, Maryam, et al.
Publicado: (2024)
por: Amirizaniani, Maryam, et al.
Publicado: (2024)
TradExpert: Revolutionizing Trading with Mixture of Expert LLMs
por: Ding, Qianggang, et al.
Publicado: (2024)
por: Ding, Qianggang, et al.
Publicado: (2024)
Benchmarking Source-Sensitive Reasoning in Turkish: Humans and LLMs under Evidential Trust Manipulation
por: Karakaş, Sercan, et al.
Publicado: (2026)
por: Karakaş, Sercan, et al.
Publicado: (2026)
Are LLMs More Skeptical of Entertainment News?
por: Lai, Huiqian
Publicado: (2026)
por: Lai, Huiqian
Publicado: (2026)
Are LLMs Smarter Than Chimpanzees? An Evaluation on Perspective Taking and Knowledge State Estimation
por: Yang, Dingyi, et al.
Publicado: (2026)
por: Yang, Dingyi, et al.
Publicado: (2026)
Debating with More Persuasive LLMs Leads to More Truthful Answers
por: Khan, Akbir, et al.
Publicado: (2024)
por: Khan, Akbir, et al.
Publicado: (2024)
Labels Matter More Than Models: Rethinking the Unsupervised Paradigm in Time Series Anomaly Detection
por: Zhong, Zhijie, et al.
Publicado: (2025)
por: Zhong, Zhijie, et al.
Publicado: (2025)
FLEx: Personalized Federated Learning for Mixture-of-Experts LLMs via Expert Grafting
por: Liu, Fan, et al.
Publicado: (2025)
por: Liu, Fan, et al.
Publicado: (2025)
Sparks of Rationality: Do Reasoning LLMs Align with Human Judgment and Choice?
por: Tak, Ala N., et al.
Publicado: (2026)
por: Tak, Ala N., et al.
Publicado: (2026)
Who Does What? Archetypes of Roles Assigned to LLMs During Human-AI Decision-Making
por: Chappidi, Shreya, et al.
Publicado: (2026)
por: Chappidi, Shreya, et al.
Publicado: (2026)
Do LLMs Dream of Ontologies?
por: Bombieri, Marco, et al.
Publicado: (2024)
por: Bombieri, Marco, et al.
Publicado: (2024)
How Likely Do LLMs with CoT Mimic Human Reasoning?
por: Bao, Guangsheng, et al.
Publicado: (2024)
por: Bao, Guangsheng, et al.
Publicado: (2024)
Rethinking Layer Redundancy: Calibration Matters More Than Search in LLM Depth Pruning
por: Kim, Minkyu, et al.
Publicado: (2026)
por: Kim, Minkyu, et al.
Publicado: (2026)
Are Biological Systems More Intelligent Than Artificial Intelligence?
por: Bennett, Michael Timothy
Publicado: (2024)
por: Bennett, Michael Timothy
Publicado: (2024)
The Machine Learning Canvas: Empirical Findings on Why Strategy Matters More Than AI Code Generation
por: Prause, Martin
Publicado: (2026)
por: Prause, Martin
Publicado: (2026)
Confidence-Aware Alignment Makes Reasoning LLMs More Reliable
por: Chen, Kejia, et al.
Publicado: (2026)
por: Chen, Kejia, et al.
Publicado: (2026)
Safety Instincts: LLMs Learn to Trust Their Internal Compass for Self-Defense
por: Shen, Guobin, et al.
Publicado: (2025)
por: Shen, Guobin, et al.
Publicado: (2025)
From Logic to Language: A Trust Index for Problem Solving with LLMs
por: Rug, Tehseen, et al.
Publicado: (2025)
por: Rug, Tehseen, et al.
Publicado: (2025)
One for All: A General Framework of LLMs-based Multi-Criteria Decision Making on Human Expert Level
por: Wang, Hui, et al.
Publicado: (2025)
por: Wang, Hui, et al.
Publicado: (2025)
Ejemplares similares
-
Beyond Semantics: How Temporal Biases Shape Retrieval in Transformer and State-Space Models
por: Bajaj, Anooshka, et al.
Publicado: (2025) -
Emergence of Episodic Memory in Transformers: Characterizing Changes in Temporal Structure of Attention Scores During Training
por: Mistry, Deven Mahesh, et al.
Publicado: (2025) -
Temporal Dependencies in In-Context Learning: The Role of Induction Heads
por: Bajaj, Anooshka, et al.
Publicado: (2026) -
High Volatility and Action Bias Distinguish LLMs from Humans in Group Coordination
por: Maini, Sahaj Singh, et al.
Publicado: (2026) -
Gradual Forgetting: Logarithmic Compression for Extending Transformer Context Windows
por: Dickson, Billy, et al.
Publicado: (2025)