Buchumschlag

No Reliable Evidence of Self-Reported Sentience in Small Large Language Models

Fuente: arXiv

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	Kaiser, Caspar, Enderby, Sean
Format:	Preprint
Veröffentlicht:	2026
Schlagworte:	Computation and Language Artificial Intelligence
Online-Zugang:	Acceder al recurso
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Ähnliche Einträge

Small Language Model Can Self-correct
von: Han, Haixia, et al.
Veröffentlicht: (2024)

Amuro and Char: Analyzing the Relationship between Pre-Training and Fine-Tuning of Large Language Models
von: Sun, Kaiser, et al.
Veröffentlicht: (2024)

Small Language Model as Data Prospector for Large Language Model
von: Ni, Shiwen, et al.
Veröffentlicht: (2024)

Evidence of Phase Transitions in Small Transformer-Based Language Models
von: Hong, Noah, et al.
Veröffentlicht: (2025)

On the Failure of Latent State Persistence in Large Language Models
von: Huang, Jen-tse, et al.
Veröffentlicht: (2025)

RadAnnotate: Large Language Models for Efficient and Reliable Radiology Report Annotation
von: Shetty, Saisha Pradeep, et al.
Veröffentlicht: (2026)

Beyond Self-Reports: Multi-Observer Agents for Personality Assessment in Large Language Models
von: Huang, Yin Jou, et al.
Veröffentlicht: (2025)

Domain-Adapted Small Language Models for Reliable Clinical Triage
von: Aljohani, Manar, et al.
Veröffentlicht: (2026)

Semantic Consistency for Assuring Reliability of Large Language Models
von: Raj, Harsh, et al.
Veröffentlicht: (2023)

Enhancing Answer Reliability Through Inter-Model Consensus of Large Language Models
von: Amiri-Margavi, Alireza, et al.
Veröffentlicht: (2024)

Towards Reliable Truth-Aligned Uncertainty Estimation in Large Language Models
von: Srey, Ponhvoan, et al.
Veröffentlicht: (2026)

Hallucination Detection: Robustly Discerning Reliable Answers in Large Language Models
von: Chen, Yuyan, et al.
Veröffentlicht: (2024)

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

Federated Co-tuning Framework for Large and Small Language Models
von: Fan, Tao, et al.
Veröffentlicht: (2024)

APCD: Adaptive Path-Contrastive Decoding for Reliable Large Language Model Generation
von: Zheng, Tianyu, et al.
Veröffentlicht: (2026)

Soft Token Attacks Cannot Reliably Audit Unlearning in Large Language Models
von: Chen, Haokun, et al.
Veröffentlicht: (2025)

A Large Language Model-Empowered Agent for Reliable and Robust Structural Analysis
von: Liu, Jiachen, et al.
Veröffentlicht: (2025)

AA-Omniscience: Evaluating Cross-Domain Knowledge Reliability in Large Language Models
von: Jackson, Declan, et al.
Veröffentlicht: (2025)

How Open Must Language Models be to Enable Reliable Scientific Inference?
von: Michaelov, James A., et al.
Veröffentlicht: (2026)

Assessing Personalized AI Mentoring with Large Language Models in the Computing Field
von: Luo, Xiao, et al.
Veröffentlicht: (2024)

Plug-in and Fine-tuning: Bridging the Gap between Small Language Models and Large Language Models
von: Kim, Kyeonghyun, et al.
Veröffentlicht: (2025)

Self-Prompting Small Language Models for Privacy-Sensitive Clinical Information Extraction
von: Chuang, Yao-Shun, et al.
Veröffentlicht: (2026)

All Language Models Large and Small
von: Chen, Zhixun, et al.
Veröffentlicht: (2024)

A Survey on Collaborative Mechanisms Between Large and Small Language Models
von: Chen, Yi, et al.
Veröffentlicht: (2025)

Improving Phishing Email Detection Performance of Small Large Language Models
von: Lin, Zijie, et al.
Veröffentlicht: (2025)

PDDL-Mind: Large Language Models are Capable on Belief Reasoning with Reliable State Tracking
von: Zhu, Wang Bill, et al.
Veröffentlicht: (2026)

Med-V1: Small Language Models for Zero-shot and Scalable Biomedical Evidence Attribution
von: Jin, Qiao, et al.
Veröffentlicht: (2026)

Using Large Language Models to Measure Symptom Severity in Patients At Risk for Schizophrenia
von: Chen, Andrew X., et al.
Veröffentlicht: (2025)

Energy Landscapes Enable Reliable Abstention in Retrieval-Augmented Large Language Models for Healthcare
von: Shankar, Ravi, et al.
Veröffentlicht: (2025)

Reliable Academic Conference Question Answering: A Study Based on Large Language Model
von: Huang, Zhiwei, et al.
Veröffentlicht: (2023)

Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models
von: Xiong, Guangzhi, et al.
Veröffentlicht: (2025)

Self-Discover: Large Language Models Self-Compose Reasoning Structures
von: Zhou, Pei, et al.
Veröffentlicht: (2024)

Are Large Language Models Reliable Argument Quality Annotators?
von: Mirzakhmedova, Nailia, et al.
Veröffentlicht: (2024)

Self-Cognition in Large Language Models: An Exploratory Study
von: Chen, Dongping, et al.
Veröffentlicht: (2024)

GRATH: Gradual Self-Truthifying for Large Language Models
von: Chen, Weixin, et al.
Veröffentlicht: (2024)

A Survey on Self-Evolution of Large Language Models
von: Tao, Zhengwei, et al.
Veröffentlicht: (2024)

SelfElicit: Your Language Model Secretly Knows Where is the Relevant Evidence
von: Liu, Zhining, et al.
Veröffentlicht: (2025)

Confidence-Calibrated Small-Large Language Model Collaboration for Cost-Efficient Reasoning
von: Zhang, Chuang, et al.
Veröffentlicht: (2026)

FedMKT: Federated Mutual Knowledge Transfer for Large and Small Language Models
von: Fan, Tao, et al.
Veröffentlicht: (2024)

Large Language Models as Universal Predictors? An Empirical Study on Small Tabular Datasets
von: Pavlidis, Nikolaos, et al.
Veröffentlicht: (2025)