:: Library Catalog

Imagen de Portada

Guardado en:

Detalles Bibliográficos
Autor principal:	Lee, Messi H. J.
Formato:	Preprint
Publicado:	2026
Materias:	Artificial Intelligence
Acceso en línea:	https://arxiv.org/abs/2605.28114
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Correlating instruction-tuning (in multimodal models) with vision-language processing (in the brain)
por: Oota, Subba Reddy, et al.
Publicado: (2025)

Reinforcement learning fine-tuning of language model for instruction following and math reasoning
por: Han, Yifu, et al.
Publicado: (2025)

Zero-shot cross-lingual transfer in instruction tuning of large language models
por: Chirkova, Nadezhda, et al.
Publicado: (2024)

Implicit Bias-Like Patterns in Reasoning Models
por: Lee, Messi H. J., et al.
Publicado: (2025)

The effect of fine-tuning on language model toxicity
por: Hawkins, Will, et al.
Publicado: (2024)

More Distinctively Black and Feminine Faces Lead to Increased Stereotyping in Vision-Language Models
por: Lee, Messi H. J., et al.
Publicado: (2024)

WizardLM: Empowering large pre-trained language models to follow complex instructions
por: Xu, Can, et al.
Publicado: (2023)

Machine individuality: Separating genuine idiosyncrasy from response bias in large language models
por: Kriegmair, Valentin, et al.
Publicado: (2026)

Inducing anxiety in large language models can induce bias
por: Coda-Forno, Julian, et al.
Publicado: (2023)

MARCUS: An agentic, multimodal vision-language model for cardiac diagnosis and management
por: O'Sullivan, Jack W, et al.
Publicado: (2026)

Evolution of meta's llama models and parameter-efficient fine-tuning of large language models: a survey
por: Abdullah, Abdulhady Abas, et al.
Publicado: (2025)

Non-instructional Fine-tuning: Enabling Instruction-Following Capabilities in Pre-trained Language Models without Instruction-Following Data
por: Xie, Juncheng, et al.
Publicado: (2024)

IWISDM: Assessing instruction following in multimodal models at scale
por: Lei, Xiaoxuan, et al.
Publicado: (2024)

How does fine-tuning improve sensorimotor representations in large language models?
por: Wu, Minghua, et al.
Publicado: (2026)

InstructAudio: Unified speech and music generation with natural language instruction
por: Qiang, Chunyu, et al.
Publicado: (2025)

EAA: Automating materials characterization with vision language model agents
por: Du, Ming, et al.
Publicado: (2026)

MURMUR: Using cross-user chatter to break collaborative language agents in groups
por: Patlan, Atharv Singh, et al.
Publicado: (2025)

RoboGPT: an intelligent agent of making embodied long-term decisions for daily instruction tasks
por: Chen, Yaran, et al.
Publicado: (2023)

MindScope: Exploring cognitive biases in large language models through Multi-Agent Systems
por: Xie, Zhentao, et al.
Publicado: (2024)

Towards a more efficient bias detection in financial language models
por: Kacem, Firas Hadj, et al.
Publicado: (2026)

Fine-tuning multilingual language models in Twitter/X sentiment analysis: a study on Eastern-European V4 languages
por: Filip, Tomáš, et al.
Publicado: (2024)

Teaching large language models to reason like expert diagnosticians
por: Buckley, Thomas A., et al.
Publicado: (2025)

Why do AI agents communicate in human language?
por: Zhou, Pengcheng, et al.
Publicado: (2025)

Humans overrely on overconfident language models, across languages
por: Rathi, Neil, et al.
Publicado: (2025)

Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models
por: Ma, Olivia, et al.
Publicado: (2024)

Fine-tuning of lightweight large language models for sentiment classification on heterogeneous financial textual data
por: Amorin, Alvaro Paredes, et al.
Publicado: (2025)

Open-weight genome language model safeguards: Assessing robustness via adversarial fine-tuning
por: Black, James R. M., et al.
Publicado: (2025)

A closer look at how large language models trust humans: patterns and biases
por: Lerman, Valeria, et al.
Publicado: (2025)

Using large language models to produce literature reviews: Usages and systematic biases of microphysics parametrizations in 2699 publications
por: Zhang, Tianhang, et al.
Publicado: (2025)

Fine-tuning large language models for domain adaptation: Exploration of training strategies, scaling, model merging and synergistic capabilities
por: Lu, Wei, et al.
Publicado: (2024)

Causal state binding predicts action control in language agents
por: Jia, Xiao
Publicado: (2026)

Recursive language models for jailbreak detection: a procedural defense for tool-augmented agents
por: Shavit, Doron
Publicado: (2026)

Metaphor identification using large language models: A comparison of RAG, prompt engineering, and fine-tuning
por: Fuoli, Matteo, et al.
Publicado: (2025)

Functional Subspace, where language models can use vector algebra to solve problems
por: Lee, Jung H., et al.
Publicado: (2026)

Post-training makes large language models less human-like
por: Binz, Marcel, et al.
Publicado: (2026)

Beyond the model: Key differentiators in large language models and multi-agent services
por: Goyal, Muskaan, et al.
Publicado: (2025)

FASTNav: Fine-tuned Adaptive Small-language-models Trained for Multi-point Robot Navigation
por: Chen, Yuxuan, et al.
Publicado: (2024)

An empirical study of LoRA-based fine-tuning of large language models for automated test case generation
por: Moradi, Milad, et al.
Publicado: (2026)

Human-like object concept representations emerge naturally in multimodal large language models
por: Du, Changde, et al.
Publicado: (2024)

Do language models practice what they preach? Examining language ideologies about gendered language reform encoded in LLMs
por: Watson, Julia, et al.
Publicado: (2024)