Copertina

Agent Fine-tuning through Distillation for Domain-specific LLMs in Microdomains

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Xue, Yawen, Tsunokake, Masaya, Koreeda, Yuta, Amin, Ekant Muljibhai, Sumiyoshi, Takashi, Sogawa, Yasuhiro
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Computation and Language
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Is Micro Domain-Adaptive Pre-Training Effective for Real-World Operations? Multi-Step Evaluation Reveals Potential and Bottlenecks
di: Tsunokake, Masaya, et al.
Pubblicazione: (2026)

Building Domain-Specific Small Language Models via Guided Data Generation
di: Kumar, Aman, et al.
Pubblicazione: (2025)

Web Page Classification using LLMs for Crawling Support
di: Sasazawa, Yuichi, et al.
Pubblicazione: (2025)

Fine-tuning and Utilization Methods of Domain-specific LLMs
di: Jeong, Cheonsu
Pubblicazione: (2024)

GFlowNet Fine-tuning for Diverse Correct Solutions in Mathematical Reasoning Tasks
di: Takase, Ryoichi, et al.
Pubblicazione: (2024)

Acquiring Bidirectionality via Large and Small Language Models
di: Goto, Takumi, et al.
Pubblicazione: (2024)

Memento: Fine-tuning LLM Agents without Fine-tuning LLMs
di: Zhou, Huichi, et al.
Pubblicazione: (2025)

KaFT: Knowledge-aware Fine-tuning for Boosting LLMs' Domain-specific Question-Answering Performance
di: Zhong, Qihuang, et al.
Pubblicazione: (2025)

Fine-tuning Large Language Models for Domain-specific Machine Translation
di: Zheng, Jiawei, et al.
Pubblicazione: (2024)

Layout Generation Agents with Large Language Models
di: Sasazawa, Yuichi, et al.
Pubblicazione: (2024)

Affordably Fine-tuned LLMs Provide Better Answers to Course-specific MCQs
di: Raimondi, Bianca, et al.
Pubblicazione: (2025)

Breaking Free Transformer Models: Task-specific Context Attribution Promises Improved Generalizability Without Fine-tuning Pre-trained LLMs
di: Tytarenko, Stepan, et al.
Pubblicazione: (2024)

Be Careful When Fine-tuning On Open-Source LLMs: Your Fine-tuning Data Could Be Secretly Stolen!
di: Zhang, Zhexin, et al.
Pubblicazione: (2025)

Relational Knowledge Distillation Using Fine-tuned Function Vectors
di: Kang, Andrea, et al.
Pubblicazione: (2026)

Large Language Model for Multi-Domain Translation: Benchmarking and Domain CoT Fine-tuning
di: Hu, Tianxiang, et al.
Pubblicazione: (2024)

Exploring Intrinsic Language-specific Subspaces in Fine-tuning Multilingual Neural Machine Translation
di: Cao, Zhe, et al.
Pubblicazione: (2024)

Topic Modeling with Fine-tuning LLMs and Bag of Sentences
di: Schneider, Johannes
Pubblicazione: (2024)

Beyond Fine-tuning: Unleashing the Potential of Continuous Pretraining for Clinical LLMs
di: Christophe, Clément, et al.
Pubblicazione: (2024)

Polarization of Autonomous Generative AI Agents Under Echo Chambers
di: Ohagi, Masaya
Pubblicazione: (2024)

Dynamic Self-Distillation via Previous Mini-batches for Fine-tuning Small Language Models
di: Fu, Yao, et al.
Pubblicazione: (2024)

Can Pre-training Indicators Reliably Predict Fine-tuning Outcomes of LLMs?
di: Zeng, Hansi, et al.
Pubblicazione: (2025)

Aloe: A Family of Fine-tuned Open Healthcare LLMs
di: Gururajan, Ashwin Kumar, et al.
Pubblicazione: (2024)

Are LLMs Effective Backbones for Fine-tuning? An Experimental Investigation of Supervised LLMs on Chinese Short Text Matching
di: Liu, Shulin, et al.
Pubblicazione: (2024)

Question-Answering System for Bangla: Fine-tuning BERT-Bangla for a Closed Domain
di: Roy, Subal Chandra, et al.
Pubblicazione: (2024)

RLSF: Fine-tuning LLMs via Symbolic Feedback
di: Jha, Piyush, et al.
Pubblicazione: (2024)

Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning
di: Zhong, Qihuang, et al.
Pubblicazione: (2025)

From 'Showgirls' to 'Performers': Fine-tuning with Gender-inclusive Language for Bias Reduction in LLMs
di: Bartl, Marion, et al.
Pubblicazione: (2024)

Do Domain-specific Experts exist in MoE-based LLMs?
di: Do, Giang, et al.
Pubblicazione: (2026)

ITERTL: An Iterative Framework for Fine-tuning LLMs for RTL Code Generation
di: Wu, Peiyang, et al.
Pubblicazione: (2024)

Fine-tuning Strategies for Domain Specific Question Answering under Low Annotation Budget Constraints
di: Guo, Kunpeng, et al.
Pubblicazione: (2024)

Dental Severity Assessment through Few-shot Learning and SBERT Fine-tuning
di: Dehghani, Mohammad
Pubblicazione: (2024)

Targeted Efficient Fine-tuning: Optimizing Parameter Updates with Data-Driven Sample Selection
di: Dong, Ming, et al.
Pubblicazione: (2024)

Towards Objective Fine-tuning: How LLMs' Prior Knowledge Causes Potential Poor Calibration?
di: Wang, Ziming, et al.
Pubblicazione: (2025)

ObfuscaTune: Obfuscated Offsite Fine-tuning and Inference of Proprietary LLMs on Private Datasets
di: Frikha, Ahmed, et al.
Pubblicazione: (2024)

Can We Use Probing to Better Understand Fine-tuning and Knowledge Distillation of the BERT NLU?
di: Hościłowicz, Jakub, et al.
Pubblicazione: (2023)

Agent Data Protocol: Unifying Datasets for Diverse, Effective Fine-tuning of LLM Agents
di: Song, Yueqi, et al.
Pubblicazione: (2025)

Can Visual Encoder Learn to See Arrows?
di: Terashita, Naoyuki, et al.
Pubblicazione: (2025)

TagRouter: Learning Route to LLMs through Tags for Open-Domain Text Generation Tasks
di: Chen, Zhou, et al.
Pubblicazione: (2025)

Towards Context-Robust LLMs: A Gated Representation Fine-tuning Approach
di: Zeng, Shenglai, et al.
Pubblicazione: (2025)

Robust and Efficient Fine-tuning of LLMs with Bayesian Reparameterization of Low-Rank Adaptation
di: Sengupta, Ayan, et al.
Pubblicazione: (2024)