Saved in:
| Main Authors: | Han, Yifu, Zhang, Geo |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.21560 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Zero-shot cross-lingual transfer in instruction tuning of large language models
by: Chirkova, Nadezhda, et al.
Published: (2024)
by: Chirkova, Nadezhda, et al.
Published: (2024)
WizardLM: Empowering large pre-trained language models to follow complex instructions
by: Xu, Can, et al.
Published: (2023)
by: Xu, Can, et al.
Published: (2023)
How does fine-tuning improve sensorimotor representations in large language models?
by: Wu, Minghua, et al.
Published: (2026)
by: Wu, Minghua, et al.
Published: (2026)
To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning
by: Sprague, Zayne, et al.
Published: (2024)
by: Sprague, Zayne, et al.
Published: (2024)
Evolution of meta's llama models and parameter-efficient fine-tuning of large language models: a survey
by: Abdullah, Abdulhady Abas, et al.
Published: (2025)
by: Abdullah, Abdulhady Abas, et al.
Published: (2025)
Retrieval-augmented reasoning with lean language models
by: Chan, Ryan Sze-Yin, et al.
Published: (2025)
by: Chan, Ryan Sze-Yin, et al.
Published: (2025)
Metaphor identification using large language models: A comparison of RAG, prompt engineering, and fine-tuning
by: Fuoli, Matteo, et al.
Published: (2025)
by: Fuoli, Matteo, et al.
Published: (2025)
Slm-mux: Orchestrating small language models for reasoning
by: Wang, Chenyu, et al.
Published: (2025)
by: Wang, Chenyu, et al.
Published: (2025)
Response: Emergent analogical reasoning in large language models
by: Hodel, Damian, et al.
Published: (2023)
by: Hodel, Damian, et al.
Published: (2023)
Code-enabled language models can outperform reasoning models on diverse tasks
by: Zhang, Cedegao E., et al.
Published: (2025)
by: Zhang, Cedegao E., et al.
Published: (2025)
MathDivide: Improved mathematical reasoning by large language models
by: Srivastava, Saksham Sahai, et al.
Published: (2024)
by: Srivastava, Saksham Sahai, et al.
Published: (2024)
Do LLMs estimate uncertainty well in instruction-following?
by: Heo, Juyeon, et al.
Published: (2024)
by: Heo, Juyeon, et al.
Published: (2024)
Do LLMs "know" internally when they follow instructions?
by: Heo, Juyeon, et al.
Published: (2024)
by: Heo, Juyeon, et al.
Published: (2024)
ZNO-Eval: Benchmarking reasoning capabilities of large language models in Ukrainian
by: Syromiatnikov, Mykyta, et al.
Published: (2025)
by: Syromiatnikov, Mykyta, et al.
Published: (2025)
Enhancing reasoning accuracy in large language models during inference time
by: Sharma, Vinay, et al.
Published: (2026)
by: Sharma, Vinay, et al.
Published: (2026)
Large language models show fragile cognitive reasoning about human emotions
by: Bhattacharyya, Sree, et al.
Published: (2025)
by: Bhattacharyya, Sree, et al.
Published: (2025)
ThoughtSource: A central hub for large language model reasoning data
by: Ott, Simon, et al.
Published: (2023)
by: Ott, Simon, et al.
Published: (2023)
Superhuman performance of a large language model on the reasoning tasks of a physician
by: Brodeur, Peter G., et al.
Published: (2024)
by: Brodeur, Peter G., et al.
Published: (2024)
Retention analysis of edited knowledge after fine-tuning
by: Wen, Fufang, et al.
Published: (2025)
by: Wen, Fufang, et al.
Published: (2025)
Investigating the interaction of linguistic and mathematical reasoning in language models using multilingual number puzzles
by: Bhattacharya, Antara Raaghavi, et al.
Published: (2025)
by: Bhattacharya, Antara Raaghavi, et al.
Published: (2025)
Social preferences with unstable interactive reasoning: Large language models in economic trust games
by: Jiamin, Ou, et al.
Published: (2025)
by: Jiamin, Ou, et al.
Published: (2025)
Evidence from counterfactual tasks supports emergent analogical reasoning in large language models
by: Webb, Taylor, et al.
Published: (2024)
by: Webb, Taylor, et al.
Published: (2024)
ClinicalGPT-R1: Pushing reasoning capability of generalist disease diagnosis with large language model
by: Lan, Wuyang, et al.
Published: (2025)
by: Lan, Wuyang, et al.
Published: (2025)
BLEUBERI: BLEU is a surprisingly effective reward for instruction following
by: Chang, Yapei, et al.
Published: (2025)
by: Chang, Yapei, et al.
Published: (2025)
Response-free item difficulty modelling for multiple-choice items with fine-tuned transformers: Component-wise representation and multi-task learning
by: Netík, Jan, et al.
Published: (2026)
by: Netík, Jan, et al.
Published: (2026)
Rethinking harmless refusals when fine-tuning foundation models
by: Pop, Florin, et al.
Published: (2024)
by: Pop, Florin, et al.
Published: (2024)
Minor SFT loss for LLM fine-tune to increase performance and reduce model deviation
by: Xie, Shiming, et al.
Published: (2024)
by: Xie, Shiming, et al.
Published: (2024)
OpenMedLM: Prompt engineering can out-perform fine-tuning in medical question-answering with open-source large language models
by: Maharjan, Jenish, et al.
Published: (2024)
by: Maharjan, Jenish, et al.
Published: (2024)
CARE: Cognitive-reasoning Augmented Reinforcement for Emotional Support Conversation
by: Zhu, Jie, et al.
Published: (2025)
by: Zhu, Jie, et al.
Published: (2025)
Multi-step retrieval and reasoning improves radiology question answering with large language models
by: Wind, Sebastian, et al.
Published: (2025)
by: Wind, Sebastian, et al.
Published: (2025)
Using Optimal Transport as Alignment Objective for fine-tuning Multilingual Contextualized Embeddings
by: Alqahtani, Sawsan, et al.
Published: (2021)
by: Alqahtani, Sawsan, et al.
Published: (2021)
ReadCtrl: Personalizing text generation with readability-controlled instruction learning
by: Tran, Hieu, et al.
Published: (2024)
by: Tran, Hieu, et al.
Published: (2024)
MedReadCtrl: Personalizing medical text generation with readability-controlled instruction learning
by: Tran, Hieu, et al.
Published: (2025)
by: Tran, Hieu, et al.
Published: (2025)
Panacea: Mitigating Harmful Fine-tuning for Large Language Models via Post-fine-tuning Perturbation
by: Wang, Yibo, et al.
Published: (2025)
by: Wang, Yibo, et al.
Published: (2025)
Fine-tuning multilingual language models in Twitter/X sentiment analysis: a study on Eastern-European V4 languages
by: Filip, Tomáš, et al.
Published: (2024)
by: Filip, Tomáš, et al.
Published: (2024)
Enabling robots to follow abstract instructions and complete complex dynamic tasks
by: Mon-Williams, Ruaridh, et al.
Published: (2024)
by: Mon-Williams, Ruaridh, et al.
Published: (2024)
RandLoRA: Full-rank parameter-efficient fine-tuning of large models
by: Albert, Paul, et al.
Published: (2025)
by: Albert, Paul, et al.
Published: (2025)
InstructAudio: Unified speech and music generation with natural language instruction
by: Qiang, Chunyu, et al.
Published: (2025)
by: Qiang, Chunyu, et al.
Published: (2025)
Fine-tuning of lightweight large language models for sentiment classification on heterogeneous financial textual data
by: Amorin, Alvaro Paredes, et al.
Published: (2025)
by: Amorin, Alvaro Paredes, et al.
Published: (2025)
Automated stereotactic radiosurgery planning using a human-in-the-loop reasoning large language model agent
by: Nusrat, Humza, et al.
Published: (2025)
by: Nusrat, Humza, et al.
Published: (2025)
Similar Items
-
Zero-shot cross-lingual transfer in instruction tuning of large language models
by: Chirkova, Nadezhda, et al.
Published: (2024) -
WizardLM: Empowering large pre-trained language models to follow complex instructions
by: Xu, Can, et al.
Published: (2023) -
How does fine-tuning improve sensorimotor representations in large language models?
by: Wu, Minghua, et al.
Published: (2026) -
To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning
by: Sprague, Zayne, et al.
Published: (2024) -
Evolution of meta's llama models and parameter-efficient fine-tuning of large language models: a survey
by: Abdullah, Abdulhady Abas, et al.
Published: (2025)