In-Context Learning and Fine-Tuning GPT for Argument Mining
Fuente:
arXiv
Saved in:
| Main Authors: | Cabessa, Jérémie, Hernault, Hugo, Mushtaq, Umer |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Limited Generalizability in Argument Mining: State-Of-The-Art Models Learn Datasets, Not Arguments
by: Feger, Marc, et al.
Published: (2025)
by: Feger, Marc, et al.
Published: (2025)
Deeper Insights Without Updates: The Power of In-Context Learning Over Fine-Tuning
by: Yin, Qingyu, et al.
Published: (2024)
by: Yin, Qingyu, et al.
Published: (2024)
ScoNe: Benchmarking Negation Reasoning in Language Models With Fine-Tuning and In-Context Learning
by: She, Jingyuan Selena, et al.
Published: (2023)
by: She, Jingyuan Selena, et al.
Published: (2023)
GPT Meets Graphs and KAN Splines: Testing Novel Frameworks on Multitask Fine-Tuned GPT-2 with LoRA
by: Bo, Gabriel, et al.
Published: (2025)
by: Bo, Gabriel, et al.
Published: (2025)
Fine-Tuning Without Forgetting In-Context Learning: A Theoretical Analysis of Linear Attention Models
by: Lee, Chungpa, et al.
Published: (2026)
by: Lee, Chungpa, et al.
Published: (2026)
In-Context Fine-Tuning for Time-Series Foundation Models
by: Das, Abhimanyu, et al.
Published: (2024)
by: Das, Abhimanyu, et al.
Published: (2024)
Filtering Beats Fine Tuning: A Bayesian Kalman View of In Context Learning in LLMs
by: Kiruluta, Andrew
Published: (2026)
by: Kiruluta, Andrew
Published: (2026)
MMICT: Boosting Multi-Modal Fine-Tuning with In-Context Examples
by: Chen, Tao, et al.
Published: (2023)
by: Chen, Tao, et al.
Published: (2023)
OpenDebateEvidence: A Massive-Scale Argument Mining and Summarization Dataset
by: Roush, Allen, et al.
Published: (2024)
by: Roush, Allen, et al.
Published: (2024)
Anchored Supervised Fine-Tuning
by: Zhu, He, et al.
Published: (2025)
by: Zhu, He, et al.
Published: (2025)
DrugImproverGPT: A Large Language Model for Drug Optimization with Fine-Tuning via Structured Policy Optimization
by: Liu, Xuefeng, et al.
Published: (2025)
by: Liu, Xuefeng, et al.
Published: (2025)
Aligning Backchannel and Dialogue Context Representations via Contrastive LLM Fine-Tuning
by: Qian, Livia, et al.
Published: (2026)
by: Qian, Livia, et al.
Published: (2026)
Prompt Tuning for Natural Language to SQL with Embedding Fine-Tuning and RAG
by: Jang, Jisoo, et al.
Published: (2025)
by: Jang, Jisoo, et al.
Published: (2025)
GPT vs RETRO: Exploring the Intersection of Retrieval and Parameter-Efficient Fine-Tuning
by: Ficek, Aleksander, et al.
Published: (2024)
by: Ficek, Aleksander, et al.
Published: (2024)
Token Buncher: Shielding LLMs from Harmful Reinforcement Learning Fine-Tuning
by: Feng, Weitao, et al.
Published: (2025)
by: Feng, Weitao, et al.
Published: (2025)
Supervised Fine-Tuning as Inverse Reinforcement Learning
by: Sun, Hao
Published: (2024)
by: Sun, Hao
Published: (2024)
QEFT: Quantization for Efficient Fine-Tuning of LLMs
by: Lee, Changhun, et al.
Published: (2024)
by: Lee, Changhun, et al.
Published: (2024)
Fine Tuning Methods for Low-resource Languages
by: Bakkenes, Tim, et al.
Published: (2025)
by: Bakkenes, Tim, et al.
Published: (2025)
Energy and Carbon Considerations of Fine-Tuning BERT
by: Wang, Xiaorong, et al.
Published: (2023)
by: Wang, Xiaorong, et al.
Published: (2023)
UFT: Unifying Supervised and Reinforcement Fine-Tuning
by: Liu, Mingyang, et al.
Published: (2025)
by: Liu, Mingyang, et al.
Published: (2025)
Context Tuning for In-Context Optimization
by: Lu, Jack, et al.
Published: (2025)
by: Lu, Jack, et al.
Published: (2025)
Deep Learning for Medical Text Processing: BERT Model Fine-Tuning and Comparative Study
by: Hu, Jiacheng, et al.
Published: (2024)
by: Hu, Jiacheng, et al.
Published: (2024)
Learning to Stay Safe: Adaptive Regularization Against Safety Degradation during Fine-Tuning
by: Goel, Jyotin, et al.
Published: (2026)
by: Goel, Jyotin, et al.
Published: (2026)
Pre-training LLM without Learning Rate Decay Enhances Supervised Fine-Tuning
by: Yano, Kazuki, et al.
Published: (2026)
by: Yano, Kazuki, et al.
Published: (2026)
Fine-Tuning Language Models with Reward Learning on Policy
by: Lang, Hao, et al.
Published: (2024)
by: Lang, Hao, et al.
Published: (2024)
Teaching LLMs How to Learn with Contextual Fine-Tuning
by: Choi, Younwoo, et al.
Published: (2025)
by: Choi, Younwoo, et al.
Published: (2025)
Causal Fine-Tuning under Latent Confounded Shift
by: Yu, Jialin, et al.
Published: (2024)
by: Yu, Jialin, et al.
Published: (2024)
Parameter-Efficient Fine-Tuning via Circular Convolution
by: Chen, Aochuan, et al.
Published: (2024)
by: Chen, Aochuan, et al.
Published: (2024)
Parameter-Efficient Fine-Tuning of State Space Models
by: Galim, Kevin, et al.
Published: (2024)
by: Galim, Kevin, et al.
Published: (2024)
Dissecting Fine-Tuning Unlearning in Large Language Models
by: Hong, Yihuai, et al.
Published: (2024)
by: Hong, Yihuai, et al.
Published: (2024)
Zhyper: Factorized Hypernetworks for Conditioned LLM Fine-Tuning
by: Abdalla, M. H. I., et al.
Published: (2025)
by: Abdalla, M. H. I., et al.
Published: (2025)
LIFT: Last-Mile Fine-Tuning for Table Explicitation
by: Khaitan, Divij, et al.
Published: (2026)
by: Khaitan, Divij, et al.
Published: (2026)
Fine-Tuning Language Models with Just Forward Passes
by: Malladi, Sadhika, et al.
Published: (2023)
by: Malladi, Sadhika, et al.
Published: (2023)
Reinforcement Learning without Human Feedback for Last Mile Fine-Tuning of Large Language Models
by: Solway, Alec
Published: (2024)
by: Solway, Alec
Published: (2024)
Active Learning for Identifying Disaster-Related Tweets: A Comparison with Keyword Filtering and Generic Fine-Tuning
by: Hanny, David, et al.
Published: (2024)
by: Hanny, David, et al.
Published: (2024)
TeleOracle: Fine-Tuned Retrieval-Augmented Generation with Long-Context Support for Network
by: Alabbasi, Nouf, et al.
Published: (2024)
by: Alabbasi, Nouf, et al.
Published: (2024)
On the Loss of Context-awareness in General Instruction Fine-tuning
by: Wang, Yihan, et al.
Published: (2024)
by: Wang, Yihan, et al.
Published: (2024)
Personalized Collaborative Fine-Tuning for On-Device Large Language Models
by: Wagner, Nicolas, et al.
Published: (2024)
by: Wagner, Nicolas, et al.
Published: (2024)
Tracking Universal Features Through Fine-Tuning and Model Merging
by: Horn, Niels, et al.
Published: (2024)
by: Horn, Niels, et al.
Published: (2024)
PocketLLM: Enabling On-Device Fine-Tuning for Personalized LLMs
by: Peng, Dan, et al.
Published: (2024)
by: Peng, Dan, et al.
Published: (2024)
Similar Items
-
Limited Generalizability in Argument Mining: State-Of-The-Art Models Learn Datasets, Not Arguments
by: Feger, Marc, et al.
Published: (2025) -
Deeper Insights Without Updates: The Power of In-Context Learning Over Fine-Tuning
by: Yin, Qingyu, et al.
Published: (2024) -
ScoNe: Benchmarking Negation Reasoning in Language Models With Fine-Tuning and In-Context Learning
by: She, Jingyuan Selena, et al.
Published: (2023) -
GPT Meets Graphs and KAN Splines: Testing Novel Frameworks on Multitask Fine-Tuned GPT-2 with LoRA
by: Bo, Gabriel, et al.
Published: (2025) -
Fine-Tuning Without Forgetting In-Context Learning: A Theoretical Analysis of Linear Attention Models
by: Lee, Chungpa, et al.
Published: (2026)