Can Smaller LLMs do better? Unlocking Cross-Domain Potential through Parameter-Efficient Fine-Tuning for Text Summarization
Fuente:
arXiv
Saved in:
| Main Authors: | Afzal, Anum, Kumawat, Mehul, Matthes, Florian |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
AdaptEval: Evaluating Large Language Models on Domain Adaptation for Text Summarization
by: Afzal, Anum, et al.
Published: (2024)
by: Afzal, Anum, et al.
Published: (2024)
FActBench: A Benchmark for Fine-grained Automatic Evaluation of LLM-Generated Text in the Medical Domain
by: Afzal, Anum, et al.
Published: (2025)
by: Afzal, Anum, et al.
Published: (2025)
JaccDiv: A Metric and Benchmark for Quantifying Diversity of Generated Marketing Text in the Music Industry
by: Afzal, Anum, et al.
Published: (2025)
by: Afzal, Anum, et al.
Published: (2025)
Towards Optimizing and Evaluating a Retrieval Augmented QA Chatbot using LLMs with Human in the Loop
by: Afzal, Anum, et al.
Published: (2024)
by: Afzal, Anum, et al.
Published: (2024)
Knowing Before Saying: LLM Representations Encode Information About Chain-of-Thought Success Before Completion
by: Afzal, Anum, et al.
Published: (2025)
by: Afzal, Anum, et al.
Published: (2025)
Parameter-Efficient Fine-Tuning for Medical Text Summarization: A Comparative Study of Lora, Prompt Tuning, and Full Fine-Tuning
by: Shernazarov, Ulugbek, et al.
Published: (2026)
by: Shernazarov, Ulugbek, et al.
Published: (2026)
Real-Time Generation of Game Video Commentary with Multimodal LLMs: Pause-Aware Decoding Approaches
by: Afzal, Anum, et al.
Published: (2026)
by: Afzal, Anum, et al.
Published: (2026)
Unlocking Parameter-Efficient Fine-Tuning for Low-Resource Language Translation
by: Su, Tong, et al.
Published: (2024)
by: Su, Tong, et al.
Published: (2024)
Enhancing News Summarization with ELearnFit through Efficient In-Context Learning and Efficient Fine-Tuning
by: Guan, Che, et al.
Published: (2024)
by: Guan, Che, et al.
Published: (2024)
Can LLMs Rank the Harmfulness of Smaller LLMs? We are Not There Yet
by: Atil, Berk, et al.
Published: (2025)
by: Atil, Berk, et al.
Published: (2025)
1-Diffractor: Efficient and Utility-Preserving Text Obfuscation Leveraging Word-Level Metric Differential Privacy
by: Meisenbacher, Stephen, et al.
Published: (2024)
by: Meisenbacher, Stephen, et al.
Published: (2024)
With Privacy, Size Matters: On the Importance of Dataset Size in Differentially Private Text Rewriting
by: Meisenbacher, Stephen, et al.
Published: (2025)
by: Meisenbacher, Stephen, et al.
Published: (2025)
Parameter-Efficient Fine-Tuning of LLaMA for the Clinical Domain
by: Gema, Aryo Pradipta, et al.
Published: (2023)
by: Gema, Aryo Pradipta, et al.
Published: (2023)
Thinking Outside of the Differential Privacy Box: A Case Study in Text Privatization with Language Model Prompting
by: Meisenbacher, Stephen, et al.
Published: (2024)
by: Meisenbacher, Stephen, et al.
Published: (2024)
Fine-Tuned Language Models for Domain-Specific Summarization and Tagging
by: Wang, Jun, et al.
Published: (2025)
by: Wang, Jun, et al.
Published: (2025)
Just Rewrite It Again: A Post-Processing Method for Enhanced Semantic Similarity and Privacy Preservation of Differentially Private Rewritten Text
by: Meisenbacher, Stephen, et al.
Published: (2024)
by: Meisenbacher, Stephen, et al.
Published: (2024)
A Systematic Exploration of Text Decomposition and Budget Distribution in Differentially Private Text Obfuscation
by: Meisenbacher, Stephen, et al.
Published: (2026)
by: Meisenbacher, Stephen, et al.
Published: (2026)
Selective Fine-Tuning of GPT Architectures for Parameter-Efficient Clinical Text Classification
by: Irany, Fariba Afrin, et al.
Published: (2026)
by: Irany, Fariba Afrin, et al.
Published: (2026)
On the Impact of Noise in Differentially Private Text Rewriting
by: Meisenbacher, Stephen, et al.
Published: (2025)
by: Meisenbacher, Stephen, et al.
Published: (2025)
Parameter-Efficient Fine-Tuning of LLMs with Mixture of Space Experts
by: Zhang, Buze, et al.
Published: (2026)
by: Zhang, Buze, et al.
Published: (2026)
DomainSum: A Hierarchical Benchmark for Fine-Grained Domain Shift in Abstractive Text Summarization
by: Yuan, Haohan, et al.
Published: (2024)
by: Yuan, Haohan, et al.
Published: (2024)
Comparing Knowledge Sources for Open-Domain Scientific Claim Verification
by: Vladika, Juraj, et al.
Published: (2024)
by: Vladika, Juraj, et al.
Published: (2024)
Med42 -- Evaluating Fine-Tuning Strategies for Medical LLMs: Full-Parameter vs. Parameter-Efficient Approaches
by: Christophe, Clément, et al.
Published: (2024)
by: Christophe, Clément, et al.
Published: (2024)
LLMs for Legal Subsumption in German Employment Contracts
by: Wardas, Oliver, et al.
Published: (2025)
by: Wardas, Oliver, et al.
Published: (2025)
Parameter-Efficient Fine-Tuning With Adapters
by: Chen, Keyu, et al.
Published: (2024)
by: Chen, Keyu, et al.
Published: (2024)
A Hybrid Architecture with Efficient Fine Tuning for Abstractive Patent Document Summarization
by: Jayatilleke, Nevidu, et al.
Published: (2025)
by: Jayatilleke, Nevidu, et al.
Published: (2025)
Tiny Titans: Can Smaller Large Language Models Punch Above Their Weight in the Real World for Meeting Summarization?
by: Fu, Xue-Yong, et al.
Published: (2024)
by: Fu, Xue-Yong, et al.
Published: (2024)
Fine-Tuned LLMs Know They Don't Know: A Parameter-Efficient Approach to Recovering Honesty
by: Shi, Zeyu, et al.
Published: (2025)
by: Shi, Zeyu, et al.
Published: (2025)
Improving Text Embeddings for Smaller Language Models Using Contrastive Fine-tuning
by: Ukarapol, Trapoom, et al.
Published: (2024)
by: Ukarapol, Trapoom, et al.
Published: (2024)
Fine-Tuning LLMs for Report Summarization: Analysis on Supervised and Unsupervised Data
by: Rallapalli, Swati, et al.
Published: (2025)
by: Rallapalli, Swati, et al.
Published: (2025)
Supervised Fine-Tuning Needs to Unlock the Potential of Token Priority
by: Shen, Zhanming, et al.
Published: (2026)
by: Shen, Zhanming, et al.
Published: (2026)
Low-Resource Domain Adaptation for Speech LLMs via Text-Only Fine-Tuning
by: Fang, Yangui, et al.
Published: (2025)
by: Fang, Yangui, et al.
Published: (2025)
Beyond QA Pairs: Assessing Parameter-Efficient Fine-Tuning for Fact Embedding in LLMs
by: Ratnakar, Shivam, et al.
Published: (2025)
by: Ratnakar, Shivam, et al.
Published: (2025)
NeuroAda: Activating Each Neuron's Potential for Parameter-Efficient Fine-Tuning
by: Zhang, Zhi, et al.
Published: (2025)
by: Zhang, Zhi, et al.
Published: (2025)
Enhancing Answer Attribution for Faithful Text Generation with Large Language Models
by: Vladika, Juraj, et al.
Published: (2024)
by: Vladika, Juraj, et al.
Published: (2024)
From Drafts to Answers: Unlocking LLM Potential via Aggregation Fine-Tuning
by: Li, Yafu, et al.
Published: (2025)
by: Li, Yafu, et al.
Published: (2025)
SIBO: A Simple Booster for Parameter-Efficient Fine-Tuning
by: Wen, Zhihao, et al.
Published: (2024)
by: Wen, Zhihao, et al.
Published: (2024)
High-Rank Structured Modulation for Parameter-Efficient Fine-Tuning
by: Liu, Yongkang, et al.
Published: (2026)
by: Liu, Yongkang, et al.
Published: (2026)
Prompting and Fine-Tuning of Small LLMs for Length-Controllable Telephone Call Summarization
by: Thulke, David, et al.
Published: (2024)
by: Thulke, David, et al.
Published: (2024)
DP-MLM: Differentially Private Text Rewriting Using Masked Language Models
by: Meisenbacher, Stephen, et al.
Published: (2024)
by: Meisenbacher, Stephen, et al.
Published: (2024)
Similar Items
-
AdaptEval: Evaluating Large Language Models on Domain Adaptation for Text Summarization
by: Afzal, Anum, et al.
Published: (2024) -
FActBench: A Benchmark for Fine-grained Automatic Evaluation of LLM-Generated Text in the Medical Domain
by: Afzal, Anum, et al.
Published: (2025) -
JaccDiv: A Metric and Benchmark for Quantifying Diversity of Generated Marketing Text in the Music Industry
by: Afzal, Anum, et al.
Published: (2025) -
Towards Optimizing and Evaluating a Retrieval Augmented QA Chatbot using LLMs with Human in the Loop
by: Afzal, Anum, et al.
Published: (2024) -
Knowing Before Saying: LLM Representations Encode Information About Chain-of-Thought Success Before Completion
by: Afzal, Anum, et al.
Published: (2025)