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Fine-Tuning
704 directly classified papers
Papers per year
2014: 1
2016: 1
2017: 1
2018: 5
2019: 17
2020: 38
2021: 42
2022: 62
2023: 84
2024: 201
2025: 251
2026: 1
Papers
A Critical Evaluation of AI Feedback for Aligning Large Language Models
NIPS 2024
Hints-In-Browser: Benchmarking Language Models for Programming Feedback Generation
NIPS 2024
Boundary Matters: A Bi-Level Active Finetuning Method
NIPS 2024
AttnDreamBooth: Towards Text-Aligned Personalized Text-to-Image Generation
NIPS 2024
The Art of Saying No: Contextual Noncompliance in Language Models
NIPS 2024
Uncertainty-aware Fine-tuning of Segmentation Foundation Models
NIPS 2024
Instruction Tuning Large Language Models to Understand Electronic Health Records
NIPS 2024
Recursive Introspection: Teaching Language Model Agents How to Self-Improve
NIPS 2024
DreamClear: High-Capacity Real-World Image Restoration with Privacy-Safe Dataset Curation
NIPS 2024
LISA: Layerwise Importance Sampling for Memory-Efficient Large Language Model Fine-Tuning
NIPS 2024
Vision-Language Models are Strong Noisy Label Detectors
NIPS 2024
CLAVE: An Adaptive Framework for Evaluating Values of LLM Generated Responses
NIPS 2024
Synthesize, Partition, then Adapt: Eliciting Diverse Samples from Foundation Models
NIPS 2024
S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity
NIPS 2024
Repurposing Language Models into Embedding Models: Finding the Compute-Optimal Recipe
NIPS 2024
Alignment for Honesty
NIPS 2024
ReFT: Representation Finetuning for Language Models
NIPS 2024
Stress-Testing Capability Elicitation With Password-Locked Models
NIPS 2024
Instruction Tuning With Loss Over Instructions
NIPS 2024
Structured Unrestricted-Rank Matrices for Parameter Efficient Finetuning
NIPS 2024
AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment
NIPS 2024
Probabilistic Federated Prompt-Tuning with Non-IID and Imbalanced Data
NIPS 2024
TuneTables: Context Optimization for Scalable Prior-Data Fitted Networks
NIPS 2024
SmallToLarge (S2L): Scalable Data Selection for Fine-tuning Large Language Models by Summarizing Training Trajectories of Small Models
NIPS 2024
Cross-model Control: Improving Multiple Large Language Models in One-time Training
NIPS 2024
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