matlok
's Collections
Papers - Fine-tuning
updated
Unleashing the Power of Pre-trained Language Models for Offline
Reinforcement Learning
Paper
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2310.20587
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Published
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16
SELF: Language-Driven Self-Evolution for Large Language Model
Paper
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2310.00533
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Published
•
2
QLoRA: Efficient Finetuning of Quantized LLMs
Paper
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2305.14314
•
Published
•
46
QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models
Paper
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2309.14717
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Published
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44
Table-GPT: Table-tuned GPT for Diverse Table Tasks
Paper
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2310.09263
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Published
•
39
Self-Play Fine-Tuning Converts Weak Language Models to Strong Language
Models
Paper
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2401.01335
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Published
•
64
LLM2LLM: Boosting LLMs with Novel Iterative Data Enhancement
Paper
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2403.15042
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Published
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25
Toolformer: Language Models Can Teach Themselves to Use Tools
Paper
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2302.04761
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Published
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11
The Unreasonable Ineffectiveness of the Deeper Layers
Paper
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2403.17887
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Published
•
78
InternLM2 Technical Report
Paper
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2403.17297
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Published
•
30
LIMA: Less Is More for Alignment
Paper
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2305.11206
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Published
•
21
Direct Preference Optimization: Your Language Model is Secretly a Reward
Model
Paper
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2305.18290
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Published
•
49
sDPO: Don't Use Your Data All at Once
Paper
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2403.19270
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Published
•
40
Deep reinforcement learning from human preferences
Paper
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1706.03741
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Published
•
3
Fine-tuning Language Models for Factuality
Paper
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2311.08401
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Published
•
28
An Emulator for Fine-Tuning Large Language Models using Small Language
Models
Paper
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2310.12962
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Published
•
14
Gecko: Versatile Text Embeddings Distilled from Large Language Models
Paper
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2403.20327
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Published
•
47
Model Stock: All we need is just a few fine-tuned models
Paper
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2403.19522
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Published
•
10
ReFT: Representation Finetuning for Language Models
Paper
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2404.03592
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Published
•
91
UltraFeedback: Boosting Language Models with High-quality Feedback
Paper
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2310.01377
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Published
•
5
RL for Consistency Models: Faster Reward Guided Text-to-Image Generation
Paper
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2404.03673
•
Published
•
14
Stream of Search (SoS): Learning to Search in Language
Paper
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2404.03683
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Published
•
29
CantTalkAboutThis: Aligning Language Models to Stay on Topic in
Dialogues
Paper
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2404.03820
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Published
•
24
ORPO: Monolithic Preference Optimization without Reference Model
Paper
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2403.07691
•
Published
•
63
Learn Your Reference Model for Real Good Alignment
Paper
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2404.09656
•
Published
•
82
Fine-Tuning Enhances Existing Mechanisms: A Case Study on Entity
Tracking
Paper
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2402.14811
•
Published
•
4
Comprehensive Survey of Model Compression and Speed up for Vision
Transformers
Paper
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2404.10407
•
Published
•
1
OpenBezoar: Small, Cost-Effective and Open Models Trained on Mixes of
Instruction Data
Paper
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2404.12195
•
Published
•
11
Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning
Paper
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2303.15647
•
Published
•
4
Hyper-X: A Unified Hypernetwork for Multi-Task Multilingual Transfer
Paper
•
2205.12148
•
Published
•
2
Beyond the Turn-Based Game: Enabling Real-Time Conversations with Duplex
Models
Paper
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2406.15718
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Published
•
14
In-context Vectors: Making In Context Learning More Effective and
Controllable Through Latent Space Steering
Paper
•
2311.06668
•
Published
•
5
SpreadsheetLLM: Encoding Spreadsheets for Large Language Models
Paper
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2407.09025
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Published
•
129
LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models
Paper
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2403.13372
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Published
•
62
Adapting While Learning: Grounding LLMs for Scientific Problems with
Intelligent Tool Usage Adaptation
Paper
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2411.00412
•
Published
•
9
CLEAR: Character Unlearning in Textual and Visual Modalities
Paper
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2410.18057
•
Published
•
200
LoRA vs Full Fine-tuning: An Illusion of Equivalence
Paper
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2410.21228
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Published
•
2
Cut Your Losses in Large-Vocabulary Language Models
Paper
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2411.09009
•
Published
•
43
LLaMA-Mesh: Unifying 3D Mesh Generation with Language Models
Paper
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2411.09595
•
Published
•
71
No More Adam: Learning Rate Scaling at Initialization is All You Need
Paper
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2412.11768
•
Published
•
41