Democratizing Reasoning Ability: Tailored Learning from Large Language Model
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2310.13332
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Published
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14
Knowledge distillation
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Teaching Language Models to Self-Improve through Interactive Demonstrations
Paper • 2310.13522 • Published • 11 -
Self-Convinced Prompting: Few-Shot Question Answering with Repeated Introspection
Paper • 2310.05035 • Published • 1 -
Tuna: Instruction Tuning using Feedback from Large Language Models
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Reflection-Tuning: Data Recycling Improves LLM Instruction-Tuning
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SILC: Improving Vision Language Pretraining with Self-Distillation
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Conditional Diffusion Distillation
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AutoMix: Automatically Mixing Language Models
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An Emulator for Fine-Tuning Large Language Models using Small Language Models
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Effective Distillation of Table-based Reasoning Ability from LLMs
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Sci-CoT: Leveraging Large Language Models for Enhanced Knowledge Distillation in Small Models for Scientific QA
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The Consensus Game: Language Model Generation via Equilibrium Search
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CLIN: A Continually Learning Language Agent for Rapid Task Adaptation and Generalization
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DistillSpec: Improving Speculative Decoding via Knowledge Distillation
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Large Language Models Are Also Good Prototypical Commonsense Reasoners
Paper • 2309.13165 • Published • 1 -
DialCoT Meets PPO: Decomposing and Exploring Reasoning Paths in Smaller Language Models
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Fantastic Gains and Where to Find Them: On the Existence and Prospect of General Knowledge Transfer between Any Pretrained Model
Paper • 2310.17653 • Published • 2 -
Compress, Then Prompt: Improving Accuracy-Efficiency Trade-off of LLM Inference with Transferable Prompt
Paper • 2305.11186 • Published • 1 -
Self-slimmed Vision Transformer
Paper • 2111.12624 • Published • 1 -
Commonsense Knowledge Transfer for Pre-trained Language Models
Paper • 2306.02388 • Published • 1 -
Symbolic Knowledge Distillation: from General Language Models to Commonsense Models
Paper • 2110.07178 • Published • 1 -
Snowman: A Million-scale Chinese Commonsense Knowledge Graph Distilled from Foundation Model
Paper • 2306.10241 • Published • 1 -
Distilling Efficient Language-Specific Models for Cross-Lingual Transfer
Paper • 2306.01709 • Published • 1 -
Parameter-Efficient Neural Reranking for Cross-Lingual and Multilingual Retrieval
Paper • 2204.02292 • Published • 1 -
Composable Sparse Fine-Tuning for Cross-Lingual Transfer
Paper • 2110.07560 • Published • 1 -
HARD: Hard Augmentations for Robust Distillation
Paper • 2305.14890 • Published • 1 -
Transfer to a Low-Resource Language via Close Relatives: The Case Study on Faroese
Paper • 2304.08823 • Published • 1 -
Massively Multilingual Lexical Specialization of Multilingual Transformers
Paper • 2208.01018 • Published • 1 -
Robust Active Distillation
Paper • 2210.01213 • Published • 1 -
LTD: Low Temperature Distillation for Robust Adversarial Training
Paper • 2111.02331 • Published • 1 -
Mitigating the Accuracy-Robustness Trade-off via Multi-Teacher Adversarial Distillation
Paper • 2306.16170 • Published • 1 -
Mutual Adversarial Training: Learning together is better than going alone
Paper • 2112.05005 • Published • 1 -
Weight Averaging Improves Knowledge Distillation under Domain Shift
Paper • 2309.11446 • Published • 1 -
Cross-Architecture Knowledge Distillation
Paper • 2207.05273 • Published • 1 -
Cross-Domain Ensemble Distillation for Domain Generalization
Paper • 2211.14058 • Published • 1 -
TransKD: Transformer Knowledge Distillation for Efficient Semantic Segmentation
Paper • 2202.13393 • Published • 1 -
Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes
Paper • 2305.02301 • Published • 2 -
Zephyr: Direct Distillation of LM Alignment
Paper • 2310.16944 • Published • 121 -
Personalised Distillation: Empowering Open-Sourced LLMs with Adaptive Learning for Code Generation
Paper • 2310.18628 • Published • 7 -
TeacherLM: Teaching to Fish Rather Than Giving the Fish, Language Modeling Likewise
Paper • 2310.19019 • Published • 9 -
How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources
Paper • 2306.04751 • Published • 5 -
Small Language Models Improve Giants by Rewriting Their Outputs
Paper • 2305.13514 • Published • 2 -
ICLEF: In-Context Learning with Expert Feedback for Explainable Style Transfer
Paper • 2309.08583 • Published • 1 -
A Survey on Model Compression for Large Language Models
Paper • 2308.07633 • Published • 3 -
Distil-Whisper: Robust Knowledge Distillation via Large-Scale Pseudo Labelling
Paper • 2311.00430 • Published • 57 -
Token-Scaled Logit Distillation for Ternary Weight Generative Language Models
Paper • 2308.06744 • Published • 1 -
Understanding and Improving Knowledge Distillation for Quantization-Aware Training of Large Transformer Encoders
Paper • 2211.11014 • Published • 1 -
Model compression via distillation and quantization
Paper • 1802.05668 • Published • 1 -
Feature Affinity Assisted Knowledge Distillation and Quantization of Deep Neural Networks on Label-Free Data
Paper • 2302.10899 • Published • 1 -
Improving Differentiable Architecture Search via Self-Distillation
Paper • 2302.05629 • Published • 1 -
Co-training and Co-distillation for Quality Improvement and Compression of Language Models
Paper • 2311.02849 • Published • 3 -
Tailoring Self-Rationalizers with Multi-Reward Distillation
Paper • 2311.02805 • Published • 3 -
Can a student Large Language Model perform as well as it's teacher?
Paper • 2310.02421 • Published • 1 -
NetDistiller: Empowering Tiny Deep Learning via In-Situ Distillation
Paper • 2310.19820 • Published • 1 -
Talking Models: Distill Pre-trained Knowledge to Downstream Models via Interactive Communication
Paper • 2310.03188 • Published • 1 -
A Comparative Analysis of Task-Agnostic Distillation Methods for Compressing Transformer Language Models
Paper • 2310.08797 • Published • 1 -
MiniLMv2: Multi-Head Self-Attention Relation Distillation for Compressing Pretrained Transformers
Paper • 2012.15828 • Published • 1 -
Self-Distillation for Further Pre-training of Transformers
Paper • 2210.02871 • Published • 1 -
Class Token and Knowledge Distillation for Multi-head Self-Attention Speaker Verification Systems
Paper • 2111.03842 • Published • 1 -
Multi-Mode Online Knowledge Distillation for Self-Supervised Visual Representation Learning
Paper • 2304.06461 • Published • 1 -
Retrieval-based Knowledge Transfer: An Effective Approach for Extreme Large Language Model Compression
Paper • 2310.15594 • Published • 1 -
MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers
Paper • 2002.10957 • Published • 1 -
UNFUSED: UNsupervised Finetuning Using SElf supervised Distillation
Paper • 2303.05668 • Published • 1 -
One-Step Knowledge Distillation and Fine-Tuning in Using Large Pre-Trained Self-Supervised Learning Models for Speaker Verification
Paper • 2305.17394 • Published • 1 -
BPKD: Boundary Privileged Knowledge Distillation For Semantic Segmentation
Paper • 2306.08075 • Published • 1 -
Prototype-guided Cross-task Knowledge Distillation for Large-scale Models
Paper • 2212.13180 • Published • 1 -
ProKD: An Unsupervised Prototypical Knowledge Distillation Network for Zero-Resource Cross-Lingual Named Entity Recognition
Paper • 2301.08855 • Published • 1 -
DPHuBERT: Joint Distillation and Pruning of Self-Supervised Speech Models
Paper • 2305.17651 • Published • 1 -
Recycle-and-Distill: Universal Compression Strategy for Transformer-based Speech SSL Models with Attention Map Reusing and Masking Distillation
Paper • 2305.11685 • Published • 2 -
Large Language Model Distillation Doesn't Need a Teacher
Paper • 2305.14864 • Published • 2 -
One Student Knows All Experts Know: From Sparse to Dense
Paper • 2201.10890 • Published • 1 -
BD-KD: Balancing the Divergences for Online Knowledge Distillation
Paper • 2212.12965 • Published • 1 -
Rethinking Momentum Knowledge Distillation in Online Continual Learning
Paper • 2309.02870 • Published • 1 -
Beyond Not-Forgetting: Continual Learning with Backward Knowledge Transfer
Paper • 2211.00789 • Published • 1 -
Preserving Linear Separability in Continual Learning by Backward Feature Projection
Paper • 2303.14595 • Published • 2 -
Big-model Driven Few-shot Continual Learning
Paper • 2309.00862 • Published • 1 -
Augmentation with Projection: Towards an Effective and Efficient Data Augmentation Paradigm for Distillation
Paper • 2210.11768 • Published • 1 -
Understanding the Role of Mixup in Knowledge Distillation: An Empirical Study
Paper • 2211.03946 • Published • 1 -
What Makes a "Good" Data Augmentation in Knowledge Distillation -- A Statistical Perspective
Paper • 2012.02909 • Published • 1 -
Group channel pruning and spatial attention distilling for object detection
Paper • 2306.01526 • Published • 1 -
Structured Pruning Learns Compact and Accurate Models
Paper • 2204.00408 • Published • 1 -
MPCFormer: fast, performant and private Transformer inference with MPC
Paper • 2211.01452 • Published • 1 -
Towards Teachable Conversational Agents
Paper • 2102.10387 • Published • 1 -
OrchestraLLM: Efficient Orchestration of Language Models for Dialogue State Tracking
Paper • 2311.09758 • Published • 1 -
Task-Specific Expert Pruning for Sparse Mixture-of-Experts
Paper • 2206.00277 • Published • 1 -
Augmented Large Language Models with Parametric Knowledge Guiding
Paper • 2305.04757 • Published • 2 -
NLP From Scratch Without Large-Scale Pretraining: A Simple and Efficient Framework
Paper • 2111.04130 • Published • 1 -
Answering Unseen Questions With Smaller Language Models Using Rationale Generation and Dense Retrieval
Paper • 2308.04711 • Published • 1