Distilling LLMs' Decomposition Abilities into Compact Language Models
Fuente:
arXiv
Saved in:
| Main Authors: | Tarasov, Denis, Shridhar, Kumar |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Compact Language Models via Pruning and Knowledge Distillation
by: Muralidharan, Saurav, et al.
Published: (2024)
by: Muralidharan, Saurav, et al.
Published: (2024)
Predicting Compact Phrasal Rewrites with Large Language Models for ASR Post Editing
by: Zhang, Hao, et al.
Published: (2025)
by: Zhang, Hao, et al.
Published: (2025)
daDPO: Distribution-Aware DPO for Distilling Conversational Abilities
by: Zhang, Zhengze, et al.
Published: (2025)
by: Zhang, Zhengze, et al.
Published: (2025)
Unlocking Continual Learning Abilities in Language Models
by: Du, Wenyu, et al.
Published: (2024)
by: Du, Wenyu, et al.
Published: (2024)
On the Reasoning Abilities of Masked Diffusion Language Models
by: Svete, Anej, et al.
Published: (2025)
by: Svete, Anej, et al.
Published: (2025)
Towards Reasoning Ability of Small Language Models
by: Srivastava, Gaurav, et al.
Published: (2025)
by: Srivastava, Gaurav, et al.
Published: (2025)
Deception Abilities Emerged in Large Language Models
by: Hagendorff, Thilo
Published: (2023)
by: Hagendorff, Thilo
Published: (2023)
Distill and Align Decomposition for Enhanced Claim Verification
by: Magomere, Jabez, et al.
Published: (2026)
by: Magomere, Jabez, et al.
Published: (2026)
Self-Evolving Critique Abilities in Large Language Models
by: Tang, Zhengyang, et al.
Published: (2025)
by: Tang, Zhengyang, et al.
Published: (2025)
Emergent Abilities in Large Language Models: A Survey
by: Berti, Leonardo, et al.
Published: (2025)
by: Berti, Leonardo, et al.
Published: (2025)
Understanding Emergent Abilities of Language Models from the Loss Perspective
by: Du, Zhengxiao, et al.
Published: (2024)
by: Du, Zhengxiao, et al.
Published: (2024)
An Empirical Study of Data Ability Boundary in LLMs' Math Reasoning
by: Chen, Zui, et al.
Published: (2024)
by: Chen, Zui, et al.
Published: (2024)
Evaluating the Generalization Ability of Quantized LLMs: Benchmark, Analysis, and Toolbox
by: Liu, Yijun, et al.
Published: (2024)
by: Liu, Yijun, et al.
Published: (2024)
Latent Context Compilation: Distilling Long Context into Compact Portable Memory
by: Li, Zeju, et al.
Published: (2026)
by: Li, Zeju, et al.
Published: (2026)
Psychological Counseling Ability of Large Language Models
by: Peng, Fangyu, et al.
Published: (2025)
by: Peng, Fangyu, et al.
Published: (2025)
On Teacher Hacking in Language Model Distillation
by: Tiapkin, Daniil, et al.
Published: (2025)
by: Tiapkin, Daniil, et al.
Published: (2025)
AQA-Bench: An Interactive Benchmark for Evaluating LLMs' Sequential Reasoning Ability
by: Yang, Siwei, et al.
Published: (2024)
by: Yang, Siwei, et al.
Published: (2024)
Distillation Contrastive Decoding: Improving LLMs Reasoning with Contrastive Decoding and Distillation
by: Phan, Phuc, et al.
Published: (2024)
by: Phan, Phuc, et al.
Published: (2024)
Efficiently Distilling LLMs for Edge Applications
by: Kundu, Achintya, et al.
Published: (2024)
by: Kundu, Achintya, et al.
Published: (2024)
AlignDistil: Token-Level Language Model Alignment as Adaptive Policy Distillation
by: Zhang, Songming, et al.
Published: (2025)
by: Zhang, Songming, et al.
Published: (2025)
Conifer: Improving Complex Constrained Instruction-Following Ability of Large Language Models
by: Sun, Haoran, et al.
Published: (2024)
by: Sun, Haoran, et al.
Published: (2024)
Do Large Language Models Have Compositional Ability? An Investigation into Limitations and Scalability
by: Xu, Zhuoyan, et al.
Published: (2024)
by: Xu, Zhuoyan, et al.
Published: (2024)
Understanding Reasoning Ability of Language Models From the Perspective of Reasoning Paths Aggregation
by: Wang, Xinyi, et al.
Published: (2024)
by: Wang, Xinyi, et al.
Published: (2024)
EmbedLLM: Learning Compact Representations of Large Language Models
by: Zhuang, Richard, et al.
Published: (2024)
by: Zhuang, Richard, et al.
Published: (2024)
Evolutionary Contrastive Distillation for Language Model Alignment
by: Katz-Samuels, Julian, et al.
Published: (2024)
by: Katz-Samuels, Julian, et al.
Published: (2024)
Exposing the Achilles' Heel: Evaluating LLMs Ability to Handle Mistakes in Mathematical Reasoning
by: Singh, Joykirat, et al.
Published: (2024)
by: Singh, Joykirat, et al.
Published: (2024)
Delta Knowledge Distillation for Large Language Models
by: Cao, Yihan, et al.
Published: (2025)
by: Cao, Yihan, et al.
Published: (2025)
Structured Agent Distillation for Large Language Model
by: Liu, Jun, et al.
Published: (2025)
by: Liu, Jun, et al.
Published: (2025)
Large Language Models Explore by Latent Distilling
by: Zeng, Yuanhao, et al.
Published: (2026)
by: Zeng, Yuanhao, et al.
Published: (2026)
MATH-Perturb: Benchmarking LLMs' Math Reasoning Abilities against Hard Perturbations
by: Huang, Kaixuan, et al.
Published: (2025)
by: Huang, Kaixuan, et al.
Published: (2025)
Kakugo: Distillation of Low-Resource Languages into Small Language Models
by: Devine, Peter, et al.
Published: (2026)
by: Devine, Peter, et al.
Published: (2026)
Skewed Memorization in Large Language Models: Quantification and Decomposition
by: Li, Hao, et al.
Published: (2025)
by: Li, Hao, et al.
Published: (2025)
ADaPT: As-Needed Decomposition and Planning with Language Models
by: Prasad, Archiki, et al.
Published: (2023)
by: Prasad, Archiki, et al.
Published: (2023)
BOND: Aligning LLMs with Best-of-N Distillation
by: Sessa, Pier Giuseppe, et al.
Published: (2024)
by: Sessa, Pier Giuseppe, et al.
Published: (2024)
A Decomposition Perspective to Long-context Reasoning for LLMs
by: Xiao, Yanling, et al.
Published: (2026)
by: Xiao, Yanling, et al.
Published: (2026)
Pre-trained Language Models Improve the Few-shot Prompt Ability of Decision Transformer
by: Yang, Yu, et al.
Published: (2024)
by: Yang, Yu, et al.
Published: (2024)
How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data Composition
by: Dong, Guanting, et al.
Published: (2023)
by: Dong, Guanting, et al.
Published: (2023)
UniSD: Towards a Unified Self-Distillation Framework for Large Language Models
by: Jin, Yiqiao, et al.
Published: (2026)
by: Jin, Yiqiao, et al.
Published: (2026)
Pruning and Distilling Mixture-of-Experts into Dense Language Models
by: Kim, Junhyuck, et al.
Published: (2026)
by: Kim, Junhyuck, et al.
Published: (2026)
Towards the Law of Capacity Gap in Distilling Language Models
by: Zhang, Chen, et al.
Published: (2023)
by: Zhang, Chen, et al.
Published: (2023)
Similar Items
-
Compact Language Models via Pruning and Knowledge Distillation
by: Muralidharan, Saurav, et al.
Published: (2024) -
Predicting Compact Phrasal Rewrites with Large Language Models for ASR Post Editing
by: Zhang, Hao, et al.
Published: (2025) -
daDPO: Distribution-Aware DPO for Distilling Conversational Abilities
by: Zhang, Zhengze, et al.
Published: (2025) -
Unlocking Continual Learning Abilities in Language Models
by: Du, Wenyu, et al.
Published: (2024) -
On the Reasoning Abilities of Masked Diffusion Language Models
by: Svete, Anej, et al.
Published: (2025)