What Should Feature Distillation Transfer in LLMs? A Task-Tangent Geometry View
Fuente:
arXiv
Saved in:
| Main Authors: | Saadi, Khouloud, Wang, Di |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Validity-Calibrated Reasoning Distillation
by: Saadi, Khouloud, et al.
Published: (2026)
by: Saadi, Khouloud, et al.
Published: (2026)
JGU Mainz's Submission to the WMT25 Shared Task on LLMs with Limited Resources for Slavic Languages: MT and QA
by: Saadi, Hossain Shaikh, et al.
Published: (2025)
by: Saadi, Hossain Shaikh, et al.
Published: (2025)
Feature Alignment and Representation Transfer in Knowledge Distillation for Large Language Models
by: Yang, Junjie, et al.
Published: (2025)
by: Yang, Junjie, et al.
Published: (2025)
Distilling Text Style Transfer With Self-Explanation From LLMs
by: Zhang, Chiyu, et al.
Published: (2024)
by: Zhang, Chiyu, et al.
Published: (2024)
"Yeah Right!" -- Do LLMs Exhibit Multimodal Feature Transfer?
by: Reichman, Benjamin, et al.
Published: (2025)
by: Reichman, Benjamin, et al.
Published: (2025)
What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests
by: Staufer, Dimitri
Published: (2025)
by: Staufer, Dimitri
Published: (2025)
What if LLMs Have Different World Views: Simulating Alien Civilizations with LLM-based Agents
by: Xue, Zhaoqian, et al.
Published: (2024)
by: Xue, Zhaoqian, et al.
Published: (2024)
CoMMET: To What Extent Can LLMs Perform Theory of Mind Tasks?
by: Chen, Ruirui, et al.
Published: (2026)
by: Chen, Ruirui, et al.
Published: (2026)
Exploring the Limits of Model Compression in LLMs: A Knowledge Distillation Study on QA Tasks
by: Datta, Joyeeta, et al.
Published: (2025)
by: Datta, Joyeeta, et al.
Published: (2025)
Feature Structure Distillation with Centered Kernel Alignment in BERT Transferring
by: Jung, Hee-Jun, et al.
Published: (2022)
by: Jung, Hee-Jun, et al.
Published: (2022)
Preference Curriculum: LLMs Should Always Be Pretrained on Their Preferred Data
by: Zhang, Xuemiao, et al.
Published: (2025)
by: Zhang, Xuemiao, et al.
Published: (2025)
Finding the Translation Switch: Discovering and Exploiting the Task-Initiation Features in LLMs
by: Wu, Xinwei, et al.
Published: (2026)
by: Wu, Xinwei, et al.
Published: (2026)
Should We Respect LLMs? A Cross-Lingual Study on the Influence of Prompt Politeness on LLM Performance
by: Yin, Ziqi, et al.
Published: (2024)
by: Yin, Ziqi, et al.
Published: (2024)
Divide-or-Conquer? Which Part Should You Distill Your LLM?
by: Wu, Zhuofeng, et al.
Published: (2024)
by: Wu, Zhuofeng, et al.
Published: (2024)
Hybrid Policy Distillation for LLMs
by: Zhu, Wenhong, et al.
Published: (2026)
by: Zhu, Wenhong, et al.
Published: (2026)
Selecting Auxiliary Data via Neural Tangent Kernels for Low-Resource Domains
by: Wang, Pingjie, et al.
Published: (2025)
by: Wang, Pingjie, et al.
Published: (2025)
LLMs Should Express Uncertainty Explicitly
by: Guo, Junyu, et al.
Published: (2026)
by: Guo, Junyu, et al.
Published: (2026)
The Good, The Bad, and The Greedy: Evaluation of LLMs Should Not Ignore Non-Determinism
by: Song, Yifan, et al.
Published: (2024)
by: Song, Yifan, et al.
Published: (2024)
Cross-Examination Framework: A Task-Agnostic Diagnostic for Information Fidelity in Text-to-Text Generation
by: Raha, Tathagata, et al.
Published: (2026)
by: Raha, Tathagata, et al.
Published: (2026)
LLMs Should Incorporate Explicit Mechanisms for Human Empathy
by: You, Xiaoxing, et al.
Published: (2026)
by: You, Xiaoxing, et al.
Published: (2026)
Probing the Geometry of Truth: Consistency and Generalization of Truth Directions in LLMs Across Logical Transformations and Question Answering Tasks
by: Bao, Yuntai, et al.
Published: (2025)
by: Bao, Yuntai, et al.
Published: (2025)
Bridging Language Barriers in Healthcare: A Study on Arabic LLMs
by: Saadi, Nada, et al.
Published: (2025)
by: Saadi, Nada, et al.
Published: (2025)
Towards Cross-Tokenizer Distillation: the Universal Logit Distillation Loss for LLMs
by: Boizard, Nicolas, et al.
Published: (2024)
by: Boizard, Nicolas, et al.
Published: (2024)
Keypoint-based Progressive Chain-of-Thought Distillation for LLMs
by: Feng, Kaituo, et al.
Published: (2024)
by: Feng, Kaituo, et al.
Published: (2024)
Beyond Answers: Transferring Reasoning Capabilities to Smaller LLMs Using Multi-Teacher Knowledge Distillation
by: Tian, Yijun, et al.
Published: (2024)
by: Tian, Yijun, et al.
Published: (2024)
Exploring the Personality Traits of LLMs through Latent Features Steering
by: Yang, Shu, et al.
Published: (2024)
by: Yang, Shu, et al.
Published: (2024)
The Lottery LLM Hypothesis, Rethinking What Abilities Should LLM Compression Preserve?
by: Tang, Zhenheng, et al.
Published: (2025)
by: Tang, Zhenheng, et al.
Published: (2025)
DRAG: Distilling RAG for SLMs from LLMs to Transfer Knowledge and Mitigate Hallucination via Evidence and Graph-based Distillation
by: Chen, Jennifer, et al.
Published: (2025)
by: Chen, Jennifer, et al.
Published: (2025)
Retrieval Augmented Question Answering: When Should LLMs Admit Ignorance?
by: Wang, Dingmin, et al.
Published: (2025)
by: Wang, Dingmin, et al.
Published: (2025)
BitDistiller: Unleashing the Potential of Sub-4-Bit LLMs via Self-Distillation
by: Du, Dayou, et al.
Published: (2024)
by: Du, Dayou, et al.
Published: (2024)
Dense X Retrieval: What Retrieval Granularity Should We Use?
by: Chen, Tong, et al.
Published: (2023)
by: Chen, Tong, et al.
Published: (2023)
Distilling Instruction-following Abilities of Large Language Models with Task-aware Curriculum Planning
by: Yue, Yuanhao, et al.
Published: (2024)
by: Yue, Yuanhao, et al.
Published: (2024)
Feature Alignment-Based Knowledge Distillation for Efficient Compression of Large Language Models
by: Wang, Shuo, et al.
Published: (2024)
by: Wang, Shuo, et al.
Published: (2024)
Is Modularity Transferable? A Case Study through the Lens of Knowledge Distillation
by: Klimaszewski, Mateusz, et al.
Published: (2024)
by: Klimaszewski, Mateusz, et al.
Published: (2024)
Training Task Experts through Retrieval Based Distillation
by: Ge, Jiaxin, et al.
Published: (2024)
by: Ge, Jiaxin, et al.
Published: (2024)
What Does Neuro Mean to Cardio? Investigating the Role of Clinical Specialty Data in Medical LLMs
by: Yan, Xinlan, et al.
Published: (2025)
by: Yan, Xinlan, et al.
Published: (2025)
What Features in Prompts Jailbreak LLMs? Investigating the Mechanisms Behind Attacks
by: Kirch, Nathalie, et al.
Published: (2024)
by: Kirch, Nathalie, et al.
Published: (2024)
Efficient Knowledge Transfer in Multi-Task Learning through Task-Adaptive Low-Rank Representation
by: Zhang, Xiao, et al.
Published: (2025)
by: Zhang, Xiao, et al.
Published: (2025)
SELT: Self-Evaluation Tree Search for LLMs with Task Decomposition
by: Wu, Mengsong, et al.
Published: (2025)
by: Wu, Mengsong, et al.
Published: (2025)
LLMs as Implicit Imputers: Uncertainty Should Scale with Missing Information
by: van Buuren, Stef
Published: (2026)
by: van Buuren, Stef
Published: (2026)
Similar Items
-
Validity-Calibrated Reasoning Distillation
by: Saadi, Khouloud, et al.
Published: (2026) -
JGU Mainz's Submission to the WMT25 Shared Task on LLMs with Limited Resources for Slavic Languages: MT and QA
by: Saadi, Hossain Shaikh, et al.
Published: (2025) -
Feature Alignment and Representation Transfer in Knowledge Distillation for Large Language Models
by: Yang, Junjie, et al.
Published: (2025) -
Distilling Text Style Transfer With Self-Explanation From LLMs
by: Zhang, Chiyu, et al.
Published: (2024) -
"Yeah Right!" -- Do LLMs Exhibit Multimodal Feature Transfer?
by: Reichman, Benjamin, et al.
Published: (2025)