Saved in:
| Main Authors: | Renduchintala, H S V N S Kowndinya, Bhatia, Sumit, Ramakrishnan, Ganesh |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2403.08370 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Heterogeneity in Formal Linguistic Competence of Language Models: Is Data the Real Bottleneck?
by: Renduchintala, H S V N S Kowndinya, et al.
Published: (2026)
by: Renduchintala, H S V N S Kowndinya, et al.
Published: (2026)
On the Effect of Instruction Tuning Loss on Generalization
by: Chatterjee, Anwoy, et al.
Published: (2025)
by: Chatterjee, Anwoy, et al.
Published: (2025)
POSIX: A Prompt Sensitivity Index For Large Language Models
by: Chatterjee, Anwoy, et al.
Published: (2024)
by: Chatterjee, Anwoy, et al.
Published: (2024)
Consistency Is the Key: Detecting Hallucinations in LLM Generated Text By Checking Inconsistencies About Key Facts
by: Gupta, Raavi, et al.
Published: (2025)
by: Gupta, Raavi, et al.
Published: (2025)
Tied-Lora: Enhancing parameter efficiency of LoRA with weight tying
by: Renduchintala, Adithya, et al.
Published: (2023)
by: Renduchintala, Adithya, et al.
Published: (2023)
DynamixSFT: Dynamic Mixture Optimization of Instruction Tuning Collections
by: Shin, Haebin, et al.
Published: (2025)
by: Shin, Haebin, et al.
Published: (2025)
Upcycling Instruction Tuning from Dense to Mixture-of-Experts via Parameter Merging
by: Hui, Tingfeng, et al.
Published: (2024)
by: Hui, Tingfeng, et al.
Published: (2024)
Inducing Robustness in a 2 Dimensional Direct Preference Optimization Paradigm
by: Shashidhar, Sarvesh, et al.
Published: (2025)
by: Shashidhar, Sarvesh, et al.
Published: (2025)
Instruction Mining: Instruction Data Selection for Tuning Large Language Models
by: Cao, Yihan, et al.
Published: (2023)
by: Cao, Yihan, et al.
Published: (2023)
The Best Instruction-Tuning Data are Those That Fit
by: Zhang, Dylan, et al.
Published: (2025)
by: Zhang, Dylan, et al.
Published: (2025)
Selective Reflection-Tuning: Student-Selected Data Recycling for LLM Instruction-Tuning
by: Li, Ming, et al.
Published: (2024)
by: Li, Ming, et al.
Published: (2024)
LESS: Selecting Influential Data for Targeted Instruction Tuning
by: Xia, Mengzhou, et al.
Published: (2024)
by: Xia, Mengzhou, et al.
Published: (2024)
Contrastive Instruction Tuning
by: Yan, Tianyi Lorena, et al.
Published: (2024)
by: Yan, Tianyi Lorena, et al.
Published: (2024)
Federated Data-Efficient Instruction Tuning for Large Language Models
by: Qin, Zhen, et al.
Published: (2024)
by: Qin, Zhen, et al.
Published: (2024)
Uncertainty-Aware Gradient Signal-to-Noise Data Selection for Instruction Tuning
by: Yuan, Zhihang, et al.
Published: (2026)
by: Yuan, Zhihang, et al.
Published: (2026)
LongForm: Effective Instruction Tuning with Reverse Instructions
by: Köksal, Abdullatif, et al.
Published: (2023)
by: Köksal, Abdullatif, et al.
Published: (2023)
Generative Representational Instruction Tuning
by: Muennighoff, Niklas, et al.
Published: (2024)
by: Muennighoff, Niklas, et al.
Published: (2024)
Instruction Tuning with Human Curriculum
by: Lee, Bruce W., et al.
Published: (2023)
by: Lee, Bruce W., et al.
Published: (2023)
XFT: Unlocking the Power of Code Instruction Tuning by Simply Merging Upcycled Mixture-of-Experts
by: Ding, Yifeng, et al.
Published: (2024)
by: Ding, Yifeng, et al.
Published: (2024)
Diversity as a Reward: Fine-Tuning LLMs on a Mixture of Domain-Undetermined Data
by: Ling, Zhenqing, et al.
Published: (2025)
by: Ling, Zhenqing, et al.
Published: (2025)
TAGCOS: Task-agnostic Gradient Clustered Coreset Selection for Instruction Tuning Data
by: Zhang, Jipeng, et al.
Published: (2024)
by: Zhang, Jipeng, et al.
Published: (2024)
Neural Networks for Learnable and Scalable Influence Estimation of Instruction Fine-Tuning Data
by: Agarwal, Ishika, et al.
Published: (2025)
by: Agarwal, Ishika, et al.
Published: (2025)
Parameter-Efficient Routed Fine-Tuning: Mixture-of-Experts Demands Mixture of Adaptation Modules
by: Liu, Yilun, et al.
Published: (2025)
by: Liu, Yilun, et al.
Published: (2025)
What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning
by: Liu, Wei, et al.
Published: (2023)
by: Liu, Wei, et al.
Published: (2023)
The Right Time Matters: Data Arrangement Affects Zero-Shot Generalization in Instruction Tuning
by: He, Bingxiang, et al.
Published: (2024)
by: He, Bingxiang, et al.
Published: (2024)
SMART: Self-Aware Agent for Tool Overuse Mitigation
by: Qian, Cheng, et al.
Published: (2025)
by: Qian, Cheng, et al.
Published: (2025)
Parameter Efficient Instruction Tuning: An Empirical Study
by: He, Pengfei
Published: (2024)
by: He, Pengfei
Published: (2024)
ROSE: A Reward-Oriented Data Selection Framework for LLM Task-Specific Instruction Tuning
by: Wu, Yang, et al.
Published: (2024)
by: Wu, Yang, et al.
Published: (2024)
TableDreamer: Progressive and Weakness-guided Data Synthesis from Scratch for Table Instruction Tuning
by: Zheng, Mingyu, et al.
Published: (2025)
by: Zheng, Mingyu, et al.
Published: (2025)
Improving Model Evaluation using SMART Filtering of Benchmark Datasets
by: Gupta, Vipul, et al.
Published: (2024)
by: Gupta, Vipul, et al.
Published: (2024)
MoR: Mixture of Ranks for Low-Rank Adaptation Tuning
by: Tang, Chuanyu, et al.
Published: (2024)
by: Tang, Chuanyu, et al.
Published: (2024)
MEPT: Mixture of Expert Prompt Tuning as a Manifold Mapper
by: Zeng, Runjia, et al.
Published: (2025)
by: Zeng, Runjia, et al.
Published: (2025)
Multilingual Instruction Tuning With Just a Pinch of Multilinguality
by: Shaham, Uri, et al.
Published: (2024)
by: Shaham, Uri, et al.
Published: (2024)
MathScale: Scaling Instruction Tuning for Mathematical Reasoning
by: Tang, Zhengyang, et al.
Published: (2024)
by: Tang, Zhengyang, et al.
Published: (2024)
Instruction Fine-Tuning: Does Prompt Loss Matter?
by: Huerta-Enochian, Mathew, et al.
Published: (2024)
by: Huerta-Enochian, Mathew, et al.
Published: (2024)
Instruction Tuning for Large Language Models: A Survey
by: Zhang, Shengyu, et al.
Published: (2023)
by: Zhang, Shengyu, et al.
Published: (2023)
MURI: High-Quality Instruction Tuning Datasets for Low-Resource Languages via Reverse Instructions
by: Köksal, Abdullatif, et al.
Published: (2024)
by: Köksal, Abdullatif, et al.
Published: (2024)
From Language Modeling to Instruction Following: Understanding the Behavior Shift in LLMs after Instruction Tuning
by: Wu, Xuansheng, et al.
Published: (2023)
by: Wu, Xuansheng, et al.
Published: (2023)
Bandit Guided Submodular Curriculum for Adaptive Subset Selection
by: Chanda, Prateek, et al.
Published: (2025)
by: Chanda, Prateek, et al.
Published: (2025)
Instruction-Tuned, but Not More Verifiable Instruction-Following: A Cross-Task Diagnosis for LoRA Adapters
by: Zou, Junyi
Published: (2026)
by: Zou, Junyi
Published: (2026)
Similar Items
-
Heterogeneity in Formal Linguistic Competence of Language Models: Is Data the Real Bottleneck?
by: Renduchintala, H S V N S Kowndinya, et al.
Published: (2026) -
On the Effect of Instruction Tuning Loss on Generalization
by: Chatterjee, Anwoy, et al.
Published: (2025) -
POSIX: A Prompt Sensitivity Index For Large Language Models
by: Chatterjee, Anwoy, et al.
Published: (2024) -
Consistency Is the Key: Detecting Hallucinations in LLM Generated Text By Checking Inconsistencies About Key Facts
by: Gupta, Raavi, et al.
Published: (2025) -
Tied-Lora: Enhancing parameter efficiency of LoRA with weight tying
by: Renduchintala, Adithya, et al.
Published: (2023)