Data Selection for Multi-turn Dialogue Instruction Tuning
Fuente:
arXiv
Guardado en:
| Autores principales: | Li, Bo, Zhang, Shikun, Ye, Wei |
|---|---|
| Formato: | Preprint |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Improving Multi-turn Dialogue Consistency with Self-Recall Thinking
por: Pang, Renning, et al.
Publicado: (2026)
por: Pang, Renning, et al.
Publicado: (2026)
On the Multi-turn Instruction Following for Conversational Web Agents
por: Deng, Yang, et al.
Publicado: (2024)
por: Deng, Yang, et al.
Publicado: (2024)
MIG: Automatic Data Selection for Instruction Tuning by Maximizing Information Gain in Semantic Space
por: Chen, Yicheng, et al.
Publicado: (2025)
por: Chen, Yicheng, et al.
Publicado: (2025)
PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization
por: Wang, Yidong, et al.
Publicado: (2023)
por: Wang, Yidong, et al.
Publicado: (2023)
Instruction Data Selection via Answer Divergence
por: Li, Bo, et al.
Publicado: (2026)
por: Li, Bo, et al.
Publicado: (2026)
CPsyCoun: A Report-based Multi-turn Dialogue Reconstruction and Evaluation Framework for Chinese Psychological Counseling
por: Zhang, Chenhao, et al.
Publicado: (2024)
por: Zhang, Chenhao, et al.
Publicado: (2024)
Retrieval as Generation: A Unified Framework with Self-Triggered Information Planning
por: Li, Bo, et al.
Publicado: (2026)
por: Li, Bo, et al.
Publicado: (2026)
TextBind: Multi-turn Interleaved Multimodal Instruction-following in the Wild
por: Li, Huayang, et al.
Publicado: (2023)
por: Li, Huayang, et al.
Publicado: (2023)
Speak Out of Turn: Safety Vulnerability of Large Language Models in Multi-turn Dialogue
por: Zhou, Zhenhong, et al.
Publicado: (2024)
por: Zhou, Zhenhong, et al.
Publicado: (2024)
Selective Reflection-Tuning: Student-Selected Data Recycling for LLM Instruction-Tuning
por: Li, Ming, et al.
Publicado: (2024)
por: Li, Ming, et al.
Publicado: (2024)
A Survey on Recent Advances in LLM-Based Multi-turn Dialogue Systems
por: Yi, Zihao, et al.
Publicado: (2024)
por: Yi, Zihao, et al.
Publicado: (2024)
A State-Update Prompting Strategy for Efficient and Robust Multi-turn Dialogue
por: Liu, Ziyi
Publicado: (2025)
por: Liu, Ziyi
Publicado: (2025)
MARS-Bench: A Multi-turn Athletic Real-world Scenario Benchmark for Dialogue Evaluation
por: Yang, Chenghao, et al.
Publicado: (2025)
por: Yang, Chenghao, et al.
Publicado: (2025)
TACOS: Open Tagging and Comparative Scoring for Instruction Fine-Tuning Data Selection
por: He, Xixiang, et al.
Publicado: (2025)
por: He, Xixiang, et al.
Publicado: (2025)
Inductive-Deductive Strategy Reuse for Multi-Turn Instructional Dialogues
por: Ou, Jiao, et al.
Publicado: (2024)
por: Ou, Jiao, et al.
Publicado: (2024)
Select2Reason: Efficient Instruction-Tuning Data Selection for Long-CoT Reasoning
por: Yang, Cehao, et al.
Publicado: (2025)
por: Yang, Cehao, et al.
Publicado: (2025)
CrowdSelect: Synthetic Instruction Data Selection with Multi-LLM Wisdom
por: Li, Yisen, et al.
Publicado: (2025)
por: Li, Yisen, et al.
Publicado: (2025)
MemeCMD: An Automatically Generated Chinese Multi-turn Dialogue Dataset with Contextually Retrieved Memes
por: Wang, Yuheng, et al.
Publicado: (2025)
por: Wang, Yuheng, et al.
Publicado: (2025)
SEADialogues: A Multilingual Culturally Grounded Multi-turn Dialogue Dataset on Southeast Asian Languages
por: Kautsar, Muhammad Dehan Al, et al.
Publicado: (2025)
por: Kautsar, Muhammad Dehan Al, et al.
Publicado: (2025)
Instruction Mining: Instruction Data Selection for Tuning Large Language Models
por: Cao, Yihan, et al.
Publicado: (2023)
por: Cao, Yihan, et al.
Publicado: (2023)
LESS: Selecting Influential Data for Targeted Instruction Tuning
por: Xia, Mengzhou, et al.
Publicado: (2024)
por: Xia, Mengzhou, et al.
Publicado: (2024)
Asymmetric Actor-Critic for Multi-turn LLM Agents
por: Jiang, Shuli, et al.
Publicado: (2026)
por: Jiang, Shuli, et al.
Publicado: (2026)
TAGCOS: Task-agnostic Gradient Clustered Coreset Selection for Instruction Tuning Data
por: Zhang, Jipeng, et al.
Publicado: (2024)
por: Zhang, Jipeng, et al.
Publicado: (2024)
Not All Documents Are What You Need for Extracting Instruction Tuning Data
por: Zhang, Chi, et al.
Publicado: (2025)
por: Zhang, Chi, et al.
Publicado: (2025)
Evaluating & Reducing Deceptive Dialogue From Language Models with Multi-turn RL
por: Abdulhai, Marwa, et al.
Publicado: (2025)
por: Abdulhai, Marwa, et al.
Publicado: (2025)
ROSE: A Reward-Oriented Data Selection Framework for LLM Task-Specific Instruction Tuning
por: Wu, Yang, et al.
Publicado: (2024)
por: Wu, Yang, et al.
Publicado: (2024)
Position Debiasing Fine-Tuning for Causal Perception in Long-Term Dialogue
por: Fan, Shixuan, et al.
Publicado: (2024)
por: Fan, Shixuan, et al.
Publicado: (2024)
What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning
por: Liu, Wei, et al.
Publicado: (2023)
por: Liu, Wei, et al.
Publicado: (2023)
Beyond Single-shot Writing: Deep Research Agents are Unreliable at Multi-turn Report Revision
por: Chen, Bingsen, et al.
Publicado: (2026)
por: Chen, Bingsen, et al.
Publicado: (2026)
Unveiling the Impact of Coding Data Instruction Fine-Tuning on Large Language Models Reasoning
por: Zhang, Xinlu, et al.
Publicado: (2024)
por: Zhang, Xinlu, et al.
Publicado: (2024)
Raw Text is All you Need: Knowledge-intensive Multi-turn Instruction Tuning for Large Language Model
por: Hou, Xia, et al.
Publicado: (2024)
por: Hou, Xia, et al.
Publicado: (2024)
Uncertainty-Aware Gradient Signal-to-Noise Data Selection for Instruction Tuning
por: Yuan, Zhihang, et al.
Publicado: (2026)
por: Yuan, Zhihang, et al.
Publicado: (2026)
Investigating Instruction Tuning Large Language Models on Graphs
por: Zhu, Kerui, et al.
Publicado: (2024)
por: Zhu, Kerui, et al.
Publicado: (2024)
SelectIT: Selective Instruction Tuning for LLMs via Uncertainty-Aware Self-Reflection
por: Liu, Liangxin, et al.
Publicado: (2024)
por: Liu, Liangxin, et al.
Publicado: (2024)
Personalized Topic Selection Model for Topic-Grounded Dialogue
por: Fan, Shixuan, et al.
Publicado: (2024)
por: Fan, Shixuan, et al.
Publicado: (2024)
M2S: Multi-turn to Single-turn jailbreak in Red Teaming for LLMs
por: Ha, Junwoo, et al.
Publicado: (2025)
por: Ha, Junwoo, et al.
Publicado: (2025)
Call for Rigor in Reporting Quality of Instruction Tuning Data
por: Moon, Hyeonseok, et al.
Publicado: (2025)
por: Moon, Hyeonseok, et al.
Publicado: (2025)
The Best Instruction-Tuning Data are Those That Fit
por: Zhang, Dylan, et al.
Publicado: (2025)
por: Zhang, Dylan, et al.
Publicado: (2025)
CrisisSense-LLM: Instruction Fine-Tuned Large Language Model for Multi-label Social Media Text Classification in Disaster Informatics
por: Yin, Kai, et al.
Publicado: (2024)
por: Yin, Kai, et al.
Publicado: (2024)
Instruct Once, Chat Consistently in Multiple Rounds: An Efficient Tuning Framework for Dialogue
por: Wang, Jian, et al.
Publicado: (2024)
por: Wang, Jian, et al.
Publicado: (2024)
Ejemplares similares
-
Improving Multi-turn Dialogue Consistency with Self-Recall Thinking
por: Pang, Renning, et al.
Publicado: (2026) -
On the Multi-turn Instruction Following for Conversational Web Agents
por: Deng, Yang, et al.
Publicado: (2024) -
MIG: Automatic Data Selection for Instruction Tuning by Maximizing Information Gain in Semantic Space
por: Chen, Yicheng, et al.
Publicado: (2025) -
PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization
por: Wang, Yidong, et al.
Publicado: (2023) -
Instruction Data Selection via Answer Divergence
por: Li, Bo, et al.
Publicado: (2026)