Conifer: Improving Complex Constrained Instruction-Following Ability of Large Language Models
Fuente:
arXiv
Saved in:
| Main Authors: | Sun, Haoran, Liu, Lixin, Li, Junjie, Wang, Fengyu, Dong, Baohua, Lin, Ran, Huang, Ruohui |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
InFoBench: Evaluating Instruction Following Ability in Large Language Models
by: Qin, Yiwei, et al.
Published: (2024)
by: Qin, Yiwei, et al.
Published: (2024)
Constraint Back-translation Improves Complex Instruction Following of Large Language Models
by: Qi, Yunjia, et al.
Published: (2024)
by: Qi, Yunjia, et al.
Published: (2024)
Revisiting Compositional Generalization Capability of Large Language Models Considering Instruction Following Ability
by: Sakai, Yusuke, et al.
Published: (2025)
by: Sakai, Yusuke, et al.
Published: (2025)
KITE: A Benchmark for Evaluating Korean Instruction-Following Abilities in Large Language Models
by: Kim, Dongjun, et al.
Published: (2025)
by: Kim, Dongjun, et al.
Published: (2025)
Rubrics to Tokens: Bridging Response-level Rubrics and Token-level Rewards in Instruction Following Tasks
by: Xu, Tianze, et al.
Published: (2026)
by: Xu, Tianze, et al.
Published: (2026)
Beyond Instruction Following: Evaluating Inferential Rule Following of Large Language Models
by: Sun, Wangtao, et al.
Published: (2024)
by: Sun, Wangtao, et al.
Published: (2024)
MulDimIF: A Multi-Dimensional Constraint Framework for Evaluating and Improving Instruction Following in Large Language Models
by: Ye, Junjie, et al.
Published: (2025)
by: Ye, Junjie, et al.
Published: (2025)
SPaR: Self-Play with Tree-Search Refinement to Improve Instruction-Following in Large Language Models
by: Cheng, Jiale, et al.
Published: (2024)
by: Cheng, Jiale, et al.
Published: (2024)
LIFEBench: Evaluating Length Instruction Following in Large Language Models
by: Zhang, Wei, et al.
Published: (2025)
by: Zhang, Wei, et al.
Published: (2025)
Step-by-Step Mastery: Enhancing Soft Constraint Following Ability of Large Language Models
by: Ren, Qingyu, et al.
Published: (2025)
by: Ren, Qingyu, et al.
Published: (2025)
Can It Edit? Evaluating the Ability of Large Language Models to Follow Code Editing Instructions
by: Cassano, Federico, et al.
Published: (2023)
by: Cassano, Federico, et al.
Published: (2023)
MuSC: Improving Complex Instruction Following with Multi-granularity Self-Contrastive Training
by: Huang, Hui, et al.
Published: (2025)
by: Huang, Hui, et al.
Published: (2025)
GraphGPT: Graph Instruction Tuning for Large Language Models
by: Tang, Jiabin, et al.
Published: (2023)
by: Tang, Jiabin, et al.
Published: (2023)
Self-Evolving Critique Abilities in Large Language Models
by: Tang, Zhengyang, et al.
Published: (2025)
by: Tang, Zhengyang, et al.
Published: (2025)
LexInstructEval: Lexical Instruction Following Evaluation for Large Language Models
by: Ren, Huimin, et al.
Published: (2025)
by: Ren, Huimin, et al.
Published: (2025)
AGENTIF: Benchmarking Instruction Following of Large Language Models in Agentic Scenarios
by: Qi, Yunjia, et al.
Published: (2025)
by: Qi, Yunjia, et al.
Published: (2025)
LogicGame: Benchmarking Rule-Based Reasoning Abilities of Large Language Models
by: Gui, Jiayi, et al.
Published: (2024)
by: Gui, Jiayi, et al.
Published: (2024)
Revisiting the Reliability of Language Models in Instruction-Following
by: Dong, Jianshuo, et al.
Published: (2025)
by: Dong, Jianshuo, et al.
Published: (2025)
MUFFIN: Curating Multi-Faceted Instructions for Improving Instruction-Following
by: Lou, Renze, et al.
Published: (2023)
by: Lou, Renze, et al.
Published: (2023)
LARFT: Closing the Cognition-Action Gap for Length Instruction Following in Large Language Models
by: Zhang, Wei, et al.
Published: (2026)
by: Zhang, Wei, et al.
Published: (2026)
Improving Interactive Diagnostic Ability of a Large Language Model Agent Through Clinical Experience Learning
by: Sun, Zhoujian, et al.
Published: (2025)
by: Sun, Zhoujian, et al.
Published: (2025)
Benchmarking Complex Instruction-Following with Multiple Constraints Composition
by: Wen, Bosi, et al.
Published: (2024)
by: Wen, Bosi, et al.
Published: (2024)
DeepJSONEval: Benchmarking Complex Nested JSON Data Mining for Large Language Models
by: Zhou, Zhicheng, et al.
Published: (2025)
by: Zhou, Zhicheng, et al.
Published: (2025)
RefuteBench: Evaluating Refuting Instruction-Following for Large Language Models
by: Yan, Jianhao, et al.
Published: (2024)
by: Yan, Jianhao, et al.
Published: (2024)
Humanity in AI: Detecting the Personality of Large Language Models
by: Zhan, Baohua, et al.
Published: (2024)
by: Zhan, Baohua, et al.
Published: (2024)
Can Large Language Models Understand Real-World Complex Instructions?
by: He, Qianyu, et al.
Published: (2023)
by: He, Qianyu, et al.
Published: (2023)
Improving Instruction-Following in Language Models through Activation Steering
by: Stolfo, Alessandro, et al.
Published: (2024)
by: Stolfo, Alessandro, et al.
Published: (2024)
Empowering Cross-lingual Abilities of Instruction-tuned Large Language Models by Translation-following demonstrations
by: Ranaldi, Leonardo, et al.
Published: (2023)
by: Ranaldi, Leonardo, et al.
Published: (2023)
CIF-Bench: A Chinese Instruction-Following Benchmark for Evaluating the Generalizability of Large Language Models
by: LI, Yizhi, et al.
Published: (2024)
by: LI, Yizhi, et al.
Published: (2024)
Diverse and Fine-Grained Instruction-Following Ability Exploration with Synthetic Data
by: Gu, Zihui, et al.
Published: (2024)
by: Gu, Zihui, et al.
Published: (2024)
Aligning Large Language Models to Follow Instructions and Hallucinate Less via Effective Data Filtering
by: Si, Shuzheng, et al.
Published: (2025)
by: Si, Shuzheng, et al.
Published: (2025)
Improving Natural Language Understanding for LLMs via Large-Scale Instruction Synthesis
by: Yuan, Lin, et al.
Published: (2025)
by: Yuan, Lin, et al.
Published: (2025)
CodeIF-Bench: Evaluating Instruction-Following Capabilities of Large Language Models in Interactive Code Generation
by: Wang, Peiding, et al.
Published: (2025)
by: Wang, Peiding, et al.
Published: (2025)
RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models
by: Wang, Zekun Moore, et al.
Published: (2023)
by: Wang, Zekun Moore, et al.
Published: (2023)
Instruction Following by Principled Boosting Attention of Large Language Models
by: Guardieiro, Vitoria, et al.
Published: (2025)
by: Guardieiro, Vitoria, et al.
Published: (2025)
Incentivizing Reasoning for Advanced Instruction-Following of Large Language Models
by: Qin, Yulei, et al.
Published: (2025)
by: Qin, Yulei, et al.
Published: (2025)
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)
Orca: Enhancing Role-Playing Abilities of Large Language Models by Integrating Personality Traits
by: Huang, Yuxuan
Published: (2024)
by: Huang, Yuxuan
Published: (2024)
Editing Factual Knowledge and Explanatory Ability of Medical Large Language Models
by: Xu, Derong, et al.
Published: (2024)
by: Xu, Derong, et al.
Published: (2024)
Follow-Up Questions Improve Documents Generated by Large Language Models
by: Tix, Bernadette J
Published: (2024)
by: Tix, Bernadette J
Published: (2024)
Similar Items
-
InFoBench: Evaluating Instruction Following Ability in Large Language Models
by: Qin, Yiwei, et al.
Published: (2024) -
Constraint Back-translation Improves Complex Instruction Following of Large Language Models
by: Qi, Yunjia, et al.
Published: (2024) -
Revisiting Compositional Generalization Capability of Large Language Models Considering Instruction Following Ability
by: Sakai, Yusuke, et al.
Published: (2025) -
KITE: A Benchmark for Evaluating Korean Instruction-Following Abilities in Large Language Models
by: Kim, Dongjun, et al.
Published: (2025) -
Rubrics to Tokens: Bridging Response-level Rubrics and Token-level Rewards in Instruction Following Tasks
by: Xu, Tianze, et al.
Published: (2026)