Saved in:
| Main Authors: | Liu, Xiangyang, He, Junliang, Qiu, Xipeng |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.00473 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
World Modeling Makes a Better Planner: Dual Preference Optimization for Embodied Task Planning
by: Wang, Siyin, et al.
Published: (2025)
by: Wang, Siyin, et al.
Published: (2025)
DenoSent: A Denoising Objective for Self-Supervised Sentence Representation Learning
by: Wang, Xinghao, et al.
Published: (2024)
by: Wang, Xinghao, et al.
Published: (2024)
Abstraction-of-Thought Makes Language Models Better Reasoners
by: Hong, Ruixin, et al.
Published: (2024)
by: Hong, Ruixin, et al.
Published: (2024)
Evaluating the Performance of Large Language Models on GAOKAO Benchmark
by: Zhang, Xiaotian, et al.
Published: (2023)
by: Zhang, Xiaotian, et al.
Published: (2023)
AnnoLLM: Making Large Language Models to Be Better Crowdsourced Annotators
by: He, Xingwei, et al.
Published: (2023)
by: He, Xingwei, et al.
Published: (2023)
Make Large Language Model a Better Ranker
by: Chao, Wen-Shuo, et al.
Published: (2024)
by: Chao, Wen-Shuo, et al.
Published: (2024)
Making Long-Context Language Models Better Multi-Hop Reasoners
by: Li, Yanyang, et al.
Published: (2024)
by: Li, Yanyang, et al.
Published: (2024)
Full Parameter Fine-tuning for Large Language Models with Limited Resources
by: Lv, Kai, et al.
Published: (2023)
by: Lv, Kai, et al.
Published: (2023)
Learning to Self-Verify Makes Language Models Better Reasoners
by: Chen, Yuxin, et al.
Published: (2026)
by: Chen, Yuxin, et al.
Published: (2026)
Emergent Structured Representations Support Flexible In-Context Inference in Large Language Models
by: Xu, Ningyu, et al.
Published: (2026)
by: Xu, Ningyu, et al.
Published: (2026)
ExpNote: Black-box Large Language Models are Better Task Solvers with Experience Notebook
by: Sun, Wangtao, et al.
Published: (2023)
by: Sun, Wangtao, et al.
Published: (2023)
When Less Language is More: Language-Reasoning Disentanglement Makes LLMs Better Multilingual Reasoners
by: Zhao, Weixiang, et al.
Published: (2025)
by: Zhao, Weixiang, et al.
Published: (2025)
Scaling Laws for Fact Memorization of Large Language Models
by: Lu, Xingyu, et al.
Published: (2024)
by: Lu, Xingyu, et al.
Published: (2024)
RankPrompt: Step-by-Step Comparisons Make Language Models Better Reasoners
by: Hu, Chi, et al.
Published: (2024)
by: Hu, Chi, et al.
Published: (2024)
AutoLogi: Automated Generation of Logic Puzzles for Evaluating Reasoning Abilities of Large Language Models
by: Zhu, Qin, et al.
Published: (2025)
by: Zhu, Qin, et al.
Published: (2025)
Chain of Strategy Optimization Makes Large Language Models Better Emotional Supporter
by: Zhao, Weixiang, et al.
Published: (2025)
by: Zhao, Weixiang, et al.
Published: (2025)
Knowledge Graph-Infused Fine-Tuning for Structured Reasoning in Large Language Models
by: Zhang, Wuyang, et al.
Published: (2025)
by: Zhang, Wuyang, et al.
Published: (2025)
Error Classification of Large Language Models on Math Word Problems: A Dynamically Adaptive Framework
by: Sun, Yuhong, et al.
Published: (2025)
by: Sun, Yuhong, et al.
Published: (2025)
DetectiveQA: Evaluating Long-Context Reasoning on Detective Novels
by: Xu, Zhe, et al.
Published: (2024)
by: Xu, Zhe, et al.
Published: (2024)
Advantageous Parameter Expansion Training Makes Better Large Language Models
by: Gu, Naibin, et al.
Published: (2025)
by: Gu, Naibin, et al.
Published: (2025)
Making Large Language Models Perform Better in Knowledge Graph Completion
by: Zhang, Yichi, et al.
Published: (2023)
by: Zhang, Yichi, et al.
Published: (2023)
Beyond Attention Magnitude: Leveraging Inter-layer Rank Consistency for Efficient Vision-Language-Action Models
by: Liu, Peiju, et al.
Published: (2026)
by: Liu, Peiju, et al.
Published: (2026)
In-Memory Learning: A Declarative Learning Framework for Large Language Models
by: Wang, Bo, et al.
Published: (2024)
by: Wang, Bo, et al.
Published: (2024)
Training-Free Long-Context Scaling of Large Language Models
by: An, Chenxin, et al.
Published: (2024)
by: An, Chenxin, et al.
Published: (2024)
S^3cMath: Spontaneous Step-level Self-correction Makes Large Language Models Better Mathematical Reasoners
by: Yan, Yuchen, et al.
Published: (2024)
by: Yan, Yuchen, et al.
Published: (2024)
Benchmarking Hallucination in Large Language Models based on Unanswerable Math Word Problem
by: Sun, Yuhong, et al.
Published: (2024)
by: Sun, Yuhong, et al.
Published: (2024)
PURPLE: Making a Large Language Model a Better SQL Writer
by: Ren, Tonghui, et al.
Published: (2024)
by: Ren, Tonghui, et al.
Published: (2024)
Thus Spake Long-Context Large Language Model
by: Liu, Xiaoran, et al.
Published: (2025)
by: Liu, Xiaoran, et al.
Published: (2025)
Mentor-KD: Making Small Language Models Better Multi-step Reasoners
by: Lee, Hojae, et al.
Published: (2024)
by: Lee, Hojae, et al.
Published: (2024)
Data-free Weight Compress and Denoise for Large Language Models
by: Peng, Runyu, et al.
Published: (2024)
by: Peng, Runyu, et al.
Published: (2024)
Supervised Knowledge Makes Large Language Models Better In-context Learners
by: Yang, Linyi, et al.
Published: (2023)
by: Yang, Linyi, et al.
Published: (2023)
SpeechTokenizer: Unified Speech Tokenizer for Speech Large Language Models
by: Zhang, Xin, et al.
Published: (2023)
by: Zhang, Xin, et al.
Published: (2023)
Inference-Time Decontamination: Reusing Leaked Benchmarks for Large Language Model Evaluation
by: Zhu, Qin, et al.
Published: (2024)
by: Zhu, Qin, et al.
Published: (2024)
Aggregation of Reasoning: A Hierarchical Framework for Enhancing Answer Selection in Large Language Models
by: Yin, Zhangyue, et al.
Published: (2024)
by: Yin, Zhangyue, et al.
Published: (2024)
How Attention Sinks Emerge in Large Language Models: An Interpretability Perspective
by: Peng, Runyu, et al.
Published: (2026)
by: Peng, Runyu, et al.
Published: (2026)
Calibrating the Confidence of Large Language Models by Eliciting Fidelity
by: Zhang, Mozhi, et al.
Published: (2024)
by: Zhang, Mozhi, et al.
Published: (2024)
Evolution of Concepts in Language Model Pre-Training
by: Ge, Xuyang, et al.
Published: (2025)
by: Ge, Xuyang, et al.
Published: (2025)
In Their Own Words: Reasoning Traces Tailored for Small Models Make Them Better Reasoners
by: Kim, Jaehoon, et al.
Published: (2025)
by: Kim, Jaehoon, et al.
Published: (2025)
GAOKAO-MM: A Chinese Human-Level Benchmark for Multimodal Models Evaluation
by: Zong, Yi, et al.
Published: (2024)
by: Zong, Yi, et al.
Published: (2024)
MetaAlign: Align Large Language Models with Diverse Preferences during Inference Time
by: Zhang, Mozhi, et al.
Published: (2024)
by: Zhang, Mozhi, et al.
Published: (2024)
Similar Items
-
World Modeling Makes a Better Planner: Dual Preference Optimization for Embodied Task Planning
by: Wang, Siyin, et al.
Published: (2025) -
DenoSent: A Denoising Objective for Self-Supervised Sentence Representation Learning
by: Wang, Xinghao, et al.
Published: (2024) -
Abstraction-of-Thought Makes Language Models Better Reasoners
by: Hong, Ruixin, et al.
Published: (2024) -
Evaluating the Performance of Large Language Models on GAOKAO Benchmark
by: Zhang, Xiaotian, et al.
Published: (2023) -
AnnoLLM: Making Large Language Models to Be Better Crowdsourced Annotators
by: He, Xingwei, et al.
Published: (2023)