Saved in:
| Main Authors: | Langner, Mikołaj, Eliasz, Jan, Rudnicka, Ewa, Kocoń, Jan |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.03830 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
AggTruth: Contextual Hallucination Detection using Aggregated Attention Scores in LLMs
by: Matys, Piotr, et al.
Published: (2025)
by: Matys, Piotr, et al.
Published: (2025)
Divide (Text) and Conquer (Sentiment): Improved Sentiment Classification by Constituent Conflict Resolution
by: Kościałkowski, Jan, et al.
Published: (2025)
by: Kościałkowski, Jan, et al.
Published: (2025)
Backtranslation and paraphrasing in the LLM era? Comparing data augmentation methods for emotion classification
by: Radliński, Łukasz, et al.
Published: (2025)
by: Radliński, Łukasz, et al.
Published: (2025)
What properties of reasoning supervision are associated with improved downstream model quality?
by: Langner, Mikołaj, et al.
Published: (2026)
by: Langner, Mikołaj, et al.
Published: (2026)
Language, Culture, and Ideology: Personalizing Offensiveness Detection in Political Tweets with Reasoning LLMs
by: Pihulski, Dzmitry, et al.
Published: (2025)
by: Pihulski, Dzmitry, et al.
Published: (2025)
Hierarchical Divide-and-Conquer for Fine-Grained Alignment in LLM-Based Medical Evaluation
by: Zheng, Shunfan, et al.
Published: (2025)
by: Zheng, Shunfan, et al.
Published: (2025)
LLMSQL: Upgrading WikiSQL for the LLM Era of Text-to-SQL
by: Pihulski, Dzmitry, et al.
Published: (2025)
by: Pihulski, Dzmitry, et al.
Published: (2025)
An Examination on the Effectiveness of Divide-and-Conquer Prompting in Large Language Models
by: Zhang, Yizhou, et al.
Published: (2024)
by: Zhang, Yizhou, et al.
Published: (2024)
AgentGroupChat-V2: Divide-and-Conquer Is What LLM-Based Multi-Agent System Need
by: Gu, Zhouhong, et al.
Published: (2025)
by: Gu, Zhouhong, et al.
Published: (2025)
Enhancing LLM Character-Level Manipulation via Divide and Conquer
by: Xiong, Zhen, et al.
Published: (2025)
by: Xiong, Zhen, et al.
Published: (2025)
DCR: Divide-and-Conquer Reasoning for Multi-choice Question Answering with LLMs
by: Meng, Zijie, et al.
Published: (2024)
by: Meng, Zijie, 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)
Control Large Language Models via Divide and Conquer
by: Li, Bingxuan, et al.
Published: (2024)
by: Li, Bingxuan, et al.
Published: (2024)
DCIS: Efficient Length Extrapolation of LLMs via Divide-and-Conquer Scaling Factor Search
by: Yang, Lei, et al.
Published: (2024)
by: Yang, Lei, et al.
Published: (2024)
Long Context Scaling: Divide and Conquer via Multi-Agent Question-driven Collaboration
by: Xiao, Sibo, et al.
Published: (2025)
by: Xiao, Sibo, et al.
Published: (2025)
When Does Divide and Conquer Work for Long Context LLM? A Noise Decomposition Framework
by: Xu, Zhen, et al.
Published: (2025)
by: Xu, Zhen, et al.
Published: (2025)
Training LLMs for Divide-and-Conquer Reasoning Elevates Test-Time Scalability
by: Liang, Xiao, et al.
Published: (2026)
by: Liang, Xiao, et al.
Published: (2026)
Divide and Conquer: Accelerating Diffusion-Based Large Language Models via Adaptive Parallel Decoding
by: Luo, Xiangzhong, et al.
Published: (2026)
by: Luo, Xiangzhong, et al.
Published: (2026)
Can Grammarly and ChatGPT accelerate language change? AI-powered technologies and their impact on the English language: wordiness vs. conciseness
by: Rudnicka, Karolina
Published: (2025)
by: Rudnicka, Karolina
Published: (2025)
The "negative end" of change in grammar: terminology, concepts and causes
by: Rudnicka, Karolina
Published: (2025)
by: Rudnicka, Karolina
Published: (2025)
"In order that" -- a data driven study of symptoms and causes of obsolescence
by: Rudnicka, Karolina
Published: (2025)
by: Rudnicka, Karolina
Published: (2025)
Variation of sentence length across time and genre
by: Rudnicka, Karolina
Published: (2025)
by: Rudnicka, Karolina
Published: (2025)
Prompt Tuned Embedding Classification for Multi-Label Industry Sector Allocation
by: Buchner, Valentin Leonhard, et al.
Published: (2023)
by: Buchner, Valentin Leonhard, et al.
Published: (2023)
Divide and Conquer: A Hybrid Strategy Defeats Multimodal Large Language Models
by: Mao, Yanxu, et al.
Published: (2024)
by: Mao, Yanxu, et al.
Published: (2024)
Divide-and-Conquer Meets Consensus: Unleashing the Power of Functions in Code Generation
by: Chen, Jingchang, et al.
Published: (2024)
by: Chen, Jingchang, et al.
Published: (2024)
SYMDIREC: A Neuro-Symbolic Divide-Retrieve-Conquer Framework for Enhanced RTL Synthesis and Summarization
by: Vijayaraghavan, Prashanth, et al.
Published: (2026)
by: Vijayaraghavan, Prashanth, et al.
Published: (2026)
OmAgent: A Multi-modal Agent Framework for Complex Video Understanding with Task Divide-and-Conquer
by: Zhang, Lu, et al.
Published: (2024)
by: Zhang, Lu, et al.
Published: (2024)
Personalized Large Language Models
by: Woźniak, Stanisław, et al.
Published: (2024)
by: Woźniak, Stanisław, et al.
Published: (2024)
Cobblestone: A Divide-and-Conquer Approach for Automating Formal Verification
by: Kasibatla, Saketh Ram, et al.
Published: (2024)
by: Kasibatla, Saketh Ram, et al.
Published: (2024)
Fine-grained Verbal Attack Detection via a Hierarchical Divide-and-Conquer Framework
by: Zheng, Quan, et al.
Published: (2026)
by: Zheng, Quan, et al.
Published: (2026)
LLM Prompt Duel Optimizer: Efficient Label-Free Prompt Optimization
by: Wu, Yuanchen, et al.
Published: (2025)
by: Wu, Yuanchen, et al.
Published: (2025)
Divide, Reweight, and Conquer: A Logit Arithmetic Approach for In-Context Learning
by: Huang, Chengsong, et al.
Published: (2024)
by: Huang, Chengsong, et al.
Published: (2024)
Piece of Table: A Divide-and-Conquer Approach for Selecting Subtables in Table Question Answering
by: Lee, Wonjin, et al.
Published: (2024)
by: Lee, Wonjin, et al.
Published: (2024)
DCR-Consistency: Divide-Conquer-Reasoning for Consistency Evaluation and Improvement of Large Language Models
by: Cui, Wendi, et al.
Published: (2024)
by: Cui, Wendi, et al.
Published: (2024)
Predicting stock prices with ChatGPT-annotated Reddit sentiment
by: Kmak, Mateusz, et al.
Published: (2025)
by: Kmak, Mateusz, et al.
Published: (2025)
ICU: Conquering Language Barriers in Vision-and-Language Modeling by Dividing the Tasks into Image Captioning and Language Understanding
by: Wu, Guojun
Published: (2023)
by: Wu, Guojun
Published: (2023)
Efficient Prompt Caching via Embedding Similarity
by: Zhu, Hanlin, et al.
Published: (2024)
by: Zhu, Hanlin, et al.
Published: (2024)
Self-DC: When to Reason and When to Act? Self Divide-and-Conquer for Compositional Unknown Questions
by: Wang, Hongru, et al.
Published: (2024)
by: Wang, Hongru, et al.
Published: (2024)
Try, Check and Retry: A Divide-and-Conquer Framework for Boosting Long-context Tool-Calling Performance of LLMs
by: Chen, Kunfeng, et al.
Published: (2026)
by: Chen, Kunfeng, et al.
Published: (2026)
Decomposition-Based Synthesis for Applying Divide-and-Conquer-Like Algorithmic Paradigms
by: Ji, Ruyi, et al.
Published: (2022)
by: Ji, Ruyi, et al.
Published: (2022)
Similar Items
-
AggTruth: Contextual Hallucination Detection using Aggregated Attention Scores in LLMs
by: Matys, Piotr, et al.
Published: (2025) -
Divide (Text) and Conquer (Sentiment): Improved Sentiment Classification by Constituent Conflict Resolution
by: Kościałkowski, Jan, et al.
Published: (2025) -
Backtranslation and paraphrasing in the LLM era? Comparing data augmentation methods for emotion classification
by: Radliński, Łukasz, et al.
Published: (2025) -
What properties of reasoning supervision are associated with improved downstream model quality?
by: Langner, Mikołaj, et al.
Published: (2026) -
Language, Culture, and Ideology: Personalizing Offensiveness Detection in Political Tweets with Reasoning LLMs
by: Pihulski, Dzmitry, et al.
Published: (2025)