arXiv Machine Learning

Improving Synthetic Data Generation for Argument Mining via Adversarial Reinforcement Learning

The paper introduces an adversarial reinforcement learning framework to generate synthetic data for Argument Mining (AM). By jointly training a generator and a discriminator, the system produces structured AM instances that are both accurate and diverse. Experiments show consistent performance gains on three benchmark datasets in both full-data and low-resource scenarios.

arXiv AI

Jun 17

R1-SyntheticVL: Is Synthetic Data from Generative Models Ready for Multimodal Large Language Model?

arXiv:2602. 03300v2 Announce Type: replace-cross Abstract: In this work, we aim to develop effective data synthesis techniques that autonomously synthesize multimodal training data for enhancing MLLMs in solving complex real-world tasks.

By Jingyi Zhang, Tianyi Lin, Huanjin Yao, Xiang Lan, Shunyu Liu, Jiaxing Huang

llms multimodal benchmarks safety

arXiv AI

Jun 26

Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes

arXiv:2509. 09960v2 Announce Type: replace-cross Abstract: Synthetic tabular data generation is increasingly essential in machine learning, supporting downstream applications when real-world, high-quality tabular data is insufficient.

By Mingxuan Jiang, Keyang Chen, Yongxin Wang, Yongsheng Zhao, Ziyue Dai, Yicun Liu, Zeping Li, Qiuyang Zhang, Hongyi Nie, Hongbin Zhu, Sen Liu, Guangnan Ye, Hongfeng Chai

llms diffusion fine-tuning benchmarks safety

arXiv Computation and Language

Sep 22

Extracting Arguments, Not Just Classifying Them: Instruction-Tuned LLMs for Generative Component Detection

arXiv:2609.24855v1 Announce Type: cross Abstract: Argumentative component detection (ACD) is a core subtask of Argument(ation) Mining (AM) and one of its most challenging aspects, as it requires join...

By Sofiane Elguendouze (UniCA, I3S, MARIANNE), Erwan Hain (UniCA, MARIANNE), Elena Cabrio (MARIANNE, UniCA), Serena Villata (CNRS, MARIANNE)

llms computer-vision fine-tuning benchmarks

arXiv AI

Jul 22

Fence: Specialized SLM Guardrails for LLM Applications

arXiv:2607. 18268v1 Announce Type: new Abstract: Real-world applications that use closed-source large language models (LLMs) need advanced safety measures that go beyond the basic content filters.

By Kumud Lakara, Ruibo Shi, Fran Silavong

llms diffusion safety

Hugging Face Trending Papers

Sep 21

Extracting Arguments, Not Just Classifying Them: Instruction-Tuned LLMs for Generative Component Detection

Argumentative component detection (ACD) is a core subtask of Argument(ation) Mining (AM) and one of its most challenging aspects, as it requires jointly delimiting argumentative spans and classifying...

llms computer-vision fine-tuning benchmarks

arXiv Computation and Language

Sep 22

Want Better Synthetic Data? Steer It: Activation Steering for Low-Resource Language Generation

arXiv:2606.18389v2 Announce Type: replace Abstract: Large language models (LLMs) have become an effective tool for synthetic data generation, including for low-resource languages, where generated dat...

By Jan Cegin, Daniil Gurgurov, Yusser Al Ghussin, Simon Ostermann

llms fine-tuning

arXiv AI

Oct 1

Beyond Mode Collapse: Generating Diverse Synthetic Expert Conversations via Generative Flow Networks

arXiv:2609.38359v1 Announce Type: new Abstract: High quality synthetic data is central to post training LLMs for adaptive AI applications that represent the diverse expert strategies and decisions in...

By Sumit Asthana, Michael Ion, Kevyn Collins Thompson

llms

arXiv AI

Aug 13

When the API Speaks the Wrong Language: Revisiting Post-Training for Multilingual Tool Use

arXiv:2608. 11715v1 Announce Type: cross Abstract: The reliability of Large Language Models (LLMs) for API calling degrades in multilingual settings.

By Siddharth Chauhan, Thomas Butler, Abhishek Singhania, Pankaj Porwal, Honey Gupta

llms agents reinforcement-learning fine-tuning benchmarks

arXiv AI

Jun 8

CAF-Gen: A Multi-Agent System for Enriching Argumentation Structures

arXiv:2606. 06646v1 Announce Type: cross Abstract: Formalizing complex reasoning from natural text is one of the central challenges in computational linguistics.

By Jakub B\k{a}ba, Jaros{\l}aw Chudziak

agents safety

arXiv AI

Jul 28

Loong: Synthesize Long Chain-of-Thoughts at Scale through Verifiers

arXiv:2509. 03059v2 Announce Type: replace-cross Abstract: Recent advances in Large Language Models (LLMs) have shown that their reasoning capabilities can be significantly improved through Reinforcement Learning with Verifiable Reward (RLVR), particularly in domains like mathematics and programming, where ground-truth correctness can be automatically evaluated.

By Xingyue Huang, Rishabh, Gregor Franke, Ziyi Yang, Jiamu Bai, Weijie Bai, Jinhe Bi, Zifeng Ding, Yiqun Duan, Chengyu Fan, Wendong Fan, Xin Gao, Ruohao Guo, Yuan He, Zhuangzhuang He, Xianglong Hu, Neil Johnson, Bowen Li, Fangru Lin, Siyu Lin, Tong Liu, Yunpu Ma, Hao Shen, Hao Sun, Beibei Wang, Fangyijie Wang, Hao Wang, Haoran Wang, Yang Wang, Yifeng Wang, Zhaowei Wang, Ziyang Wang, Yifan Wu, Zikai Xiao, Chengxing Xie, Fan Yang, Junxiao Yang, Qianshuo Ye, Ziyu Ye, Guangtao Zeng, Yuwen Ebony Zhang, Zeyu Zhang, Zihao Zhu, Bernard Ghanem, Philip Torr, Guohao Li

llms agents reinforcement-learning benchmarks

arXiv AI

Jun 29

ToE: A Hierarchical and Explainable Claim Verification Framework with Dynamic Multi-source Evidence Retrieval and Aggregation

arXiv:2606. 27736v1 Announce Type: new Abstract: The rapid spread of fake news poses increasing threats to information ecosystems, especially as AI-generated misinformation under Generative Engine Optimization (GEO) poisoning allows adversarially crafted content to be systematically surfaced by retrieval systems, contaminating LLM reasoning.

By Zhaoqi Wang, Zijian Zhang, Kun Zheng, Zhen Li, Xin Li, Chunlei Li, Jiamou Liu

llms agents reinforcement-learning

arXiv AI

Aug 18

ML-AutoResearch: Training Machine Learning Research Agents with Automatically Generated Environments

arXiv:2603. 17216v2 Announce Type: replace Abstract: With the advent of AI agents, automated scientific discovery is becoming an increasingly plausible goal.

By Ziyang Cai, Amir Saeidi, Harkirat Behl

agents fine-tuning benchmarks

Related stories