1 citations · 2 across the 10 of their papers we have counts for
10 papers
Contrastive Branch Policy Optimization
Ying Wang, Changlin Qiu, Bang Lin +4
Reinforcement learning with verifiable rewards (RLVR) enables language models to learn multi-turn interaction with external tools, yet its sparse outcome rewards provide no signal…
New Terms, New Toxicity: Consensus-based Chinese Neologism Toxicity Detection via Search-Augmented LLMs
Shiyao Cui, QingLin Zhang, Di Wang +7
Neologisms, emerging terms in meaning or form, can serve as new vehicles for toxic expression, like "country girl" as a stigmatizing label targeting feminism. Such toxic neologisms…
P^2O: Joint Policy and Prompt Optimization
Xinyu Lu, Kaiqi Zhang, Jinglin Yang +6
Reinforcement Learning with Verifiable Rewards (RLVR) enhances Large Language Model (LLM) reasoning but is suffer from advantage collapse: when all rollouts of a query receive iden…
Decoupling Reasoning and Confidence: Resurrecting Calibration in Reinforcement Learning from Verifiable Rewards
Zhengzhao Ma, Xueru Wen, Boxi Cao +6
Reinforcement Learning from Verifiable Rewards (RLVR) significantly enhances large language models (LLMs) reasoning but severely suffers from calibration degeneration, where models…
Enhancing LLM-Based Agents via Global Planning and Hierarchical Execution
Junjie Chen, Haitao Li, Jingli Yang +2
Intelligent agent systems based on Large Language Models (LLMs) have shown great potential in real-world applications. However, existing agent frameworks still face critical limita…
PoAct: Policy and Action Dual-Control Agent for Generalized Applications
Guozhi Yuan, Youfeng Liu, Jingli Yang +6
Based on their superior comprehension and reasoning capabilities, Large Language Model (LLM) driven agent frameworks have achieved significant success in numerous complex reasoning…
LegalAgentBench: Evaluating LLM Agents in Legal Domain
Haitao Li, Junjie Chen, Jingli Yang +10
With the increasing intelligence and autonomy of LLM agents, their potential applications in the legal domain are becoming increasingly apparent. However, existing general-domain b…
Data Contamination Report from the 2024 CONDA Shared Task
Oscar Sainz, Iker García-Ferrero, Alon Jacovi +25
The 1st Workshop on Data Contamination (CONDA 2024) focuses on all relevant aspects of data contamination in natural language processing, where data contamination is understood as…
Retrieval-Augmented Language Model for Extreme Multi-Label Knowledge Graph Link Prediction
Yu-Hsiang Lin, Huang-Ting Shieh, Chih-Yu Liu +4
Extrapolation in Large language models (LLMs) for open-ended inquiry encounters two pivotal issues: (1) hallucination and (2) expensive training costs. These issues present challen…
Noise-Robust De-Duplication at Scale
Emily Silcock, Luca D'Amico-Wong, Jinglin Yang +1
Identifying near duplicates within large, noisy text corpora has a myriad of applications that range from de-duplicating training datasets, reducing privacy risk, and evaluating te…