10 papers
Learning after Execution with Action-Specific Credit
Yufeng Tian, Jiahao Tang, Minghao Wu
Execution is often used as terminal feedback: it scores a generated program, but the resulting state is not used for another learning action. Learning after execution instead uses…
BabelArena: A Large-Scale Multilingual Benchmark for LLM Agents
Peng Kuang, Yuchun Fan, Jiangnan Li +7
Large language model (LLM) agents increasingly execute multi-step workflows through tool use and interaction with users and environments. However, current agent evaluations are lar…
FinGuard: Detecting Financial Regulatory Non-Compliance in LLM Interactions
Huaixia Dou, Jie Zhu, Minghao Wu +5
As large language models (LLMs) are increasingly deployed in financial services, a single non-compliant interaction can expose institutions to regulatory penalties and direct consu…
TEMPO: Scaling Test-time Training for Large Reasoning Models
Qingyang Zhang, Xinke Kong, Haitao Wu +7
Test-time training (TTT) adapts model parameters on unlabeled test instances during inference time, which continuously extends capabilities beyond the reach of offline training. De…
Learning to Summarize by Learning to Quiz: Adversarial Agentic Collaboration for Long Document Summarization
Weixuan Wang, Minghao Wu, Barry Haddow +1
Long document summarization remains a significant challenge for current large language models (LLMs), as existing approaches commonly struggle with information loss, factual incons…
ExpertSteer: Intervening in LLMs through Expert Knowledge
Weixuan Wang, Minghao Wu, Barry Haddow +1
Large Language Models (LLMs) exhibit remarkable capabilities across various tasks, yet guiding them to follow desired behaviours during inference remains a significant challenge. A…
HBO: Hierarchical Balancing Optimization for Fine-Tuning Large Language Models
Weixuan Wang, Minghao Wu, Barry Haddow +1
Fine-tuning large language models (LLMs) on a mixture of diverse datasets poses challenges due to data imbalance and heterogeneity. Existing methods often address these issues acro…
Demystifying Multilingual Chain-of-Thought in Process Reward Modeling
Weixuan Wang, Minghao Wu, Barry Haddow +1
Large language models (LLMs) are designed to perform a wide range of tasks. To improve their ability to solve complex problems requiring multi-step reasoning, recent research lever…
Bridging the Language Gaps in Large Language Models with Inference-Time Cross-Lingual Intervention
Weixuan Wang, Minghao Wu, Barry Haddow +1
Large Language Models (LLMs) have shown remarkable capabilities in natural language processing but exhibit significant performance gaps among different languages. Most existing app…
Rewarding What Matters: Step-by-Step Reinforcement Learning for Task-Oriented Dialogue
Huifang Du, Shuqin Li, Minghao Wu +3
Reinforcement learning (RL) is a powerful approach to enhance task-oriented dialogue (TOD) systems. However, existing RL methods tend to mainly focus on generation tasks, such as d…