15 papers · 1 filter
DeepPrune: Parallel Scaling without Inter-trace Redundancy
Shangqing Tu, Yaxuan Li, Yushi Bai +2
Parallel scaling has emerged as a powerful paradigm to enhance reasoning capabilities in large language models (LLMs) by generating multiple Chain-of-Thought (CoT) traces simultane…
Chaining the Evidence: Robust Reinforcement Learning for Deep Search Agents with Citation-Aware Rubric Rewards
Jiajie Zhang, Xin Lv, Ling Feng +2
Reinforcement learning (RL) has emerged as a critical technique for enhancing LLM-based deep search agents. However, existing approaches primarily rely on binary outcome rewards, w…
AdaptThink: Reasoning Models Can Learn When to Think
Jiajie Zhang, Nianyi Lin, Lei Hou +2
Recently, large reasoning models have achieved impressive performance on various tasks by employing human-like deep thinking. However, the lengthy thinking process substantially in…
Toward Generalizable Evaluation in the LLM Era: A Survey Beyond Benchmarks
Yixin Cao, Shibo Hong, Xinze Li +24
Large Language Models (LLMs) are advancing at an amazing speed and have become indispensable across academia, industry, and daily applications. To keep pace with the status quo, th…
EventSum: A Large-Scale Event-Centric Summarization Dataset for Chinese Multi-News Documents
Mengna Zhu, Kaisheng Zeng, Mao Wang +4
In real life, many dynamic events, such as major disasters and large-scale sports events, evolve continuously over time. Obtaining an overview of these events can help people quick…
ADELIE: Aligning Large Language Models on Information Extraction
Yunjia Qi, Hao Peng, Xiaozhi Wang +3
Large language models (LLMs) usually fall short on information extraction (IE) tasks and struggle to follow the complex instructions of IE tasks. This primarily arises from LLMs no…