2 citations · 6 across the 20 of their papers we have counts for
6 papers · 1 filter
StoryWriter: A Multi-Agent Framework for Long Story Generation
Haotian Xia, Hao Peng, Yunjia Qi +4
Long story generation remains a challenge for existing large language models (LLMs), primarily due to two main factors: (1) discourse coherence, which requires plot consistency, lo…
VerIF: Verification Engineering for Reinforcement Learning in Instruction Following
Hao Peng, Yunjia Qi, Xiaozhi Wang +3
Reinforcement learning with verifiable rewards (RLVR) has become a key technique for enhancing large language models (LLMs), with verification engineering playing a central role. H…
AGENTIF: Benchmarking Instruction Following of Large Language Models in Agentic Scenarios
Yunjia Qi, Hao Peng, Xiaozhi Wang +5
Large Language Models (LLMs) have demonstrated advanced capabilities in real-world agentic applications. Growing research efforts aim to develop LLM-based agents to address practic…
MRCEval: A Comprehensive, Challenging and Accessible Machine Reading Comprehension Benchmark
Shengkun Ma, Hao Peng, Lei Hou +1
Machine Reading Comprehension (MRC) is an essential task in evaluating natural language understanding. Existing MRC datasets primarily assess specific aspects of reading comprehens…
Agentic Reward Modeling: Integrating Human Preferences with Verifiable Correctness Signals for Reliable Reward Systems
Hao Peng, Yunjia Qi, Xiaozhi Wang +4
Reward models (RMs) are crucial for the training and inference-time scaling up of large language models (LLMs). However, existing reward models primarily focus on human preferences…
LongBench v2: Towards Deeper Understanding and Reasoning on Realistic Long-context Multitasks
Yushi Bai, Shangqing Tu, Jiajie Zhang +9
This paper introduces LongBench v2, a benchmark designed to assess the ability of LLMs to handle long-context problems requiring deep understanding and reasoning across real-world…