77 citations · 304 across the 92 of their papers we have counts for
74 papers · 1 filter
LongTraceRL: Learning Long-Context Reasoning from Search Agent Trajectories with Rubric Rewards
Nianyi Lin, Jiajie Zhang, Lei Hou +1
Long-context reasoning remains a central challenge for large language models, which often fail to locate and integrate key information in extensive distracting content. Reinforceme…
StoryAlign: Evaluating and Training Reward Models for Story Generation
Haotian Xia, Hao Peng, Yunjia Qi +4
Story generation aims to automatically produce coherent, structured, and engaging narratives. Although large language models (LLMs) have significantly advanced text generation, sto…
MAIC-UI: Making Interactive Courseware with Generative UI
Shangqing Tu, Yanjia Li, Keyu Chen +7
Creating interactive STEM courseware traditionally requires HTML/CSS/JavaScript expertise, leaving barriers for educators. While generative AI can produce HTML codes, existing tool…
On the Paradoxical Interference between Instruction-Following and Task Solving
Yunjia Qi, Hao Peng, Xintong Shi +5
Instruction following aims to align Large Language Models (LLMs) with human intent by specifying explicit constraints on how tasks should be performed. However, we reveal a counter…
RPC-Bench: A Fine-grained Benchmark for Research Paper Comprehension
Yelin Chen, Fanjin Zhang, Suping Sun +8
Understanding research papers remains challenging for foundation models due to specialized scientific discourse and complex figures and tables, yet existing benchmarks offer limite…
WebSeer: Training Deeper Search Agents through Reinforcement Learning with Self-Reflection
Guanzhong He, Zhen Yang, Jinxin Liu +3
Search agents have achieved significant advancements in enabling intelligent information retrieval and decision-making within interactive environments. Although reinforcement learn…
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…
Establishing Trustworthy LLM Evaluation via Shortcut Neuron Analysis
Kejian Zhu, Shangqing Tu, Zhuoran Jin +3
The development of large language models (LLMs) depends on trustworthy evaluation. However, most current evaluations rely on public benchmarks, which are prone to data contaminatio…
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…
LecEval: An Automated Metric for Multimodal Knowledge Acquisition in Multimedia Learning
Joy Lim Jia Yin, Daniel Zhang-Li, Jifan Yu +8
Evaluating the quality of slide-based multimedia instruction is challenging. Existing methods like manual assessment, reference-based metrics, and large language model evaluators f…
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…
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…
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…
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…
Awaking the Slides: A Tuning-free and Knowledge-regulated AI Tutoring System via Language Model Coordination
Daniel Zhang-Li, Zheyuan Zhang, Jifan Yu +8
The vast pre-existing slides serve as rich and important materials to carry lecture knowledge. However, effectively leveraging lecture slides to serve students is difficult due to…
LongCite: Enabling LLMs to Generate Fine-grained Citations in Long-context QA
Jiajie Zhang, Yushi Bai, Xin Lv +8
Though current long-context large language models (LLMs) have demonstrated impressive capacities in answering user questions based on extensive text, the lack of citations in their…
OpenEP: Open-Ended Future Event Prediction
Yong Guan, Hao Peng, Xiaozhi Wang +2
Future event prediction (FEP) is a long-standing and crucial task in the world, as understanding the evolution of events enables early risk identification, informed decision-making…
LongWriter: Unleashing 10,000+ Word Generation from Long Context LLMs
Yushi Bai, Jiajie Zhang, Xin Lv +6
Current long context large language models (LLMs) can process inputs up to 100,000 tokens, yet struggle to generate outputs exceeding even a modest length of 2,000 words. Through c…
LLMAEL: Large Language Models are Good Context Augmenters for Entity Linking
Amy Xin, Yunjia Qi, Zijun Yao +5
Specialized entity linking (EL) models are well-trained at mapping mentions to unique knowledge base (KB) entities according to a given context. However, specialized EL models stru…
Aligning Teacher with Student Preferences for Tailored Training Data Generation
Yantao Liu, Zhao Zhang, Zijun Yao +3
Large Language Models (LLMs) have shown significant promise as copilots in various tasks. Local deployment of LLMs on edge devices is necessary when handling privacy-sensitive data…
SeaKR: Self-aware Knowledge Retrieval for Adaptive Retrieval Augmented Generation
Zijun Yao, Weijian Qi, Liangming Pan +5
This paper introduces Self-aware Knowledge Retrieval (SeaKR), a novel adaptive RAG model that extracts self-aware uncertainty of LLMs from their internal states. SeaKR activates re…
R-Eval: A Unified Toolkit for Evaluating Domain Knowledge of Retrieval Augmented Large Language Models
Shangqing Tu, Yuanchun Wang, Jifan Yu +6
Large language models have achieved remarkable success on general NLP tasks, but they may fall short for domain-specific problems. Recently, various Retrieval-Augmented Large Langu…
Simulating Classroom Education with LLM-Empowered Agents
Zheyuan Zhang, Daniel Zhang-Li, Jifan Yu +9
Large language models (LLMs) have been applied across various intelligent educational tasks to assist teaching. While preliminary studies have focused on task-specific, independent…