4 citations · 13 across the 42 of their papers we have counts for
43 papers
When RAG Meets Query Planning: Logical Query Trees for Resolving Exploratory Reasoning Problems
Ganlin Xu, Linghao Zhang, Zhitao Yin +7
Retrieval-Augmented Generation (RAG) effectively grounds large language models (LLMs) in external knowledge but struggles with \textbf{exploratory reasoning problems (ERPs)} that a…
SEA-Eval: A Benchmark for Evaluating Self-Evolving Agents Beyond Episodic Assessment
Sihang Jiang, Lipeng Ma, Zhonghua Hong +9
Current LLM-based agents demonstrate strong performance in episodic task execution but remain constrained by static toolsets and episodic amnesia, failing to accumulate experience…
SEIF: Self-Evolving Reinforcement Learning for Instruction Following
Qingyu Ren, Qianyu He, Jiajie Zhu +7
Instruction following is a fundamental capability of large language models (LLMs), yet continuously improving this capability remains challenging. Existing methods typically rely e…
GenericAgent: A Token-Efficient Self-Evolving LLM Agent via Contextual Information Density Maximization (V1.0)
Jiaqing Liang, Jinyi Han, Weijia Li +15
Long-horizon large language model (LLM) agents are fundamentally limited by context. As interactions become longer, tool descriptions, retrieved memories, and raw environmental fee…
What Makes an Ideal Quote? Recommending "Unexpected yet Rational" Quotations via Novelty
Bowei Zhang, Jin Xiao, Guanglei Yue +4
Quotation recommendation aims to enrich writing by suggesting quotes that complement a given context, yet existing systems mostly optimize surface-level topical relevance and ignor…
Structured Reasoning for Large Language Models
Jinyi Han, Zixiang Di, Zishang Jiang +4
Large language models (LLMs) achieve strong performance by generating long chains of thought, but longer traces always introduce redundant or ineffective reasoning steps. One typic…
LsrIF: Enhancing Logic-Structured Instruction Following of Large Language Models
Qingyu Ren, Qianyu He, Jingwen Chang +9
Instruction following is critical for large language models, yet real-world instructions often involve multiple constraints with logical structures, such as parallel composition, s…
Do Large Language Models Truly Understand Cross-cultural Differences?
Shiwei Guo, Sihang Jiang, Qianxi He +6
In recent years, large language models (LLMs) have demonstrated strong performance on multilingual tasks. Given its wide range of applications, cross-cultural understanding capabil…
Skeletons Matter: Dynamic Data Augmentation for Text-to-Query
Yuchen Ji, Bo Xu, Jie Shi +5
The task of translating natural language questions into query languages has long been a central focus in semantic parsing. Recent advancements in Large Language Models (LLMs) have…
Instructions are all you need: Self-supervised Reinforcement Learning for Instruction Following
Qingyu Ren, Qianyu He, Powei Chang +5
Language models often struggle to follow multi-constraint instructions that are crucial for real-world applications. Existing reinforcement learning (RL) approaches suffer from dep…
Mind the Generation Process: Fine-Grained Confidence Estimation During LLM Generation
Jinyi Han, Tingyun Li, Shisong Chen +8
While large language models (LLMs) have demonstrated remarkable performance across diverse tasks, they fundamentally lack self-awareness and frequently exhibit overconfidence, assi…
Beyond the Trade-off: Self-Supervised Reinforcement Learning for Reasoning Models' Instruction Following
Qingyu Ren, Qianyu He, Bowei Zhang +6
Reasoning models excel in complex problem solving but exhibit a concerning trade off between reasoning capabilities and instruction following abilities. Existing approaches for imp…
A Stitch in Time Saves Nine: Proactive Self-Refinement for Language Models
Jinyi Han, Xinyi Wang, Haiquan Zhao +9
Recent advances in self-refinement have demonstrated significant potential for improving the outputs of large language models (LLMs) through iterative refinement. However, most exi…
Logical Consistency is Vital: Neural-Symbolic Information Retrieval for Negative-Constraint Queries
Ganlin Xu, Zhoujia Zhang, Wangyi Mei +7
Information retrieval plays a crucial role in resource localization. Current dense retrievers retrieve the relevant documents within a corpus via embedding similarities, which comp…
BookWorld: From Novels to Interactive Agent Societies for Creative Story Generation
Yiting Ran, Xintao Wang, Tian Qiu +3
Recent advances in large language models (LLMs) have enabled social simulation through multi-agent systems. Prior efforts focus on agent societies created from scratch, assigning a…
PowerAttention: Exponentially Scaling of Receptive Fields for Effective Sparse Attention
Lida Chen, Dong Xu, Chenxin An +8
Large Language Models (LLMs) face efficiency bottlenecks due to the quadratic complexity of the attention mechanism when processing long contexts. Sparse attention methods offer a…
Order Matters: Investigate the Position Bias in Multi-constraint Instruction Following
Jie Zeng, Qianyu He, Qingyu Ren +5
Real-world instructions with multiple constraints pose a significant challenge to existing large language models (LLMs). An observation is that the LLMs exhibit dramatic performanc…
Order Doesn't Matter, But Reasoning Does: Training LLMs with Order-Centric Augmentation
Qianxi He, Qianyu He, Jiaqing Liang +4
Logical reasoning is essential for large language models (LLMs) to ensure accurate and coherent inference. However, LLMs struggle with reasoning order variations and fail to genera…
CDS: Knowledge Component-Driven Data Synthesis Guided by Cognitive Diagnosis Theory
Haokun Zhao, Jinyi Han, Jiaqing Liang +3
Large Language Models (LLMs) have achieved significant advancements, but the increasing complexity of tasks and higher performance demands highlight the need for continuous improve…
Step-by-Step Mastery: Enhancing Soft Constraint Following Ability of Large Language Models
Qingyu Ren, Jie Zeng, Qianyu He +5
It is crucial for large language models (LLMs) to follow instructions that involve multiple constraints. However, it is an unexplored area to enhance LLMs' ability to follow soft c…
MultiLingPoT: Enhancing Mathematical Reasoning with Multilingual Program Fine-tuning
Nianqi Li, Zujie Liang, Siyu Yuan +3
Program-of-Thought (PoT), which aims to use programming language instead of natural language as an intermediate step in reasoning, is an important way for LLMs to solve mathematica…
QUILL: Quotation Generation Enhancement of Large Language Models
Jin Xiao, Bowei Zhang, Qianyu He +6
While Large language models (LLMs) have become excellent writing assistants, they still struggle with quotation generation. This is because they either hallucinate when providing f…
SEGMENT+: Long Text Processing with Short-Context Language Models
Wei Shi, Shuang Li, Kerun Yu +9
There is a growing interest in expanding the input capacity of language models (LMs) across various domains. However, simply increasing the context window does not guarantee robust…
Past Meets Present: Creating Historical Analogy with Large Language Models
Nianqi Li, Siyu Yuan, Jiangjie Chen +5
Historical analogies, which compare known past events with contemporary but unfamiliar events, are important abilities that help people make decisions and understand the world. How…