13 citations · 43 across the 29 of their papers we have counts for
30 papers
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…
CL-bench Life: Can Language Models Learn from Real-Life Context?
Shihan Dou, Yujiong Shen, Chenhao Huang +35
Today's AI assistants such as OpenClaw are designed to handle context effectively, making context learning an increasingly important capability for models. As these systems move be…
RubricEval: A Rubric-Level Meta-Evaluation Benchmark for LLM Judges in Instruction Following
Tianjun Pan, Xuan Lin, Wenyan Yang +7
Rubric-based evaluation has become a prevailing paradigm for evaluating instruction following in large language models (LLMs). Despite its widespread use, the reliability of these…
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…
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…
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…
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…
ChemAmp: Amplified Chemistry Tools via Composable Agents
Zhucong Li, Powei Chang, Jin Xiao +6
Although LLM-based agents are proven to master tool orchestration in scientific fields, particularly chemistry, their single-task performance remains limited by underlying tool con…
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…
ChemHTS: Hierarchical Tool Stacking for Enhancing Chemical Agents
Zhucong Li, Jin Xiao, Bowei Zhang +5
Large Language Models (LLMs) have demonstrated remarkable potential in scientific research, particularly in chemistry-related tasks such as molecular design, reaction prediction, a…
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…
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…
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…
Think Thrice Before You Act: Progressive Thought Refinement in Large Language Models
Chengyu Du, Jinyi Han, Yizhou Ying +9
Recent advancements in large language models (LLMs) have demonstrated that progressive refinement, rather than providing a single answer, results in more accurate and thoughtful ou…
Light Up the Shadows: Enhance Long-Tailed Entity Grounding with Concept-Guided Vision-Language Models
Yikai Zhang, Qianyu He, Xintao Wang +3
Multi-Modal Knowledge Graphs (MMKGs) have proven valuable for various downstream tasks. However, scaling them up is challenging because building large-scale MMKGs often introduces…
Reason from Fallacy: Enhancing Large Language Models' Logical Reasoning through Logical Fallacy Understanding
Yanda Li, Dixuan Wang, Jiaqing Liang +4
Large Language Models (LLMs) have demonstrated good performance in many reasoning tasks, but they still struggle with some complicated reasoning tasks including logical reasoning.…
From Complex to Simple: Enhancing Multi-Constraint Complex Instruction Following Ability of Large Language Models
Qianyu He, Jie Zeng, Qianxi He +2
It is imperative for Large language models (LLMs) to follow instructions with elaborate requirements (i.e. Complex Instructions Following). Yet, it remains under-explored how to en…
Is There a One-Model-Fits-All Approach to Information Extraction? Revisiting Task Definition Biases
Wenhao Huang, Qianyu He, Zhixu Li +2
Definition bias is a negative phenomenon that can mislead models. Definition bias in information extraction appears not only across datasets from different domains but also within…
Laying the Foundation First? Investigating the Generalization from Atomic Skills to Complex Reasoning Tasks
Yuncheng Huang, Qianyu He, Yipei Xu +2
Current language models have demonstrated their capability to develop basic reasoning, but struggle in more complicated reasoning tasks that require a combination of atomic skills,…
Small Language Model Can Self-correct
Haixia Han, Jiaqing Liang, Jie Shi +2
Generative Language Models (LMs) such as ChatGPT have exhibited remarkable performance across various downstream tasks. Nevertheless, one of their most prominent drawbacks is gener…
Enhancing Quantitative Reasoning Skills of Large Language Models through Dimension Perception
Yuncheng Huang, Qianyu He, Jiaqing Liang +3
Quantities are distinct and critical components of texts that characterize the magnitude properties of entities, providing a precise perspective for the understanding of natural la…
Can Large Language Models Understand Real-World Complex Instructions?
Qianyu He, Jie Zeng, Wenhao Huang +14
Large language models (LLMs) can understand human instructions, showing their potential for pragmatic applications beyond traditional NLP tasks. However, they still struggle with c…
KnowledGPT: Enhancing Large Language Models with Retrieval and Storage Access on Knowledge Bases
Xintao Wang, Qianwen Yang, Yongting Qiu +5
Large language models (LLMs) have demonstrated impressive impact in the field of natural language processing, but they still struggle with several issues regarding, such as complet…
HAUSER: Towards Holistic and Automatic Evaluation of Simile Generation
Qianyu He, Yikai Zhang, Jiaqing Liang +3
Similes play an imperative role in creative writing such as story and dialogue generation. Proper evaluation metrics are like a beacon guiding the research of simile generation (SG…