24 citations · 35 across the 16 of their papers we have counts for
17 papers
TRAJDEBUG: Tracing Error Lifecycle to Identify Critical Failures in Long-Horizon Agent Trajectories
Yunjia Qi, Zehua Yin, Xintong Shi +10
LLM-based agentic systems have shown remarkable capabilities in complex domains, while suffering from cascading errors and difficulty in debugging. Critical error detection aims to…
Can LLM-as-a-Judge Reliably Verify Rubrics in Agentic Scenarios?
Yangda Peng, Yunjia Qi, Haotian Xia +8
Rubric-based scoring has become a widely used paradigm in model evaluation, typically with LLM-as-a-Judge (LaaJ) for rubric scoring. However, the reliability of LaaJ for rubric sco…
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…
WildReward: Learning Reward Models from In-the-Wild Human Interactions
Hao Peng, Yunjia Qi, Xiaozhi Wang +3
Reward models (RMs) are crucial for the training of large language models (LLMs), yet they typically rely on large-scale human-annotated preference pairs. With the widespread deplo…
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…
Evaluating Hydro-Science and Engineering Knowledge of Large Language Models
Shiruo Hu, Wenbo Shan, Yingjia Li +16
Hydro-Science and Engineering (Hydro-SE) is a critical and irreplaceable domain that secures human water supply, generates clean hydropower energy, and mitigates flood and drought…
LoSiA: Efficient High-Rank Fine-Tuning via Subnet Localization and Optimization
Xujia Wang, Yunjia Qi, Bin Xu
Parameter-Efficient Fine-Tuning (PEFT) methods, such as LoRA, significantly reduce the number of trainable parameters by introducing low-rank decomposition matrices. However, exist…
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…
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…
Constraint Back-translation Improves Complex Instruction Following of Large Language Models
Yunjia Qi, Hao Peng, Xiaozhi Wang +3
Large language models (LLMs) struggle to follow instructions with complex constraints in format, length, etc. Following the conventional instruction-tuning practice, previous works…
MAVEN-Fact: A Large-scale Event Factuality Detection Dataset
Chunyang Li, Hao Peng, Xiaozhi Wang +4
Event Factuality Detection (EFD) task determines the factuality of textual events, i.e., classifying whether an event is a fact, possibility, or impossibility, which is essential f…
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…
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…
When does In-context Learning Fall Short and Why? A Study on Specification-Heavy Tasks
Hao Peng, Xiaozhi Wang, Jianhui Chen +8
In-context learning (ICL) has become the default method for using large language models (LLMs), making the exploration of its limitations and understanding the underlying causes cr…
KoLA: Carefully Benchmarking World Knowledge of Large Language Models
Jifan Yu, Xiaozhi Wang, Shangqing Tu +32
The unprecedented performance of large language models (LLMs) necessitates improvements in evaluations. Rather than merely exploring the breadth of LLM abilities, we believe meticu…