80 citations · 297 across the 46 of their papers we have counts for
48 papers
AutoSFT: An Agentic System for Automated Data Research in Supervised Fine-Tuning
Bingxiang He, Pengqi Sun, Shihao Peng +18
Language model agents increasingly automate post-training, but when given the full training stack they converge on supervised fine-tuning and spend most effort revising training da…
Reproducing, Analyzing, and Detecting Reward Hacking in Rubric-Based Reinforcement Learning
Xuekang Wang, Zhuoyuan Hao, Shuo Hou +3
Rubric-based reinforcement learning (RL) uses an LLM-as-a-Judge (LaaJ) to score model outputs according to rubrics as rewards. However, policy models may exploit latent biases in t…
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…
Auxiliary Metrics Help Decoding Skill Neurons in the Wild
Yixiu Zhao, Xiaozhi Wang, Zijun Yao +2
Large language models (LLMs) exhibit remarkable capabilities across a wide range of tasks, yet their internal mechanisms remain largely opaque. In this paper, we introduce a simple…
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…
LinguaLens: Towards Interpreting Linguistic Mechanisms of Large Language Models via Sparse Auto-Encoder
Yi Jing, Zijun Yao, Hongzhu Guo +4
Large language models (LLMs) demonstrate exceptional performance on tasks requiring complex linguistic abilities, such as reference disambiguation and metaphor recognition/generati…
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…
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…
Configurable Foundation Models: Building LLMs from a Modular Perspective
Chaojun Xiao, Zhengyan Zhang, Chenyang Song +20
Advancements in LLMs have recently unveiled challenges tied to computational efficiency and continual scalability due to their requirements of huge parameters, making the applicati…
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…
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…
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…
Towards Understanding Safety Alignment: A Mechanistic Perspective from Safety Neurons
Jianhui Chen, Xiaozhi Wang, Zijun Yao +3
Large language models (LLMs) excel in various capabilities but pose safety risks such as generating harmful content and misinformation, even after safety alignment. In this paper,…
TacoERE: Cluster-aware Compression for Event Relation Extraction
Yong Guan, Xiaozhi Wang, Lei Hou +4
Event relation extraction (ERE) is a critical and fundamental challenge for natural language processing. Existing work mainly focuses on directly modeling the entire document, whic…
Event GDR: Event-Centric Generative Document Retrieval
Yong Guan, Dingxiao Liu, Jinchen Ma +4
Generative document retrieval, an emerging paradigm in information retrieval, learns to build connections between documents and identifiers within a single model, garnering signifi…
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…
MAVEN-Arg: Completing the Puzzle of All-in-One Event Understanding Dataset with Event Argument Annotation
Xiaozhi Wang, Hao Peng, Yong Guan +9
Understanding events in texts is a core objective of natural language understanding, which requires detecting event occurrences, extracting event arguments, and analyzing inter-eve…
Mastering the Task of Open Information Extraction with Large Language Models and Consistent Reasoning Environment
Ji Qi, Kaixuan Ji, Xiaozhi Wang +5
Open Information Extraction (OIE) aims to extract objective structured knowledge from natural texts, which has attracted growing attention to build dedicated models with human expe…
OmniEvent: A Comprehensive, Fair, and Easy-to-Use Toolkit for Event Understanding
Hao Peng, Xiaozhi Wang, Feng Yao +5
Event understanding aims at understanding the content and relationship of events within texts, which covers multiple complicated information extraction tasks: event detection, even…