4 citations · 27 across the 23 of their papers we have counts for
24 papers
Follow the Latent Roadmap: Navigating Revocable Decoding for Diffusion LLMs with Anchor Tokens
Yizhen Yao, Qinglin Zhu, Runcong Zhao +4
Diffusion Large Language Models (dLLMs) offer a promising avenue for parallel generation but face a trade-off between decoding speed and quality. While revocable decoding strategie…
Detecting Contextual Hallucinations in LLMs with Frequency-Aware Attention
Siya Qi, Yudong Chen, Runcong Zhao +6
Hallucination detection is critical for ensuring the reliability of large language models (LLMs) in context-based generation. Prior work has explored intrinsic signals available du…
Beyond Static Cropping: Layer-Adaptive Visual Localization and Decoding Enhancement
Zipeng Zhu, Zhanghao Hu, Qinglin Zhu +5
Large Vision-Language Models (LVLMs) have advanced rapidly by aligning visual patches with the text embedding space, but a fixed visual-token budget forces images to be resized to…
Pull Requests as a Training Signal for Repo-Level Code Editing
Qinglin Zhu, Tianyu Chen, Shuai Lu +8
Repository-level code editing requires models to understand complex dependencies and execute precise multi-file modifications across a large codebase. While recent gains on SWE-ben…
Latent Refinement Decoding: Enhancing Diffusion-Based Language Models by Refining Belief States
Qinglin Zhu, Yizhen Yao, Runcong Zhao +7
Autoregressive (AR) models remain the standard for natural language generation but still suffer from high latency due to strictly sequential decoding. Recent diffusion-inspired app…
SymbolicThought: Integrating Language Models and Symbolic Reasoning for Consistent and Interpretable Human Relationship Understanding
Runcong Zhao, Qinglin Zhu, Hainiu Xu +3
Understanding character relationships is essential for interpreting complex narratives and conducting socially grounded AI research. However, manual annotation is time-consuming an…
LearnLens: LLM-Enabled Personalised, Curriculum-Grounded Feedback with Educators in the Loop
Runcong Zhao, Artem Bobrov, Jiazheng Li +2
Effective feedback is essential for student learning but is time-intensive for teachers. We present LearnLens, a modular, LLM-based system that generates personalised, curriculum-a…
PECAN: LLM-Guided Dynamic Progress Control with Attention-Guided Hierarchical Weighted Graph for Long-Document QA
Xinyu Wang, Yanzheng Xiang, Lin Gui +1
Long-document QA presents challenges with large-scale text and long-distance dependencies. Recent advances in Large Language Models (LLMs) enable entire documents to be processed i…
Weak Reward Model Transforms Generative Models into Robust Causal Event Extraction Systems
Italo Luis da Silva, Hanqi Yan, Lin Gui +1
The inherent ambiguity of cause and effect boundaries poses a challenge in evaluating causal event extraction tasks. Traditional metrics like Exact Match and BertScore poorly refle…
Encourage or Inhibit Monosemanticity? Revisit Monosemanticity from a Feature Decorrelation Perspective
Hanqi Yan, Yanzheng Xiang, Guangyi Chen +3
To better interpret the intrinsic mechanism of large language models (LLMs), recent studies focus on monosemanticity on its basic units. A monosemantic neuron is dedicated to a sin…
PLAYER*: Enhancing LLM-based Multi-Agent Communication and Interaction in Murder Mystery Games
Qinglin Zhu, Runcong Zhao, Bin Liang +3
We introduce WellPlay, a reasoning dataset for multi-agent conversational inference in Murder Mystery Games (MMGs). WellPlay comprises 1,482 inferential questions across 12 games,…
Scene Graph Aided Radiology Report Generation
Jun Wang, Lixing Zhu, Abhir Bhalerao +1
Radiology report generation (RRG) methods often lack sufficient medical knowledge to produce clinically accurate reports. The scene graph contains rich information to describe the…
Counterfactual Generation with Identifiability Guarantees
Hanqi Yan, Lingjing Kong, Lin Gui +4
Counterfactual generation lies at the core of various machine learning tasks, including image translation and controllable text generation. This generation process usually requires…
Addressing Order Sensitivity of In-Context Demonstration Examples in Causal Language Models
Yanzheng Xiang, Hanqi Yan, Lin Gui +1
In-context learning has become a popular paradigm in natural language processing. However, its performance can be significantly influenced by the order of in-context demonstration…
Mirror: A Multiple-perspective Self-Reflection Method for Knowledge-rich Reasoning
Hanqi Yan, Qinglin Zhu, Xinyu Wang +2
While Large language models (LLMs) have the capability to iteratively reflect on their own outputs, recent studies have observed their struggles with knowledge-rich problems withou…
Towards Unified Task Embeddings Across Multiple Models: Bridging the Gap for Prompt-Based Large Language Models and Beyond
Xinyu Wang, Hainiu Xu, Lin Gui +1
Task embedding, a meta-learning technique that captures task-specific information, has gained popularity, especially in areas such as multi-task learning, model editing, and interp…
COPR: Continual Human Preference Learning via Optimal Policy Regularization
Han Zhang, Lin Gui, Yu Lei +8
Reinforcement Learning from Human Feedback (RLHF) is commonly utilized to improve the alignment of Large Language Models (LLMs) with human preferences. Given the evolving nature of…
OpenToM: A Comprehensive Benchmark for Evaluating Theory-of-Mind Reasoning Capabilities of Large Language Models
Hainiu Xu, Runcong Zhao, Lixing Zhu +2
Neural Theory-of-Mind (N-ToM), machine's ability to understand and keep track of the mental states of others, is pivotal in developing socially intelligent agents. However, prevale…
The Mystery of In-Context Learning: A Comprehensive Survey on Interpretation and Analysis
Yuxiang Zhou, Jiazheng Li, Yanzheng Xiang +3
Understanding in-context learning (ICL) capability that enables large language models (LLMs) to excel in proficiency through demonstration examples is of utmost importance. This im…
Are NLP Models Good at Tracing Thoughts: An Overview of Narrative Understanding
Lixing Zhu, Runcong Zhao, Lin Gui +1
Narrative understanding involves capturing the author's cognitive processes, providing insights into their knowledge, intentions, beliefs, and desires. Although large language mode…
A Scalable Framework for Table of Contents Extraction from Complex ESG Annual Reports
Xinyu Wang, Lin Gui, Yulan He
Table of contents (ToC) extraction centres on structuring documents in a hierarchical manner. In this paper, we propose a new dataset, ESGDoc, comprising 1,093 ESG annual reports f…
NarrativePlay: Interactive Narrative Understanding
Runcong Zhao, Wenjia Zhang, Jiazheng Li +4
In this paper, we introduce NarrativePlay, a novel system that allows users to role-play a fictional character and interact with other characters in narratives such as novels in an…
Can Prompt Learning Benefit Radiology Report Generation?
Jun Wang, Lixing Zhu, Abhir Bhalerao +1
Radiology report generation aims to automatically provide clinically meaningful descriptions of radiology images such as MRI and X-ray. Although great success has been achieved in…
OverPrompt: Enhancing ChatGPT through Efficient In-Context Learning
Jiazheng Li, Runcong Zhao, Yongxin Yang +2
The remarkable performance of pre-trained large language models has revolutionised various natural language processing applications. Due to huge parametersizes and extensive runnin…