4 citations · 16 across the 29 of their papers we have counts for
30 papers
Why It Hurts: Identifying the Drivers of Negative Thoughts in Emotional Support Conversations
Hainiu Xu, Zhaoyue Sun, Hanqi Yan +3
Large Language Models (LLMs) are increasingly used for emotional support tasks, such as negative thought reframing. This task relies on modifying cognitive appraisals, the subjecti…
Linear Probing Provides Robust and Efficient Detection of Machine-Generated Text
Gerrit Quaremba, Hanqi Yan, Elizabeth Black +2
Distinguishing machine-generated text (MGT) from human-written text (HWT) becomes increasingly important due to potential misuse. However, most supervised detectors often degrade o…
GRAFT: Graph-Distilled Generative Retrieval for Facet-Aware Scientific Literature Exploration
Italo Luis da Silva, Hanqi Yan, Yujing Wang +3
Scientific papers may relate by problem, method, result, or contribution, but document-level retrievers collapse these into a single similarity score without saying why they are re…
Large Language Models Hack Rewards, and Society
Wei Liu, Xinyi Mou, Hanqi Yan +2
Reinforcement learning (RL) has become a dominant post-training paradigm, enabling large language models (LLMs) to learn from rewards. We observe that societal regulations are stru…
Beyond the Literal: Decomposing Pragmatic Intent in Multimodal Meme Understanding
Zhengyi Zhao, Shubo Zhang, Zezhong Wang +6
When asked what a meme or sarcastic post means, Large Vision Language Models (LVLMs) tend to describe what the image shows rather than what the author is trying to communicate. Sta…
GRADE: Probing Knowledge Gaps in LLMs through Gradient Subspace Dynamics
Yujing Wang, Yuanbang Liang, Yukun Lai +2
Detecting whether a model's internal knowledge is sufficient to correctly answer a given question is a fundamental challenge in deploying responsible LLMs. In addition to verbalisi…
Stop the Flip-Flop: Context-Preserving Verification for Fast Revocable Diffusion Decoding
Yanzheng Xiang, Lan Wei, Yizhen Yao +8
Parallel diffusion decoding can accelerate diffusion language model inference by unmasking multiple tokens per step, but aggressive parallelism often harms quality. Revocable decod…
Fix the Structural Bottleneck: Context Compression via Explicit Information Transmission
Jiangnan Ye, Hanqi Yan, Zhenyi Shen +3
Long-context LLM agents often struggle with growing token, memory, and latency costs, making efficient context compression essential for practical deployment. Existing LLM-as-a-com…
Beyond RAG for Agent Memory: Retrieval by Decoupling and Aggregation
Zhanghao Hu, Qinglin Zhu, Runcong Zhao +4
Standard Retrieval Augmented Generation (RAG) is poorly matched to agent memory. Unlike large heterogeneous corpora, agent memory forms a bounded and coherent interaction stream in…
GraphMind: Interactive Novelty Assessment System for Accelerating Scientific Discovery
Italo Luis da Silva, Hanqi Yan, Lin Gui +1
Large Language Models (LLMs) show strong reasoning and text generation capabilities, prompting their use in scientific literature analysis, including novelty assessment. While eval…
When Thinking Backfires: Mechanistic Insights Into Reasoning-Induced Misalignment
Hanqi Yan, Hainiu Xu, Siya Qi +2
With the growing accessibility and wide adoption of large language models, concerns about their safety and alignment with human values have become paramount. In this paper, we iden…
Beyond Perplexity: Let the Reader Select Retrieval Summaries via Spectrum Projection Score
Zhanghao Hu, Qinglin Zhu, Siya Qi +3
Large Language Models (LLMs) have shown improved generation performance through retrieval-augmented generation (RAG) following the retriever-reader paradigm, which supplements mode…
Soft Reasoning: Navigating Solution Spaces in Large Language Models through Controlled Embedding Exploration
Qinglin Zhu, Runcong Zhao, Hanqi Yan +3
Large Language Models (LLMs) struggle with complex reasoning due to limited diversity and inefficient search. We propose Soft Reasoning, an embedding-based search framework that op…
SciReplicate-Bench: Benchmarking LLMs in Agent-driven Algorithmic Reproduction from Research Papers
Yanzheng Xiang, Hanqi Yan, Shuyin Ouyang +2
This study evaluates large language models (LLMs) in generating code from algorithm descriptions in recent NLP papers. The task requires two key competencies: (1) algorithm compreh…
Beyond Prompting: An Efficient Embedding Framework for Open-Domain Question Answering
Zhanghao Hu, Hanqi Yan, Qinglin Zhu +3
Large language models have recently pushed open domain question answering (ODQA) to new frontiers. However, prevailing retriever-reader pipelines often depend on multiple rounds of…
CODI: Compressing Chain-of-Thought into Continuous Space via Self-Distillation
Zhenyi Shen, Hanqi Yan, Linhai Zhang +3
Chain-of-Thought (CoT) reasoning enhances Large Language Models (LLMs) by encouraging step-by-step reasoning in natural language. However, leveraging a latent continuous space for…
Beyond Cross-Modal Alignment: Measuring and Leveraging Modality Gap in Vision-Language Models
Hanqi Yan, Xiangxiang Cui, Lu Yin +4
The success of vision-language models is primarily attributed to effective alignment across modalities such as vision and language. However, modality gaps persist in existing align…
Constrain Alignment with Sparse Autoencoders
Qingyu Yin, Chak Tou Leong, Minjun Zhu +7
The alignment of large language models (LLMs) with human preferences remains a key challenge. While post-training techniques like Reinforcement Learning from Human Feedback (RLHF)…
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…
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…
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…