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20212026
most citedPosition Bias Mitigation: A Knowledge-Aware Graph Model for Emotion Cause Extraction

4 citations · 16 across the 29 of their papers we have counts for

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9 papers · 1 filter

cs.HC2026

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…

cs.CL2026

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…

cs.IR2026

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…

cs.LG2026

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…

cs.CL2026

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…

cs.CL2026

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…

cs.CL2026

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…

cs.CL2026

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…

cs.CL2026

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…