Papers by Xuhui Zheng
Enhancing Extractive Question Answering in Multiparty Dialogues with Logical Inference Memory Network (2025.coling-main)
Copied to clipboard
| Challenge: | Existing models for multiparty dialogue question answering (QA) do not consider logical inference relations in multiparty dialogs, leading to suboptimal performance. |
| Approach: | They propose a memory network with logical inference for extractive QA in multiparty dialogues. |
| Outcome: | The proposed model achieves state-of-the-art on Molweni and FriendsQA benchmarks. |
Imperfectly Cooperative Human-AI Interactions: Comparing the Impacts of Human and AI Attributes in Simulated and User Studies (2026.findings-acl)
Copied to clipboard
Myke C. Cohen, Mingqian Zheng, Neel Bhandari, Hsien-Te Kao, Xuhui Zhou, Daniel Nguyen, Laura Cassani, Maarten Sap, Svitlana Volkova
| Challenge: | In simulations, personality traits and AI attributes were comparatively influential, but with actual human subjects, AI attributes – particularly transparency – were much more impactful. |
| Approach: | They compare a purely simulated dataset and a parallel human subjects experiment to examine how human personality traits and AI design characteristics jointly shape interaction outcomes in imperfectly cooperative scenarios. |
| Outcome: | The results show that personality traits and AI attributes are comparatively influential in simulations, but with actual human subjects, they are much more impactful. |
AHVE-CNER: Aligned Hanzi Visual Encoding Enhance Chinese Named Entity Recognition with Multi-Information (2025.coling-main)
Copied to clipboard
| Challenge: | Existing glyph-based models neglect the relationship between pictorial elements and radicals for Named Entity Recognition (NER) tasks. |
| Approach: | They propose a model that integrates multi-source visual and phonetic information of Hanzi . they propose combining pictographic features with radicals to facilitate integration . |
| Outcome: | The proposed model improves performance on benchmark datasets. |
StepSearch: Igniting LLMs Search Ability via Step-Wise Proximal Policy Optimization (2025.emnlp-main)
Copied to clipboard
| Challenge: | Recent work has demonstrated unprecedented capabilities in sophisticated linguistic comprehension and generative tasks. |
| Approach: | They propose a framework for search LLMs that trains with step-wise proximal policy optimization method to improve QA performance. |
| Outcome: | The proposed framework outperforms global-reward benchmarks on multi-hop QA with a stepwise proximal policy optimization method and richer and more detailed intermediate search rewards and token-level process supervision. |
RPD: A Distance Function Between Word Embeddings (2020.acl-srw)
Copied to clipboard
| Challenge: | Existing word embeddings are poorly understood, but little is known about how they differ between different sets of word embeds. |
| Approach: | They propose a metric called Relative Pairwise Inner Product Distance to quantify the distance between different word embeddings. |
| Outcome: | The proposed metric measures the distance between different sets of embeddings and investigates the influence of different training processes and corpora. |