Papers by Zhiwen Luo
G2: Guided Generation for Enhanced Output Diversity in LLMs (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to enhance output diversity but compromise quality of outputs. |
| Approach: | They propose a training-free plug-and-play method that enhances output diversity while preserving generation quality. |
| Outcome: | The proposed method enhances output diversity while maintaining an optimal balance between diversity and quality. |
Zero-Shot Cross-Domain Dialogue State Tracking via Dual Low-Rank Adaptation (2024.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to zero-shot dialogue state tracking (DST) involve embedding prompts into language models, but these methods have inherent limitations. |
| Approach: | They propose a plug-and-play architecture designed for zero-shot dialogue state tracking (DST) dual low-rank adaptation targets dialogue context processing and prompt optimization without incurring additional inference latency. |
| Outcome: | The proposed architecture outperforms baseline methods on multi-domain datasets and the MultiWOZ dataset. |
DuetSim: Building User Simulator with Dual Large Language Models for Task-Oriented Dialogues (2024.lrec-main)
Copied to clipboard
| Challenge: | User Simulators are used to train task-oriented dialogue systems . traditional training paradigms rely on human-engineered agendas resulting in generated responses that lack diversity and spontaneity. |
| Approach: | They propose a framework that leverages large language models to generate diverse responses . they use two LLMs to generate and verify responses, which are preferred by users . |
| Outcome: | The proposed framework produces responses that exhibit diversity and are preferred by human users. |
Latent Attention Denoising: A Training-Free Energy-Based Framework for Mitigating Hallucinations in Vision-Language Models (2026.acl-long)
Copied to clipboard
| Challenge: | Existing models for large vision-Language Models lack reliable visual hallucination . despite advances in multimodal perception, they are still limited in real-world applications . |
| Approach: | They propose a framework that recasts attention calibration as a one-step score-based denoising process. |
| Outcome: | The proposed framework achieves superior performance on generative and discriminative tasks while maintaining efficiency comparable to standard decoding. |
LayAlign: Enhancing Multilingual Reasoning in Large Language Models via Layer-Wise Adaptive Fusion and Alignment Strategy (2025.findings-naacl)
Copied to clipboard
| Challenge: | Large language models (LLMs) are pretrained on multilingual corpora but exhibit suboptimal performance on low-resource languages. |
| Approach: | They propose a framework that integrates representations from all encoder layers and an adaptive fusion-enhanced attention mechanism to enable layer-wise interaction between the LLM and the multilingual encoder. |
| Outcome: | Experiments on multilingual reasoning tasks show that the proposed framework outperforms baselines. |