Papers by Yiran Zhao
Event-Radar: Event-driven Multi-View Learning for Multimodal Fake News Detection (2024.acl-long)
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| Challenge: | Existing methods for detecting multimedia fake news have demonstrated excellent results . however, addressing event-level inconsistency and learning from poor-quality news remains a challenge . |
| Approach: | They propose an Event-diven fake news detection framework that integrates visual manipulation, textual emotion and multimodal inconsistency at event-level for fake news identification. |
| Outcome: | The proposed framework performs well on three large-scale fake news detection benchmarks. |
What Works and Doesn’t Work, A Deep Decoder for Neural Machine Translation (2022.findings-acl)
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| Challenge: | Deep learning has demonstrated performance advantages in a wide range of natural language processing tasks. |
| Approach: | They propose to deepen the decoder layer in a Transformer model to reduce the difficulty of deep learning. |
| Outcome: | The proposed method can deepen the model on both the encoder and decoder at the same time, resulting in a deeper model and improved performance. |
PLAWBENCH: A Rubric-Based Benchmark for Evaluating LLMs in Real-World Legal Practice (2026.acl-long)
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Yuzhen Shi, Huanghai Liu, Yiran HU, Song Gaojie, Xu Xinran, Yubo Ma, Tianyi Tang, Li Zhang, Qingjing Chen, Feng Di, Wenbo Lv, Weiheng Wu, Kexin Yang, Sen Yang, Wei Wang, Rongyao Shi, Qiu Yuanyang, Yuemeng Qi, Zhang Jingwen, Sui Xiaoyu, Yifan Chen, Zhang Yi, An Yang, Bowen Yu, Dayiheng Liu, Junyang Lin, Weixing Shen, Bing Zhao, Charles L. A. Clarke, HU Wei
| Challenge: | Existing benchmarks for large language models (LLMs) are coarse, single-dimensional metrics and do not explicitly assess fine-grained legal reasoning. |
| Approach: | They propose a Practical Law Benchmark to evaluate large language models in real-world legal practice scenarios. |
| Outcome: | The proposed model is based on 850 questions and 13 scenarios with expert-designed evaluation rubrics. |
AdaMergeX: Cross-Lingual Transfer with Large Language Models via Adaptive Adapter Merging (2025.naacl-long)
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| Challenge: | Large Language Models excel in highresource languages but underperform in lowresource ones. |
| Approach: | They propose a cross-lingual transfer method that decouples "task ability" from "language ability" they propose to use adaptive adapter merging to obtain target adapters by combining other adapters. |
| Outcome: | The proposed method outperforms existing methods in highresource languages . it decouples "task ability" from "language ability" but fails to fully separate "task capability" from the "source language" |
Prompt Optimization via Adversarial In-Context Learning (2024.acl-long)
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Do Long, Yiran Zhao, Hannah Brown, Yuxi Xie, James Zhao, Nancy Chen, Kenji Kawaguchi, Michael Shieh, Junxian He
| Challenge: | Existing methods to optimize prompts for in-context learning are based on adversarial learning and are computationally efficient and extensible to other LLMs and tasks. |
| Approach: | They propose a method to optimize prompts for in-context learning by a generator and a discriminator. |
| Outcome: | The proposed method improves state-of-the-art prompt optimization techniques on 13 generation and classification tasks including summarization, arithmetic reasoning, machine translation, data-to-text generation, and the MMLU and big-bench hard benchmarks. |
Disentangling Language and Culture for Evaluating Multilingual Large Language Models (2025.acl-long)
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| Challenge: | Extensive evaluations of large language models (LLMs) are conducted on a wide range of models, revealing a notable cultural-linguistic synergy phenomenon, where models exhibit better performance when questions are culturally aligned with the language. |
| Approach: | They propose a Dual Evaluation Framework to comprehensively assess the multilingual capabilities of large language models by decomposing evaluation along dimensions of linguistic medium and cultural context. |
| Outcome: | The proposed framework allows for a nuanced analysis of LLMs’ ability to process questions within both native and cross-cultural contexts cross-lingually. |
KM-BART: Knowledge Enhanced Multimodal BART for Visual Commonsense Generation (2021.acl-long)
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| Challenge: | Existing models for visual and language understanding are not capable of multimodal reasoning. |
| Approach: | They propose a Transformer-based sequence-to-sequence model capable of reasoning about commonsense knowledge from multimodal inputs of images and texts. |
| Outcome: | The proposed model performs state-of-the-art on the Visual Commonsense Generation task. |
Reasoning Robustness of LLMs to Adversarial Typographical Errors (2024.emnlp-main)
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Esther Gan, Yiran Zhao, Liying Cheng, Mao Yancan, Anirudh Goyal, Kenji Kawaguchi, Min-Yen Kan, Michael Shieh
| Challenge: | Large Language Models (LLMs) have demonstrated impressive capabilities in reasoning using Chain-of-Thought (CoT) prompting. |
| Approach: | They develop an algorithm that iteratively samples typos for words that are important to the query and selects the edit that is most likely to succeed in attacking. |
| Outcome: | The proposed algorithm detects typographical errors in large and closed-source LLMs and shows that they are robust to them. |
Is Translation All You Need? A Study on Solving Multilingual Tasks with Large Language Models (2025.naacl-long)
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| Challenge: | Large language models (LLMs) have demonstrated multilingual capabilities, yet they are mostly English-centric due to the imbalanced training corpora. |
| Approach: | They extend the evaluation to real-world user queries and non-English-centric LLMs . they show that translation into English can boost LLM performance on NLP tasks . |
| Outcome: | The proposed evaluation extends to user queries and non-English-centric LLMs . it shows that translation into English can boost performance on NLP tasks, but not universally optimal . |
Pruning General Large Language Models into Customized Expert Models (2025.findings-acl)
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| Challenge: | Large language models (LLMs) require significant computational resources to maintain their general capabilities. |
| Approach: | They propose a Custom Pruning method to prune a large general model into a smaller lightweight expert model, positioned along the "language", "domain" and "task" dimensions. |
| Outcome: | The proposed method outperforms existing pruning methods and achieves minimal loss in both expert and general capabilities across models from different model families and sizes. |