Papers by Mengdi Li
Quantifying and Understanding Uncertainty in Large Reasoning Models (2026.acl-long)
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| Challenge: | Existing methods for estimating generation uncertainty do not provide finite-sample guarantees for reasoning-answer generation. |
| Approach: | They propose a method that provides the uncertainty of the reasoning-answer structure with statistical guarantees. |
| Outcome: | The proposed method disentangles reasoning quality from answer correctness while establishing theoretical guarantees for efficient explanation methods. |
Curriculum-RLAIF: Curriculum Alignment with Reinforcement Learning from AI Feedback (2026.findings-acl)
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| Challenge: | Existing approaches to align large language models with human preferences are limited in generalizability due to distribution shift, preference label noise, and mismatch of challenging samples with model capacity. |
| Approach: | They propose a framework that constructs preference pairs with varying difficulty levels and then produces a specific curriculum for reward model training. |
| Outcome: | The proposed framework improves generalizability of reward models by a significant margin without incurring additional inference costs compared to existing non-curriculum baselines. |
SCCS: Semantics-Consistent Cross-domain Summarization via Optimal Transport Alignment (2023.findings-acl)
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Jielin Qiu, Jiacheng Zhu, Mengdi Xu, Franck Dernoncourt, Trung Bui, Zhaowen Wang, Bo Li, Ding Zhao, Hailin Jin
| Challenge: | Existing methods for multimodal summarization ignore the structure and semantics of the whole video and article. |
| Approach: | They propose a semantic-consistent cross-domain summarization model that extracts features from video and article and uses fusion methods to select representative one. |
| Outcome: | The proposed model produces high-quality multimodal summaries on three MSMO datasets. |
Quantifying Uncertainty in Natural Language Explanations of Large Language Models for Question Answering (2025.findings-emnlp)
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| Challenge: | Large language models (LLMs) have shown strong capabilities, enabling concise, context-aware answers in question answering tasks. |
| Approach: | They propose a framework that provides valid uncertainty guarantees for LLMs . they also propose 'model-agnostic' uncertainty estimation method that maintains valid guarantees even under noise. |
| Outcome: | The proposed method provides valid uncertainty guarantees even under noise. |
ChipSeek: Optimizing Verilog Generation via EDA-Integrated Reinforcement Learning (2026.acl-long)
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Zhirong Chen, Kaiyan Chang, Zhuolin Li, Cangyuan Li, Xinyang He, Chujie Chen, Mengdi Wang, Haobo Xu, Yinhe Han, Huawei Li, Ying Wang
| Challenge: | Existing approaches to optimize Register-Transfer Level (RTL) code fail to simultaneously optimize functional correctness and hardware efficiency metrics such as Power, Performance, and Area (PPA). |
| Approach: | They propose a hierarchical reward based reinforcement learning framework that integrates direct feedback from EDA simulators and synthesis tools into a reward mechanism. |
| Outcome: | The proposed framework integrates direct feedback from EDA simulators and synthesis tools into a hierarchical reward based reinforcement learning framework. |
Fraud-R1 : A Multi-Round Benchmark for Assessing the Robustness of LLM Against Augmented Fraud and Phishing Inducements (2025.findings-acl)
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Shu Yang, Shenzhe Zhu, Zeyu Wu, Keyu Wang, Junchi Yao, Junchao Wu, Lijie Hu, Mengdi Li, Derek F. Wong, Di Wang
| Challenge: | Existing fraud detection benchmarks focus on single-turn classification tasks, failing to capture dynamic nature of real-world fraud attempts. |
| Approach: | They propose a bilingual benchmark to assess LLMs' ability to resist fraud and phishing attacks across five key fraud categories: Fraudulent Services, Impersonation, Phishing Scams, Fake Job Postings, and Online Relationships. |
| Outcome: | The proposed model improves in role-play settings and in e-commerce and recommendation systems. |
CoMave: Contrastive Pre-training with Multi-scale Masking for Attribute Value Extraction (2023.findings-acl)
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Xinnan Guo, Wentao Deng, Yongrui Chen, Yang Li, Mengdi Zhou, Guilin Qi, Tianxing Wu, Dong Yang, Liubin Wang, Yong Pan
| Challenge: | Existing methods to extract product features from unstructured text still suffer from problems . e-commerce platforms are focusing on multi-scale values, which can be confusing . |
| Approach: | They propose a pre-training technique to automatically obtain attribute value pairs from product descriptions to aid e-commerce. |
| Outcome: | The proposed method improves on the existing token-level masking strategy and achieves state-of-the-art on four benchmarks. |
Enhancing Zero-Shot Chain-of-Thought Reasoning in Large Language Models through Logic (2024.lrec-main)
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| Challenge: | Experimental evaluations of large language models demonstrate the efficacy of enhanced reasoning by logic. |
| Approach: | They propose a framework that uses symbolic logic to verify and rectify reasoning steps by steps. |
| Outcome: | The proposed framework improves the zero-shot chain-of-thought reasoning ability of large language models by verifying and rectifying the reasoning steps step by step. |
Understanding the Repeat Curse in Large Language Models from a Feature Perspective (2025.findings-acl)
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| Challenge: | Large language models suffer from repetitive text generation, a phenomenon we refer to as the ”Repeat Curse”. |
| Approach: | They propose a method to induce and analyze the Repeat Curse in large language models by using mechanistic interpretability. |
| Outcome: | The proposed method induces and analyzes the Repeat Curse in large language models using mechanistic interpretability. |
Can Large Language Models Identify Implicit Suicidal Ideation? An Empirical Evaluation (2025.findings-emnlp)
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Tong Li, Shu Yang, Junchao Wu, Jiyao Wei, Lijie Hu, Mengdi Li, Derek F. Wong, Joshua R. Oltmanns, Di Wang
| Challenge: | Existing data on suicidal ideation in private conversations are limited . a new dataset of 1,200 test cases is presented to address this gap . |
| Approach: | They propose a dataset of 1,200 test cases simulating implicit suicidal ideation in private contexts. |
| Outcome: | The proposed dataset includes 1,200 test cases simulating implicit suicidal ideation in dialogue scenarios. |
Can Brain Signals Reveal Inner Alignment with Human Languages? (2023.findings-emnlp)
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| Challenge: | Brain Signals, such as Electroencephalography, and human languages have been explored independently for many downstream tasks, however, the connection between them has not been well explored. |
| Approach: | They introduce a multimodal transformer alignment model to observe coordinated representations between EEG and language. |
| Outcome: | The proposed method achieved an F1-score improvement of 1.7% on ZuCo and 9.3% on Zuco datasets for sentiment analysis, and 7.4% on ZuCO for relation detection. |