Papers by Meng Xi
RadEval: A framework for radiology text evaluation (2025.emnlp-demos)
Copied to clipboard
Justin Xu, Xi Zhang, Javid Abderezaei, Julie Bauml, Roger Boodoo, Fatemeh Haghighi, Ali Ganjizadeh, Eric Brattain, Dave Van Veen, Zaiqiao Meng, David W Eyre, Jean-Benoit Delbrouck
| Challenge: | Evaluating automated radiology report generation systems remains a fundamental challenge in the development of safe, accurate, and clinically useful medical AI. |
| Approach: | They propose a unified, open-source framework for evaluating radiology texts that consolidates a diverse range of metrics from classic ngram overlap (BLEU) and contextual measures (BERTScore) to clinical concept-based scores (GREEN). |
| Outcome: | The framework consolidates a diverse range of metrics from ngram overlap (BLEU) and contextual measures (BERTScore) to clinical concept-based scores (F1CheXbert, F1RadGraph, RaTEScore, SRR-BERT, TemporalEntityF1) and advanced LLMbased evaluators (GREEN). |
The Efficiency vs. Accuracy Trade-off: Optimizing RAG-Enhanced LLM Recommender Systems Using Multi-Head Early Exit (2025.acl-long)
Copied to clipboard
Huixue Zhou, Hengrui Gu, Zaifu Zhan, Xi Liu, Kaixiong Zhou, Yongkang Xiao, Mingfu Liang, Srinivas Prasad Govindan, Piyush Chawla, Jiyan Yang, Xiangfei Meng, Huayu Li, Buyun Zhang, Liang Luo, Wen-Yen Chen, Yiping Han, Bo Long, Rui Zhang, Tianlong Chen
| Challenge: | Existing frameworks for Large Language Models (LLMs) for Click-Through Rate prediction require a careful balance between computational efficiency and predictive accuracy. |
| Approach: | They propose a framework that integrates Retrieval-Augmented Generation with a novel multi-head early exit architecture to address both challenges. |
| Outcome: | The proposed framework reduces retrieval time while maintaining high model performance. |
CCD: Mitigating Hallucinations in Radiology MLLMs via Clinical Contrastive Decoding (2026.findings-acl)
Copied to clipboard
| Challenge: | Multimodal large language models generate medical hallucinations due to over-sensitivity to clinical sections. |
| Approach: | They propose a framework that integrates structured clinical signals from task-specific radiology expert models. |
| Outcome: | The proposed framework improves overall performance on radiology report generation (RRG) on the MIMIC-CXR dataset, it yields up to 17% improvement in RadGraph-F1. |
E-ConvRec: A Large-Scale Conversational Recommendation Dataset for E-Commerce Customer Service (2022.lrec-1)
Copied to clipboard
Meihuizi Jia, Ruixue Liu, Peiying Wang, Yang Song, Zexi Xi, Haobin Li, Xin Shen, Meng Chen, Jinhui Pang, Xiaodong He
| Challenge: | Recent research has focused on developing conversational recommendation system (CRS), which provides valuable recommendations to users through conversations. |
| Approach: | They construct an authentic Chinese dialogue dataset consisting of over 25k dialogues and 770k utterances, which contains user profile, product knowledge base, and multiple sequential real conversations between users and recommenders. |
| Outcome: | The proposed dataset contains user profile, product knowledge base, and multiple sequential real conversations between users and recommenders. |
TriSPrompt: A Hierarchical Soft Prompt Model for Multimodal Rumor Detection with Incomplete Modalities (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing multimodal rumor detection methods focus on learning joint modality representations from complete multimodal training data, rendering them ineffective in addressing the common occurrence of missing modalities in real-world scenarios. |
| Approach: | They propose a hierarchical soft prompt model TriSPrompt which integrates three types of prompts to effectively detect rumors in incomplete multimodal data. |
| Outcome: | The proposed model achieves an accuracy gain of over 13% compared to state-of-the-art models. |
Libra: Leveraging Temporal Images for Biomedical Radiology Analysis (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for radiology report generation rely on single-image analysis or rule-based heuristics to process multiple images. |
| Approach: | They propose a temporal-aware MLLM tailored for chest X-ray report generation that combines a radiology-specific image encoder with a novel Temporal Alignment Connector. |
| Outcome: | The proposed model sets new standards in clinical relevance and lexical accuracy on the MIMIC-CXR dataset. |
RIPRAG: Hack a Black-box Retrieval-Augmented Generation Question-Answering System with Reinforcement Learning (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing methods to generate RAG documents require knowledge of the target RAG system’s internal composition and implementation details, whereas black-box methods are unable to utilize interactive information. |
| Approach: | They propose a RIPRAG attack framework that treats the target RAG system as a black box and leverages a Reinforcement Learning from Black-box Feedback (RLBF) method to optimize the generation model for poisoned documents. |
| Outcome: | The proposed method achieves an attack success rate (ASR) improvement of up to 0.72 compared to baseline methods. |
SAME: Signer-Aware Mixture-of-Experts for Test-Time Adaptation in Sign Language Translation (2026.acl-long)
Copied to clipboard
| Challenge: | Existing methods for supervised fine-tuning are limited due to labeled data . existing methods require long adaptation times and batch statistics are unavailable in streaming settings . |
| Approach: | They propose a plug-and-play, signer-aware Mixture-of-Experts (MoE) TTA architecture for SLT . they use a combination of lightweight MoE modules and unsupervised regularizers to decouple domain shift . |
| Outcome: | The proposed test-time adaptation outperforms existing TTA methods in sign language translation . the proposed architecture can be used in real-world deployments without labeling . |