Papers by Xiang Fang
Annotations Are Not All You Need: A Cross-modal Knowledge Transfer Network for Unsupervised Temporal Sentence Grounding (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing work on temporal sentence grounding rely on expensive video-query paired annotations . despite this, there are no ground-truth annotations in the current work . |
| Approach: | They propose to use paired video-query and segment boundary annotations to generate temporal sentence grounding without training. |
| Outcome: | The proposed model outperforms existing unsupervised methods and beats supervised ones on two challenging datasets. |
Weakly Supervised Text Classification using Supervision Signals from a Language Model (2022.findings-naacl)
Copied to clipboard
| Challenge: | Existing weakly supervised text classification methods require a large number of annotated data and human annotations are expensive. |
| Approach: | They propose to query a masked language model with cloze style prompts to obtain supervision signals. |
| Outcome: | The proposed method outperforms baseline methods on three datasets by 2%, 4%, and 3%. |
MolTC: Towards Molecular Relational Modeling In Language Models (2024.findings-acl)
Copied to clipboard
Junfeng Fang, Shuai Zhang, Chang Wu, Zhengyi Yang, Zhiyuan Liu, Sihang Li, Kun Wang, Wenjie Du, Xiang Wang
| Challenge: | Molecular Relational Learning (MRL) is a promising way to understand interactions between molecular pairs. |
| Approach: | They propose a novel LLM-based multi-modal framework for molecular interaction modeling following Chain-of-Thought (CoT) theory which integrates graphical information of two molecules in pair. |
| Outcome: | The proposed framework integrates graphical information of two molecules in pair. |
Act-Adaptive Margin: Dynamically Calibrating Reward Models for Subjective Ambiguity (2026.acl-long)
Copied to clipboard
Feiteng Fang, Dingwei Chen, Xiang Huang, Ting-En Lin, Yuchuan Wu, Xiong Liu, Jing Ye, Ziqiang Liu, Haonan Zhang, Liang Zhu, Hamid Alinejad-Rokny, Min Yang, Yongbin Li
| Challenge: | Existing approaches to reward modeling in reinforcement learning tasks are limited when dealing with ambiguous preferences. |
| Approach: | They propose to use AAM to dynamically calibrate preference margins using the Bradley-Terry model's internal parameter knowledge to improve reward modeling in subjective tasks. |
| Outcome: | The proposed approach improves reward modeling by dynamically calibrating preference margins using the model’s internal parameter knowledge. |
Understanding Faithfulness and Reasoning of Large Language Models on Plain Biomedical Summaries (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Generating plain biomedical summaries with Large Language Models (LLMs) can enhance access to biomedically knowledge. |
| Approach: | They propose a benchmark dataset with expert-annotated Faithfulness and Reasoning on plain biomedical summaries. |
| Outcome: | The proposed dataset shows that LLMs perform poorly in generating faithful biomedical summaries and that abstractiveness and faithfulness are negatively correlated. |
Born Differently Makes a Difference: Counterfactual Study of Bias in Biography Generation from a Data-to-Text Perspective (2024.acl-short)
Copied to clipboard
| Challenge: | Current research shows that biographies reflect bias from society such as gender and religions. |
| Approach: | They propose a method that manipulates the personal attributes of interest while keeping the co-occurring attributes unchanged. |
| Outcome: | The proposed method expands the analysis of gender-centered bias in text generation. |
STAF: Pushing the Boundaries of Test-Time Adaptation towards Practical Noise Scenarios (2024.lrec-main)
Copied to clipboard
| Challenge: | Pre-trained language models have demonstrated superior performance on NLP tasks . however, when the training domain and testing domain are taken from different distributions, the deployed model often violates this assumption. |
| Approach: | They propose a Stable Test-time Adaptation Framework to stabilize the adaptation process. |
| Outcome: | The proposed framework boosts model robustness to noise distribution shifts while minimizing error accumulation and catastrophic forgetting. |
Extract-Select: A Span Selection Framework for Nested Named Entity Recognition with Generative Adversarial Training (2022.findings-acl)
Copied to clipboard
| Challenge: | Existing studies treat named entity recognition as a sequential labeling problem. |
| Approach: | They propose a span selection framework for nested named entity recognition . they propose nesting entities with different input categories would be separately extracted . |
| Outcome: | The proposed framework outperforms competing models on four benchmark datasets. |
A Critical Look at Meta-evaluating Summarisation Evaluation Metrics (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Effective summarisation evaluation metrics enable researchers and practitioners to compare different summarization systems efficiently. |
| Approach: | They argue that evaluation metrics are primarily meta-evaluated on news summarisation datasets and that there has been a noticeable shift in research focus towards evaluating the faithfulness of generated summaries. |
| Outcome: | The evaluation metrics are primarily meta-evaluated on news summarisation datasets and there has been a noticeable shift in research focus towards evaluating the faithfulness of generated summaries. |
Can VLMs Actually See and Read? A Survey on Modality Collapse in Vision-Language Models (2025.findings-acl)
Copied to clipboard
| Challenge: | Vision-language models integrate textual and visual information, enabling them to process visual inputs and generate predictions. |
| Approach: | They review work on modality collapse analysis to provide insights into the reason for this unintended behavior and review probing studies for fine-grained vision-language understanding. |
| Outcome: | The proposed models can achieve competitive performance in vision-language tasks despite relying heavily on textual information and ignoring visual information. |
Do Vision-Language Models Have Internal World Models? Towards an Atomic Evaluation (2025.findings-acl)
Copied to clipboard
Qiyue Gao, Xinyu Pi, Kevin Liu, Junrong Chen, Ruolan Yang, Xinqi Huang, Xinyu Fang, Lu Sun, Gautham Kishore, Bo Ai, Stone Tao, Mengyang Liu, Jiaxi Yang, Chao-Jung Lai, Chuanyang Jin, Jiannan Xiang, Benhao Huang, Zeming Chen, David Danks, Hao Su, Tianmin Shu, Ziqiao Ma, Lianhui Qin, Zhiting Hu
| Challenge: | Recent studies have evaluated and shown limitations in specific capabilities such as visual understanding, but a systematic evaluation of VLMs’ fundamental WM abilities remains absent. |
| Approach: | They propose a framework that assesses perception and prediction to provide an atomic evaluation of VLMs as WMs. |
| Outcome: | The proposed framework assesses perception and prediction abilities on 15 latest VLMs and compares them to human-level models. |
FARSS: Fisher-Optimized Adaptive Low-Rank and Singular-Vector Selection for Knowledge-Preserving Fine-Tuning (2026.findings-acl)
Copied to clipboard
Renxing Chen, Ziwei Xiang, Peisong Wang, Hongjian Fang, Meng Li, Fanhu Zeng, Yanan Zhu, Peipei Yang, Xu-Yao Zhang, Jian Cheng
| Challenge: | Low-rank adaptation methods for large language models have limitations in preserving world knowledge and limiting updates to preserve world knowledge. |
| Approach: | They propose a Fisher-optimized adaptive low Rank and Singular-VectorSelection framework for knowledge-preserving fine-tuning that allows efficient and task-sensitive updates. |
| Outcome: | The proposed framework outperforms existing methods for knowledge-preserving fine-tuning. |
Towards Robust Temporal Activity Localization Learning with Noisy Labels (2024.lrec-main)
Copied to clipboard
Daizong Liu, Xiaoye Qu, Xiang Fang, Jianfeng Dong, Pan Zhou, Guoshun Nan, Keke Tang, Wanlong Fang, Yu Cheng
| Challenge: | Existing methods for temporal activity localization are expensive and difficult to satisfy due to subjective labeling. |
| Approach: | They propose a new TAL setting where a TAL model should be robust to mixed training data with noisy moment boundaries. |
| Outcome: | The proposed method is significantly more robust to noisy training data than existing methods. |
Reverse Preference Optimization for Complex Instruction Following (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for identifying and evaluating preference pairs with multiple constraints are noisy. |
| Approach: | They propose a method that dynamically reverses constraints to ensure the chosen response is perfect. |
| Outcome: | The proposed method reduces noise in preference pairs by reversing constraints to ensure the chosen response is perfect. |
ConvLab-2: An Open-Source Toolkit for Building, Evaluating, and Diagnosing Dialogue Systems (2020.acl-demos)
Copied to clipboard
Qi Zhu, Zheng Zhang, Yan Fang, Xiang Li, Ryuichi Takanobu, Jinchao Li, Baolin Peng, Jianfeng Gao, Xiaoyan Zhu, Minlie Huang
| Challenge: | ConvLab-2 inherits Convlab's framework but integrates more powerful dialogue models and supports more datasets. |
| Approach: | They present ConvLab-2, an open-source toolkit that enables researchers to build task-oriented dialogue systems with state-of-the-art models and perform an end-to-end evaluation. |
| Outcome: | The new tool inherits ConvLab's framework and extends it by integrating many recently proposed state-of-the-art dialogue models. |
KG-FiD: Infusing Knowledge Graph in Fusion-in-Decoder for Open-Domain Question Answering (2022.acl-long)
Copied to clipboard
Donghan Yu, Chenguang Zhu, Yuwei Fang, Wenhao Yu, Shuohang Wang, Yichong Xu, Xiang Ren, Yiming Yang, Michael Zeng
| Challenge: | Open-Domain Question Answering (ODQA) models typically include a retrieving module and a reading module. |
| Approach: | They propose a new open-domain question-answering framework that uses a knowledge-enhanced version of FiD to improve the approach. |
| Outcome: | The proposed model improves on ODQA benchmark datasets with less than 40% computation cost. |
LIME: Less Is More for MLLM Evaluation (2025.findings-acl)
Copied to clipboard
King Zhu, Qianbo Zang, Shian Jia, Siwei Wu, Feiteng Fang, Yizhi Li, Shuyue Guo, Tianyu Zheng, Jiawei Guo, Bo Li, Haoning Wu, Xingwei Qu, Jian Yang, Ruibo Liu, Xiang Yue, Jiaheng Liu, Chenghua Lin, Hamid Alinejad-Rokny, Min Yang, Shiwen Ni, Wenhao Huang, Ge Zhang
| Challenge: | Existing MLLM benchmarks and unified evaluation frameworks cannot accurately and efficiently reflect the ability of MLMLs. |
| Approach: | They propose a semi-automated benchmark curated using a pipeline that filters out uninformative samples and eliminates answer leakage by focusing on tasks that require image-based understanding. |
| Outcome: | The proposed benchmark reduces the number of samples by 76% and evaluation time by 77% while it can more effectively distinguish different models’ abilities. |
Improving Grammatical Error Correction with Multimodal Feature Integration (2023.findings-acl)
Copied to clipboard
| Challenge: | Experimental results show that multimodal GEC models improve over strong baselines and achieve a new state-of-the-art result on the Falko-MERLIN test set. |
| Approach: | They propose a framework that integrates both speech and text features to enhance GEC by generating audio from text using advanced text-to-speech models. |
| Outcome: | The proposed framework improves on CoNLL14, BEA19 English, and Falko-MERLIN German datasets. |
The More, The Better? A Critical Study of Multimodal Context in Radiology Report Summarization (2025.findings-emnlp)
Copied to clipboard
Mong Yuan Sim, Wei Emma Zhang, Xiang Dai, Biaoyan Fang, Sarbin Ranjitkar, Arjun Burlakoti, Jamie Taylor, Haojie Zhuang
| Challenge: | Current multimodal summarization models often fail to utilize radiology images in summarizing Findings section. |
| Approach: | They conduct a thorough analysis to determine whether current multimodal summarization models can utilize radiology images in summarizing Findings section. |
| Outcome: | The Impression section plays a crucial role in communication between radiologists and physicians. |
Neuron-Level Sequential Editing for Large Language Models (2025.acl-long)
Copied to clipboard
Houcheng Jiang, Junfeng Fang, Tianyu Zhang, Baolong Bi, An Zhang, Ruipeng Wang, Tao Liang, Xiang Wang
| Challenge: | Existing model editing methods focus on single-round editing and often face significant challenges in sequential model editing. |
| Approach: | They propose a model editing method that optimizes the target layer’s hidden states using the model’s original weights to prevent model failure. |
| Outcome: | The proposed method outperforms existing model editing methods and is available on the open-source platform 4open.science. |