Papers by Yifan Ding
ChatEL: Entity Linking with Chatbots (2024.lrec-main)
Copied to clipboard
| Challenge: | Entity Linking (EL) is a challenging task in natural language processing . existing approaches focus on creating elaborate contextual models that are unwieldy and difficult to train . |
| Approach: | They propose a framework to prompt LLMs to return accurate results for Entity Linking . they use a three-step framework to generate a set of EL models that can be open-source . |
| Outcome: | The proposed framework improves the average F1 performance across 10 datasets by more than 2%. |
CodeRAG: Finding Relevant and Necessary Knowledge for Retrieval-Augmented Repository-Level Code Completion (2025.emnlp-main)
Copied to clipboard
| Challenge: | Recent advances in code large language models have produced repository-level code completion methods that automatically predict the unfinished code based on the broader information from the repository. |
| Approach: | They propose a framework to identify relevant knowledge for retrieval-augmented repository-level code completion. |
| Outcome: | The proposed framework significantly outperforms state-of-the-art methods on ReccEval and CCEval. |
Ask-and-Verify: Span Candidate Generation and Verification for Attribute Value Extraction (2022.emnlp-industry)
Copied to clipboard
| Challenge: | Existing reading comprehension models can over-generate attribute values which hinders precision. |
| Approach: | They propose a product attribute value extraction task that captures key factual information from product descriptions and a new end-to-end pipeline framework called Ask-and-Verify. |
| Outcome: | The proposed framework outperforms existing models by up to 3.1% F1 absolute improvement points while scaling to thousands of attributes. |
ProQA: Structural Prompt-based Pre-training for Unified Question Answering (2022.naacl-main)
Copied to clipboard
Wanjun Zhong, Yifan Gao, Ning Ding, Yujia Qin, Zhiyuan Liu, Ming Zhou, Jiahai Wang, Jian Yin, Nan Duan
| Challenge: | Existing QA research on question answering is focused on specific question types, knowledge domains, or reasoning skills. |
| Approach: | They propose a unified QA paradigm that solves various tasks through a single model. |
| Outcome: | The proposed model improves QA-centric ability on 11 QA benchmarks. |
ALERT: An LLM-powered Benchmark for Automatic Evaluation of Recommendation Explanations (2025.naacl-long)
Copied to clipboard
Yichuan Li, Xinyang Zhang, Chenwei Zhang, Mao Li, Tianyi Liu, Pei Chen, Yifan Gao, Kyumin Lee, Kaize Ding, Zhengyang Wang, Zhihan Zhang, Jingbo Shang, Xian Li, Trishul Chilimbi
| Challenge: | Existing benchmarks for recommendation explanation evaluation lack item diversity and user preferences data. |
| Approach: | They propose a model-agnostic recommendation explanation evaluation benchmark based on Amazon e-commerce categories with implicit preferences . they propose two novel automatic evaluators that enable scalable and human-preference aligned evaluation of explanations . |
| Outcome: | The proposed model-agnostic evaluation benchmark outperforms existing methods in a variety of domains. |
Digital Gatekeepers: Google’s Role in Curating Hashtags and Subreddits (2025.acl-long)
Copied to clipboard
| Challenge: | This study examines how search engines like Google selectively promote or suppress certain hashtags and subreddits, impacting the flow of information and impacting public conversations. |
| Approach: | They compare search engine results with nonsampled data from Reddit and Twitter/X to examine how search engines curate content through algorithmic curation. |
| Outcome: | The proposed algorithm suppresses subreddits related to sexually explicit material, conspiracy theories, advertisements, and cryptocurrencies while promoting content associated with higher engagement. |
MARCH: Multi-Agent Radiology Clinical Hierarchy for CT Report Generation (2026.acl-short)
Copied to clipboard
| Challenge: | Automated 3D radiology report generation suffers from clinical hallucinations and lacks the iterative verification characteristic of clinical workflows. |
| Approach: | They propose a multi-agent framework that emulates the professional hierarchy of radiology departments and assigns specialized roles to distinct agents. |
| Outcome: | The proposed framework outperforms state-of-the-art models in clinical fidelity and linguistic accuracy on the RadGenome-ChestCT dataset. |
A Survey on MLLM-based Visually Rich Document Understanding: Methods, Challenges, and Emerging Trends (2026.findings-acl)
Copied to clipboard
Yihao Ding, Siwen Luo, Yue Dai, Yanbei Jiang, Zechuan Li, Qiang Sun, Geoffrey Martin, Wei Liu, Yifan Peng
| Challenge: | Visually Rich Document Understanding (VRDU) frameworks are a key area of research . early approaches to VRDU relied on manually crafted rules and domain-specific heuristics . conventional deep learning approaches do not integrate the diverse modalities in documents . |
| Approach: | They review recent advances in MLLM-based Visually Rich Document Understanding (VRDU) their findings highlight emerging trends and promising research directions . |
| Outcome: | The proposed frameworks are scalable, reliable, and adaptable, the authors argue . their findings highlight emerging trends and promising research directions . |
BFS-Prover: Scalable Best-First Tree Search for LLM-based Automatic Theorem Proving (2025.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to theorem proving in large language models rely on value functions and/or Monte Carlo Tree Search (MCTS), but the potential of simpler methods like Best-First Tree Search remains underexplored. |
| Approach: | They propose a scalable expert iteration framework that implements strategic data filtering at each expert iteration round, excluding problems solvable via beam search node expansion to focus on harder cases. |
| Outcome: | The proposed framework achieves a state-of-the-art score of 72.95 on the MiniF2F test set and challenges the perceived necessity of complex tree search methods. |
FANNO: Augmenting High-Quality Instruction Data with Open-Sourced LLMs Only (2025.findings-acl)
Copied to clipboard
| Challenge: | Recent studies explore approaches to synthesize instruction data with open-sourced LLMs but require high-quality human-crafted seed data. |
| Approach: | They propose an end-to-end framework to synthesize high-quality instruction data with open-sourced LLMs and sampled unlabeled documents, eliminating the need for seed data. |
| Outcome: | The proposed framework synthesizes high-quality instruction data with open-sourced LLMs and sampled unlabeled documents, eliminating the need for seed data. |
ATAP: Automatic Template-Augmented Commonsense Knowledge Graph Completion via Pre-Trained Language Models (2024.emnlp-main)
Copied to clipboard
| Challenge: | Commosense knowledge graphs (CKGC) are powerful representations of real-world commonsense knowledge. |
| Approach: | They propose a framework that uses automatically generated prompt templates combined with pre-trained language models to improve CKGC performance. |
| Outcome: | The proposed framework mitigates the long-tail problem and improves CKGC performance on a large dataset. |
Gradient-Attention Guided Dual-Masking Synergetic Framework for Robust Text-based Person Retrieval (2025.emnlp-main)
Copied to clipboard
| Challenge: | a large-scale visionlanguage pre-training framework is limited by the scarcity of large-sized annotated vision-language data . noise-resistant data construction pipeline is needed to filter and caption web-sourced images . noisy text tokens can be a problem for fine-grained representation learning . |
| Approach: | They develop a noise-resistant data construction pipeline that leverages in-context learning capabilities of MLLMs to automatically filter and caption web-sourced images. |
| Outcome: | The proposed framework improves cross-modal alignment by masking noisy textual tokens based on the gradient-attention similarity score. |
Less Likely Brainstorming: Using Language Models to Generate Alternative Hypotheses (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing methods to reduce cognitive errors in MRI interpretations do not work for generating less likely outputs. |
| Approach: | They propose a task that asks a model to generate outputs that humans think are relevant but less likely to happen. |
| Outcome: | The proposed method compares with several state-of-the-art controlled text generation models via automatic and human evaluations and shows that it reduces cognitive errors in interpreting MRI findings. |
BackdoorAgent: A Unified Framework for Backdoor Attacks on LLM-based Agents (2026.findings-acl)
Copied to clipboard
Yunhao Feng, Yige Li, Yutao Wu, Yingshui Tan, Yanming Guo, Yifan Ding, Kun Zhai, Xingjun Ma, Yu-Gang Jiang
| Challenge: | Large language model (LLM) agents execute tasks through multi-step workflows that combine planning, memory, and tool use. |
| Approach: | They propose a modular framework that provides a unified view of backdoor threats in LLM agents. |
| Outcome: | The proposed framework provides a unified, agent-centric view of backdoor threats in LLM agents. |
Natural Language Processing in Support of Evidence-based Medicine: A Scoping Review (2025.findings-acl)
Copied to clipboard
| Challenge: | Evidence-based medicine (EBM) is at the forefront of modern healthcare, emphasizing the use of the best available scientific evidence to guide clinical decisions. |
| Approach: | They propose to investigate the use of Natural Language Processing (NLP) techniques to identify, appraise, synthesize, apply, and disseminate evidence in EBM. |
| Outcome: | The proposed methods support the five fundamental steps of EBM—Ask, Acquire, Appraise, Apply, and Assess. |