Papers by Di Cao
Know-MRI: A Knowledge Mechanisms Revealer&Interpreter for Large Language Models (2025.acl-demo)
Copied to clipboard
Jiaxiang Liu, Boxuan Xing, Chenhao Yuan, ChenxiangZhang ChenxiangZhang, Di Wu, Xiusheng Huang, Haida Yu, Chuhan Lang, Pengfei Cao, Jun Zhao, Kang Liu
| Challenge: | Existing interpretation methods only support tasks with specific inputs, limiting their practical applications. |
| Approach: | They propose an extensible module that matches different input data with interpretation methods and consolidates the interpreting outputs. |
| Outcome: | The proposed module can match different input data with interpretation methods and consolidate the interpreting outputs. |
D-Artemis: A Deliberative Cognitive Framework for Mobile GUI Multi-Agents (2026.findings-acl)
Copied to clipboard
Hongze Mi, Yibo Feng, WenJie Lu, Yuqi Wang, Jinyuan Li, Song Cao, He Cui, Tengfei Tian, Xuelin Zhang, Haotian Luo, Di Sun, Jun Fang, Hua Chai, Naiqiang Tan, Gang Pan
| Challenge: | Graphical User Interface (GUI) agents aim to automate a wide spectrum of human tasks by emulating user interaction. |
| Approach: | They propose a deliberative framework that leverages a fine-grained tip retrieval mechanism to inform its decision-making process. |
| Outcome: | The proposed framework achieves SOTA among open-source general models on AndroidWorld and ScreenSpot-V2 . it leverages a fine-grained, app-specific tip retrieval mechanism to inform its decision-making process . |
BUMP: A Benchmark of Unfaithful Minimal Pairs for Meta-Evaluation of Faithfulness Metrics (2023.acl-long)
Copied to clipboard
Liang Ma, Shuyang Cao, Robert L Logan IV, Di Lu, Shihao Ran, Ke Zhang, Joel Tetreault, Alejandro Jaimes
| Challenge: | Existing benchmarks measure the correlation with human judgements of faithfulness on model-generated summaries, but they are insufficient for diagnosing whether metrics are consistent, effective on human-written texts, and sensitive to different error types. |
| Approach: | They propose to use unfaithful minimal pairs to measure the consistency of automatic faithfulness metrics by comparing human-written summary pairs with a dataset of 889 human-writing, minimally different summary pairs. |
| Outcome: | The proposed benchmarks show that the most discriminative metrics tend not to be the most consistent, and that the best performing metrics are sensitive to errors. |
CoLA: A Choice Leakage Attack Framework to Expose Privacy Risks in Subset Training (2026.acl-long)
Copied to clipboard
| Challenge: | Existing threat models underestimate subset-training privacy risks because of the scale of modern datasets. |
| Approach: | They propose a unified framework for analyzing privacy leakage in subset selection based on side-channel metadata from the subset process or via the outputs of the target model. |
| Outcome: | The proposed framework analyzes privacy leakage in subset selection based on two different scenarios . |
Binarized LSTM Language Model (N18-1)
Copied to clipboard
| Challenge: | Long short-term memory (LSTM) language models are widely used for automatic speech recognition and natural language processing (NLP) however, they are limited by the word embedding layer. |
| Approach: | They propose to encode words into binary vectors and use binarized LSTM parameters to achieve high memory compression. |
| Outcome: | The proposed model achieves 11.3 compression ratio without loss of performance and 31.6 compression ratio with acceptable performance degradation. |
CODEMENV: Benchmarking Large Language Models on Code Migration (2025.findings-acl)
Copied to clipboard
Keyuan Cheng, Xudong Shen, Yihao Yang, TengyueWang TengyueWang, Yang Cao, Muhammad Asif Ali, Hanbin Wang, Lijie Hu, Di Wang
| Challenge: | Large language models (LLMs) have demonstrated remarkable proficiency in handling a wide range of tasks within the software engineering domain, but their ability to perform code migration—adapting code to different environments—remains underexplored. |
| Approach: | They propose a benchmark to evaluate large language models’ performance in handling code migration tasks. |
| Outcome: | The proposed benchmark comprises 922 data points across 19 Python and Java packages and offers three tasks to systematically evaluate code migration: identifying version-incompatible functions, determining function changes, and adapting code to target environments. |
Meta-Task Prompting Elicits Embeddings from Large Language Models (2024.acl-long)
Copied to clipboard
| Challenge: | Existing methods for large language modeling are based on task-related instructions or prompts. |
| Approach: | They propose a method for generating high-quality sentence embeddings from Large Language Models (LLMs) using meta-task prompts. |
| Outcome: | The proposed method produces high-quality sentences without fine-tuning . it excels on STS benchmarks and in downstream tasks, surpassing models with similar prompts . |
RealVul: Can We Detect Vulnerabilities in Web Applications with LLM? (2024.emnlp-main)
Copied to clipboard
| Challenge: | a lack of research specifically focused on vulnerabilities in the PHP language hinders the model’s ability to effectively capture the characteristics of specific vulnerabilities. |
| Approach: | They propose a framework that can isolate potential vulnerability triggers while streamlining code and eliminating unnecessary semantic information. |
| Outcome: | The proposed framework can isolate potential vulnerability triggers while streamlining the code and eliminating unnecessary semantic information. |
Towards Multi-System Log Anomaly Detection (2025.acl-industry)
Copied to clipboard
| Challenge: | Existing models require dataset-specific training, causing costly procedures and performance bottlenecks. |
| Approach: | They propose a log anomaly detection model with semantic relational reasoning that extracts cross-system semantic patterns and encodes them as high-dimensional learnable vectors. |
| Outcome: | The proposed model extracts cross-system semantic patterns and encodes them as high-dimensional learnable vectors. |
Multi-View Source Ablation for Faithful Summarization (2023.findings-eacl)
Copied to clipboard
| Challenge: | MuFaSSa is a metric for evaluating faithfulness of abstractive summaries . it uses different strategies to remove information from source document to form multiple ablated views . |
| Approach: | They propose a metric for evaluating faithfulness of abstractive summaries using multiple ablated views. |
| Outcome: | The proposed metric outperforms existing models on summarization tasks and human-annotated faithfulness labels. |
CodeIF: Benchmarking the Instruction-Following Capabilities of Large Language Models for Code Generation (2025.acl-industry)
Copied to clipboard
| Challenge: | CodeIF assesses the ability of large language models to adhere to task-oriented instructions in code generation tasks. |
| Approach: | They introduce a benchmark designed to assess LLMs' ability to adhere to task-oriented instructions within diverse code generation scenarios. |
| Outcome: | The proposed benchmark assesses LLMs' ability to adhere to task-oriented instructions in code generation tasks across a wide range of complexity levels and programming domains. |