Papers by Ding Deng
MLaKE: Multilingual Knowledge Editing Benchmark for Large Language Models (2025.coling-main)
Copied to clipboard
| Challenge: | Existing studies on knowledge editing focus on monolingual scenarios, neglecting the complexities presented by multilingual contexts and multi-hop reasoning. |
| Approach: | They propose a benchmark to evaluate the adaptability of multilingual knowledge editing methods. |
| Outcome: | The proposed benchmark evaluates the adaptability of multilingual knowledge editing methods across five languages. |
Automatic Prompt Engineering for Scalable Prompt Inversion in Text-to-Image Ad Generation (2026.acl-industry)
Copied to clipboard
| Challenge: | PRISM-DUEL is a black-box framework that formalizes prompt optimization as Automatic Prompt Engineering (APE) PRIMS-DUEl is motivated by advertising workflows requiring low-latency, diverse variants faithful to a human-designed ad. |
| Approach: | They propose a black-box framework that formalizes prompt optimization as Automatic Prompt Engineering (APE) they obtain label-free pairwise preferences and rationales from an LLM judge over pairs of generated images and use a dueling-bandit optimizer to optimize a prompt for generating controlled variations while matching the reference ad's visual content. |
| Outcome: | The proposed framework preserves visual similarity and semantic faithfulness while increasing diversity. |
Factual Consistency Evaluation for Text Summarization via Counterfactual Estimation (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods to evaluate factual consistency in text summarization neglect the intrinsic cause of factual inconsistency or rely on auxiliary tasks. |
| Approach: | They propose a method to evaluate the factual consistency in text summarization via counterfactual estimation, which formulates the causal relationship between source document, generated summary, and the language prior. |
| Outcome: | The proposed metric improves correlation with human judgments and convenience of usage on three public abstractive text summarization datasets. |
TAG: Gradient Attack on Transformer-based Language Models (2021.findings-emnlp)
Copied to clipboard
Jieren Deng, Yijue Wang, Ji Li, Chenghong Wang, Chao Shang, Hang Liu, Sanguthevar Rajasekaran, Caiwen Ding
| Challenge: | Recent studies show that publicly shared gradients in the training process can reveal the private training data to a third-party. |
| Approach: | They propose a gradient attack algorithm to reconstruct the local training data using GLUE benchmarks. |
| Outcome: | The proposed algorithm achieves 1.5x recover rate and 2.5x ROUGE-2 over previous methods without the need of ground truth label. |
VisPCO: Visual Token Pruning Configuration Optimization via Budget-Aware Pareto-Frontier Learning for Vision-Language Models (2026.acl-long)
Copied to clipboard
| Challenge: | Existing methods for visual token pruning rely on predefined configurations without determining whether they achieve optimal performance. |
| Approach: | They propose a framework that formulates visual token pruning as a Pareto configuration optimization problem to automatically identify optimal configurations. |
| Outcome: | The proposed framework approximates the empirical Pareto frontier obtained through grid search and generalizes well across pruning methods and VLM architectures. |
Remember Me, Refine Me: A Dynamic Procedural Memory Framework for Experience-Driven Agent Evolution (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing frameworks treat memory as a static append-only archive . Existing systems focus on passive accumulation, resulting in a 'passive accumulation' of memory. |
| Approach: | They propose a framework for experience-driven agent evolution that integrates procedural memory with contextual information to create a high-quality experience pool. |
| Outcome: | Experiments on BFCL-V3 and AppWorld show that ReMe outperforms memoryless Qwen3-8B. |
GR1: Reinforcement-Enhanced LLM for Geoscience Reasoning (2026.findings-acl)
Copied to clipboard
Yule Xie, Jiaxin Ding, Cheng Deng, Shiqing Gao, Junran Zhang, Sibo Zhang, Zeyuan Wang, Ke Wu, Xin Ding, Luoyi Fu, Meng Jin, Xinbing Wang
| Challenge: | Recent advances in large language models have demonstrated RL's substantial capacity to enhance multi-step reasoning beyond what supervised instruction tuning achieves. |
| Approach: | They propose a framework that converts multimodal questions into descriptive text . they propose RL-enhanced geoscience reasoning that can be fine-tuned to a text-only level . |
| Outcome: | The proposed framework improves accuracy and accuracy on multimodal questions while preserving answerability and difficulty. |
Curr-ReFT: Overcoming Training Bottlenecks in Small-scale Vision-Language Models via Curriculum Reinforcement Finetuning (2025.findings-emnlp)
Copied to clipboard
| Challenge: | State-of-the-art vision-language models require massive scaling that limits practical deployment. |
| Approach: | They propose to use supervised fine-tuning to train small-scale vision-language models but face out-of-domain collapse when trained with traditional supervised learning (SFT). |
| Outcome: | Experiments show that curr-reFT achieves state-of-the-art performance across visual tasks in both in- and out-of domain settings and benchmarks. |
Towards Open-Domain Topic Classification (2022.naacl-demo)
Copied to clipboard
| Challenge: | Existing supervised classification models are insensitive to class names, but are no longer effective in open-domain tasks where the taxonomy is unbounded. |
| Approach: | They propose a topic classification system that accepts user-defined taxonomy in real time . they train a pretrained language model on a new Wikipedia dataset and train it on Wikipedia . |
| Outcome: | The proposed system improves over existing zero-shot models and performs competitively with weakly-supervised models trained on in-domain data. |
The Right Time Matters: Data Arrangement Affects Zero-Shot Generalization in Instruction Tuning (2025.findings-acl)
Copied to clipboard
Bingxiang He, Ning Ding, Cheng Qian, Jia Deng, Ganqu Cui, Lifan Yuan, Haiwen Hong, Huan-ang Gao, Longtao Huang, Hui Xue, Huimin Chen, Zhiyuan Liu, Maosong Sun
| Challenge: | Existing work on instruction tuning has focused on task level, without considering that tasks are artificially defined and, to LLMs, merely consist of tokens and representations. |
| Approach: | They propose a training data arrangement framework that allows for continual learning and loss reduction. |
| Outcome: | The proposed framework promotes continual learning and loss reduction on unseen tasks. |
For-Value: Efficient Forward-Only Data Valuation for finetuning LLMs and VLMs (2026.acl-long)
Copied to clipboard
Wenlong Deng, Qi Zeng, Jiaming Zhang, Minghui Chen, Zixin Ding, Christos Thrampoulidis, Boying Gong, Xiaoxiao Li
| Challenge: | Existing methods for data valuation rely on gradient computations, making them prohibitive for billion-parameter models. |
| Approach: | They propose a forward-only data valuation framework that enables efficient batch-scalable value estimation while maintaining effectiveness. |
| Outcome: | The proposed framework matches or outperforms gradient-based baselines in detecting influential data and mislabeled data while achieving significant efficiency improvements. |
Fast Quiet-STaR: Thinking Without Thought Tokens (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Large Language Models have achieved impressive performance across a range of tasks, but further gains require more than scaling up model sizes or training data. |
| Approach: | They propose a method that gradually reduces the number of thought tokens . this method allows models to internalize more abstract reasoning processes . |
| Outcome: | The proposed framework preserves the benefits of token-level reasoning while reducing computational cost. |
A Secure and Efficient Federated Learning Framework for NLP (2021.emnlp-main)
Copied to clipboard
Chenghong Wang, Jieren Deng, Xianrui Meng, Yijue Wang, Ji Li, Sheng Lin, Shuo Han, Fei Miao, Sanguthevar Rajasekaran, Caiwen Ding
| Challenge: | Existing FL frameworks require a trusted aggregator or require heavy-weight cryptographic primitives, which makes the performance significantly degraded. |
| Approach: | They propose a framework that is federated and efficient for NLP . they propose to eliminate the need for trusted entities and achieve better model accuracy . |
| Outcome: | The proposed framework achieves better model accuracy and model accuracy than existing FL frameworks. |
Charge-Based Prison Term Prediction with Deep Gating Network (D19-1)
Copied to clipboard
| Challenge: | Existing work merely predicts the total prison term, but in reality a defendant is often charged with multiple crimes. |
| Approach: | They propose a charge-based prison term prediction task that better fits real needs and makes it more accurate and interpretable. |
| Outcome: | The proposed method achieves state-of-the-art performance for charge-specific feature selection and aggregation. |
EnsemW2S: Enhancing Weak-to-Strong Generalization with Large Language Model Ensembles (2026.findings-acl)
Copied to clipboard
Aakriti Agrawal, Mucong Ding, Chenghao Deng, Zora Che, Arjun Rajaram, Anirudh Satheesh, Bang An, C. Bayan Bruss, John Langford, Furong Huang
| Challenge: | Large Language Models (LLMs) are rapidly approaching and potentially exceeding human-level performance . a novel method aims to improve weak experts' generalization abilities by training them on limited human- level data . |
| Approach: | They propose a method that iteratively combines multiple weak experts to improve their generalization performance by training on limited human-level data. |
| Outcome: | The proposed method improves weak experts' generalization abilities by iterating on weak models and stronger student models. |
Rethinking Expert Trajectory Utilization in LLM Post-training for Mathematical Reasoning (2026.acl-long)
Copied to clipboard
Bowen Ding, Yuhan Chen, Jiayang Lyu, Jiyao Yuan, Qi Zhu, Shuangshuang Tian, Dantong Zhu, Futing Wang, Heyuan Deng, Fei Mi, Lifeng Shang, Tao Lin
| Challenge: | evolving generic Large Language Models into specialized Large Reasoning Models requires effective post-training. |
| Approach: | They propose a plasticity-ceiling framework to harness expert trajectories . they establish the Sequential SFT-then-RL pipeline as the superior standard . |
| Outcome: | The proposed framework overcomes stability and premature convergence deficits in synchronized approaches. |
TombRaider: Entering the Vault of History to Jailbreak Large Language Models (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing jailbreak techniques focus on prompt manipulation or intent obfuscation to bypass safety filters. |
| Approach: | They propose a jailbreak technique that exploits the ability to store, retrieve, and use historical knowledge of Large Language Models (LLMs) they use an inspector agent to extract historical information and an attacker agent to generate adversarial prompts, enabling effective bypassing of safety filters. |
| Outcome: | The proposed jailbreak technique outperforms state-of-the-art jailbreak techniques on six popular models and maintains over 55.4% ASR against defence mechanisms. |
PanoramaRAG: Enabling Consistent Global Topic Awareness in Graph-Based RAG (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing graph-based methods for enhancing Large Language Models (LLMs) with external knowledge are focusing on local relationships, resulting in suboptimal performance for tasks that require global context. |
| Approach: | They propose a "panorama"-guided paradigm that integrates a light yet comprehensive "panoramic" of the corpus to guide all stages of the retrieval process. |
| Outcome: | The proposed paradigm performs well across five datasets and a variety of tasks. |