Papers by Jiaming Luo
Improving the Robustness of Summarization Models by Detecting and Removing Input Noise (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Abstractive summarization models are typically evaluated using test data that is identically distributed as training data. |
| Approach: | They propose a method to detect and remove input noise from documents to be summarized without extra training or auxiliary models. |
| Outcome: | The proposed method recovers a large fraction of the loss in performance, sometimes as large as 11 ROUGE-1 points, without extra training, auxiliary models, or prior knowledge of the type of noise. |
Alligators All Around: Mitigating Lexical Confusion in Low-resource Machine Translation (2025.naacl-short)
Copied to clipboard
| Challenge: | Current machine translation systems for low-resource languages have a particular failure mode: they tend to confuse words within a domain. |
| Approach: | They propose a recall-based metric to measure the failure mode of machine translation systems for low-resource languages. |
| Outcome: | The proposed model outperforms a lexicon-based translator in 122 low-resource languages. |
Towards IP Intelligence: Benchmarking Large Language Models on Intellectual Property Knowledge and Practice (2026.findings-acl)
Copied to clipboard
Qiyao Wang, Guhong Chen, Hongbo Wang, Huaren Liu, Minghui Zhu, Zhifei Qin, Li Linwei, Yilin Yue, Shiqiang Wang, Jiayan Li, Wu Yihang, Ziqiang Liu, Longze Chen, Run Luo, Liyang Fan, Jiaming Li, Lei Zhang, Kan Xu, Hamid Alinejad-Rokny, Chengming Li, Shiwen Ni, Yuan Lin, Min Yang
| Challenge: | Existing datasets and benchmarks focus only on patents or cover limited aspects of the IP field, lacking alignment with real-world scenarios. |
| Approach: | They propose a bilingual IP task taxonomy and a large-scale bilingual benchmark to evaluate LLMs in real-world IP practice. |
| Outcome: | The proposed model achieves only 75.8% accuracy, indicating room for improvement . open-source IP and law-oriented models lag behind closed-source general-purpose models . |
A Diverse and Effective Retrieval-Based Debt Collection System with Expert Knowledge (2025.naacl-industry)
Copied to clipboard
| Challenge: | Existing debt collection systems lack script diversity, contextual relevance and coherence due to their complexity. |
| Approach: | They propose a script library based on real-world debt collection conversations and a retrieval based response system for contextual relevance. |
| Outcome: | The proposed system improves script diversity and responds to debtor-collector conversations better through knowledge distillation. |
Prompting PaLM for Translation: Assessing Strategies and Performance (2023.acl-long)
Copied to clipboard
| Challenge: | Large language models trained on multilingual but not parallel text exhibit remarkable ability to translate between languages. |
| Approach: | They investigate the pathways language model which has demonstrated the strongest machine translation performance among similarly-trained LLMs to date. |
| Outcome: | The pathways language model (PaLM) has demonstrated the strongest machine translation performance among similarly-trained LLMs to date. |
STORYTELLER: An Enhanced Plot-Planning Framework for Coherent and Cohesive Story Generation (2025.findings-acl)
Copied to clipboard
Jiaming Li, Yukun Chen, Ziqiang Liu, Minghuan Tan, Lei Zhang, Yunshui Li, Run Luo, Longze Chen, Jing Luo, Ahmadreza Argha, Hamid Alinejad-Rokny, Wei Zhou, Min Yang
| Challenge: | Existing methods for storytelling lack coherence and consistency, compromising the overall storytelling experience. |
| Approach: | They propose a novel approach that improves the coherence and consistency of automatically generated stories by managing plot nodes and enabling dynamic interactions between different parts of the story. |
| Outcome: | The proposed approach outperforms existing methods in 84.33% of the trials. |
How Good is Your Wikipedia? Auditing Data Quality for Low-resource and Multilingual NLP (2026.acl-long)
Copied to clipboard
Kushal Tatariya, Artur Kulmizev, Wessel Poelman, Esther Ploeger, Marcel Bollmann, Johannes Bjerva, Jiaming Luo, Heather Lent, Miryam de Lhoneux
| Challenge: | Wikipedia’s perceived high quality and broad language coverage have established it as a fundamental resource in NLP. |
| Approach: | They propose a data filtering procedure which removes a large percentage of Wikipedia's data and a 4-level quality ranking of the site. |
| Outcome: | The results show that the proposed filtering procedure outperforms the raw Wikipedia models in three language modelling scenarios. |
Deciphering Undersegmented Ancient Scripts Using Phonetic Prior (2021.tacl-1)
Copied to clipboard
| Challenge: | a number of undeciphered languages are still undecipherated, igniting fierce scientific debate . a recent study shows that NLP methods can successfully decipher lost languages . |
| Approach: | They propose a decipherment model that incorporates phonetic geometry into word segmentation and cognate alignment . they use the International Phonetic Alphabet to learn character embeddings based on historical sound change . |
| Outcome: | The proposed model shows that it can decipher both deciphered and undeciphered languages. |
Ruler: A Model-Agnostic Method to Control Generated Length for Large Language Models (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Large language models struggle to meet user’s needs when required to generate responses of a specific length due to their inherent difficulty in accurately perceiving numerical constraints. |
| Approach: | They propose a Target Length Generation Task and propose RULER, a model-agnostic approach that controls generated length for large language models. |
| Outcome: | The proposed model-agnostic approach improves instruction-following ability of large language models under length-constrained instructions and can generate appropriate MLT when length constraints are not explicitly provided. |
FinMME: Benchmark Dataset for Financial Multi-Modal Reasoning Evaluation (2025.acl-long)
Copied to clipboard
Junyu Luo, Zhizhuo Kou, Liming Yang, Xiao Luo, Jinsheng Huang, Zhiping Xiao, Jingshu Peng, Chengzhong Liu, Jiaming Ji, Xuanzhe Liu, Sirui Han, Ming Zhang, Yike Guo
| Challenge: | Multimodal Large Language Models (MLLMs) have experienced rapid development in recent years, but there is a notable lack of effective and specialized multimodal evaluation datasets in the financial domain. |
| Approach: | They introduce FinMME, a multimodal large language model with 11,000 financial research samples and 20 annotators. |
| Outcome: | The proposed model performs better than state-of-the-art models, highlighting its challenging nature. |
Neural Decipherment via Minimum-Cost Flow: From Ugaritic to Linear B (P19-1)
Copied to clipboard
| Challenge: | Existing methods for decipherment of lost languages are limited by limited data and scarce quantities of ancient text. |
| Approach: | They propose a neural approach for automatic decipherment of lost languages . they use an expressive sequence-to-sequence model to capture character-level correspondences between cognates . |
| Outcome: | The proposed approach improves on the decipherment of Ugaritic and Linear B in ancient Greek . the proposed approach is highly customized for a given language pair and does not generalize to other lost languages. |