Papers by Xingxuan Li
Verify-and-Edit: A Knowledge-Enhanced Chain-of-Thought Framework (2023.acl-long)
Copied to clipboard
| Challenge: | Large language models (LLMs) have a number of shortcomings, including lack of factual correctness. |
| Approach: | They propose a framework to increase prediction factuality by post-editing reasoning chains . they propose to use large language models to generate interpretable reasoning chains. |
| Outcome: | The proposed framework leads to accuracy improvements in open-domain question-answering tasks. |
Chain of Ideas: Revolutionizing Research Via Novel Idea Development with LLM Agents (2025.findings-emnlp)
Copied to clipboard
Long Li, Weiwen Xu, Jiayan Guo, Ruochen Zhao, Xingxuan Li, Yuqian Yuan, Boqiang Zhang, Yuming Jiang, Yifei Xin, Ronghao Dang, Yu Rong, Deli Zhao, Tian Feng, Lidong Bing
| Challenge: | Existing methods for idea generation either trivially prompt LLMs or expose LLM to extensive literature without indicating useful information. |
| Approach: | They propose a chain-of-ideas agent that organizes literature in a chains structure . they propose evaluating idea-generation methods from different perspectives . |
| Outcome: | The proposed agent outperforms existing methods and matches human quality in idea generation. |
Retrieving Multimodal Information for Augmented Generation: A Survey (2023.findings-emnlp)
Copied to clipboard
Ruochen Zhao, Hailin Chen, Weishi Wang, Fangkai Jiao, Xuan Long Do, Chengwei Qin, Bosheng Ding, Xiaobao Guo, Minzhi Li, Xingxuan Li, Shafiq Joty
| Challenge: | Large Language Models (LLMs) are increasingly using multimodality to augment their generation ability, but there is no unified perception of at which stage and how to incorporate different modalities. |
| Approach: | They propose to use multimodality to augment Large Language Models (LLMs) this will provide scholars with a deeper understanding of the methods' applications and encourage them to adapt existing techniques to the fast-growing field of LLMs. |
| Outcome: | The proposed methods improve factuality, reasoning, interpretability, and robustness of the generated content. |
ParaICL: Towards Parallel In-Context Learning (2025.naacl-long)
Copied to clipboard
| Challenge: | Existing methods to improve ICL performance are limited by the length of the input context. |
| Approach: | They propose a method that utilizes all demonstration examples without exceeding the manageable context length. |
| Outcome: | The proposed method can be scaled up to integrate with existing methods. |
Is GPT-4 a Good Data Analyst? (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Large language models (LLMs) have shown their powerful capabilities in plenty of domains and tasks, including context understanding, code generation, language generation, data storytelling, etc. |
| Approach: | They propose to use GPT-4 as a data analyst to perform end-to-end data analysis with databases from a wide range of domains. |
| Outcome: | The proposed framework compares GPT-4 with human data analysts to perform end-to-end data analysis with databases from a wide range of domains. |
Evaluating Psychological Safety of Large Language Models (2024.emnlp-main)
Copied to clipboard
| Challenge: | a recent study evaluated the psychological safety of large language models. |
| Approach: | They designed unbiased prompts to evaluate the psychological safety of large language models. |
| Outcome: | The proposed prompts showed that they were fine-tuned with behavioral metrics to reduce toxicity. |
Towards Robust Low-Resource Fine-Tuning with Multi-View Compressed Representations (2023.acl-long)
Copied to clipboard
| Challenge: | Using hidden representations, pretrained language models are prone to overfitting due to the huge amount of parameters. |
| Approach: | They propose a method that inserts random autoencoders between hidden layers of a PLM to transform activations from the previous layers into multi-view compressed representations before feeding them into the upper layers. |
| Outcome: | The proposed method improves performance across sequence- and token-level lowresource tasks. |
SeaLLMs - Large Language Models for Southeast Asia (2024.acl-demos)
Copied to clipboard
Xuan-Phi Nguyen, Wenxuan Zhang, Xin Li, Mahani Aljunied, Zhiqiang Hu, Chenhui Shen, Yew Ken Chia, Xingxuan Li, Jianyu Wang, Qingyu Tan, Liying Cheng, Guanzheng Chen, Yue Deng, Sen Yang, Chaoqun Liu, Hang Zhang, Lidong Bing
| Challenge: | Existing large language models favor high-resource languages, such as English, at the expense of low-resourced and regional languages. |
| Approach: | They propose a series of language models that specifically focuses on Southeast Asian languages. |
| Outcome: | SeaLLM models outperform ChatGPT-3.5 in non-Latin languages by large margins . linguistic disparity impedes access to state-of-the-art AI technologies for non-English-speaking populations . |
YEDDA: A Lightweight Collaborative Text Span Annotation Tool (P18-4)
Copied to clipboard
| Challenge: | Existing annotation tools do not consider post-annotation quality analysis due to inter-annotator disagreement. |
| Approach: | They propose a lightweight but efficient open-source tool for text span annotation that can be used for collaborative user annotation and administrator evaluation and analysis. |
| Outcome: | The proposed system reduces the annotation time by half compared with existing tools and the time can be compressed by 16.47% through intelligent recommendation. |
Can We Further Elicit Reasoning in LLMs? Critic-Guided Planning with Retrieval-Augmentation for Solving Challenging Tasks (2025.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to problem-solving for large language models fail to provide accurate reasoning and factual accuracy. |
| Approach: | They propose a framework that leverages fine-tuned critic models to guide reasoning and retrieval processes. |
| Outcome: | The proposed framework outperforms baselines on domain-knowledge-intensive tasks . it can be used to iterate retrieval and reasoning, and improve retrieval relevance . |