Papers by Jiapeng Liu
Sequential LLM Framework for Fashion Recommendation (2024.emnlp-industry)
Copied to clipboard
Han Liu, Xianfeng Tang, Tianlang Chen, Jiapeng Liu, Indu Indu, Henry Zou, Peng Dai, Roberto Galan, Michael Porter, Dongmei Jia, Ning Zhang, Lian Xiong
| Challenge: | Existing fashion recommendation systems struggle with the unique challenges of the fashion domain. |
| Approach: | They propose a sequential fashion recommendation framework that leverages a pre-trained large language model enhanced with recommendation-specific prompts. |
| Outcome: | The proposed framework significantly improves fashion recommendation performance on Amazon fashion. |
Cross-Lingual Document Retrieval with Smooth Learning (2020.coling-main)
Copied to clipboard
| Challenge: | Cross-lingual document search is an information retrieval task in which the queries’ language and the documents’ language are different. |
| Approach: | They propose a robust framework that measures the relevance and a loss function that is a novel objective function. |
| Outcome: | The proposed framework achieves significant gains under commonly used ranking metrics on cross-lingual document retrieval task in a variety of languages. |
Enhancing LLM-based Hatred and Toxicity Detection with Meta-Toxic Knowledge Graph (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing methods to address toxicity issues with large language models are inadequate . lack of domain-specific knowledge leads to false negatives and excessive sensitivity to toxic speech limits freedom of speech. |
| Approach: | They propose a method that leverages graph search on a meta-toxic knowledge graph to enhance hatred and toxicity detection. |
| Outcome: | The proposed method lowers false positive rate and improves toxicity detection performance in out-of-domain scenarios. |
Gardener: An Agentic AI System for Single-Cell RNA Sequence Analysis (2026.acl-demo)
Copied to clipboard
| Challenge: | Existing large language models encode workflow progress as conversational state and rely on cloud-centric execution, which hinders traceability and auditability. |
| Approach: | Gardener is an open-source desktop application for macOS and windows under the Apache License 2.0. |
| Outcome: | Gardener is released as an open-source desktop application for macOS and Windows under the Apache License 2.0. |
Giving Control Back to Models: Enabling Offensive Language Detection Models to Autonomously Identify and Mitigate Biases (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing models often rely on specific words to predict offensive content, compromising model fairness and potentially exacerbates biases against vulnerable and minority groups. |
| Approach: | They propose a bias self-awareness and data self-iteration framework to help models identify and mitigate biases by integrating multiple natural language processing techniques. |
| Outcome: | The proposed framework reduces false positive rate of models in in-distribution and out-of-difference tests, enhances model accuracy and fairness, and shows promising performance improvements on larger datasets. |
MSc-SQL: Multi-Sample Critiquing Small Language Models For Text-To-SQL Translation (2025.naacl-long)
Copied to clipboard
Satya Krishna Gorti, Ilan Gofman, Zhaoyan Liu, Jiapeng Wu, Noël Vouitsis, Guangwei Yu, Jesse C. Cresswell, Rasa Hosseinzadeh
| Challenge: | Recent advances in text-to-SQL generation rely on large closed-source models that present challenges in accessibility, privacy, and latency. |
| Approach: | They propose to use open-source text-to-SQL models to critique SQL queries . their method evaluates multiple outputs simultaneously and is competitive with larger models . |
| Outcome: | The proposed method achieves state-of-the-art performance compared to open-source models while remaining competitive with larger models at a much lower cost. |
ChemActor: Enhancing Automated Extraction of Chemical Synthesis Actions with LLM-Generated Data (2025.acl-long)
Copied to clipboard
| Challenge: | Existing methods for extracting chemical procedures from literature are insufficient and low-quality due to the inherent ambiguity of chemical language and the high cost of human annotation. |
| Approach: | They propose a fully fine-tuned large language model (LLM) as a chemical executor to convert between unstructured experimental procedures and structured action sequences. |
| Outcome: | The proposed model outperforms the baseline model on R2D and D2A tasks by 10%. |
Free your mouse! Command Large Language Models to Generate Code to Format Word Documents (2024.emnlp-main)
Copied to clipboard
| Challenge: | Recent LLMs have significantly improved code generation, making it increasingly accessible to users. |
| Approach: | They propose an automatic document formatting method, Text-to-Format, driven by various prompting strategies and a high-quality dataset DocFormEval data. |
| Outcome: | The proposed method improves the efficiency and experience of users in formatting the document and improves document formatting task. |