Search Results for author: Jiatong Li

Found 24 papers, 9 papers with code

Large Language Models are In-Context Molecule Learners

no code implementations7 Mar 2024 Jiatong Li, Wei Liu, Zhihao Ding, Wenqi Fan, Yuqiang Li, Qing Li

Large Language Models (LLMs) have demonstrated exceptional performance in biochemical tasks, especially the molecule caption translation task, which aims to bridge the gap between molecules and natural language texts.

Cross-Modal Retrieval Re-Ranking +2

ChemLLM: A Chemical Large Language Model

no code implementations10 Feb 2024 Di Zhang, Wei Liu, Qian Tan, Jingdan Chen, Hang Yan, Yuliang Yan, Jiatong Li, Weiran Huang, Xiangyu Yue, Dongzhan Zhou, Shufei Zhang, Mao Su, Hansen Zhong, Yuqiang Li, Wanli Ouyang

ChemLLM beats GPT-3. 5 on all three principal tasks in chemistry, i. e., name conversion, molecular caption, and reaction prediction, and surpasses GPT-4 on two of them.

Language Modelling Large Language Model +2

Fast Graph Condensation with Structure-based Neural Tangent Kernel

1 code implementation17 Oct 2023 Lin Wang, Wenqi Fan, Jiatong Li, Yao Ma, Qing Li

The rapid development of Internet technology has given rise to a vast amount of graph-structured data.

Dataset Condensation Graph Mining

Language Modeling for Content-enriched Recommendation

no code implementations19 Sep 2023 Junzhe Jiang, Shang Qu, Mingyue Cheng, Qi Liu, Zhiding Liu, Hao Zhang, Rujiao Zhang, Kai Zhang, Rui Li, Jiatong Li, Min Gao

Recommender systems are indispensable in the realm of online applications, and sequential recommendation has enjoyed considerable prevalence due to its capacity to encapsulate the dynamic shifts in user interests.

Language Modelling Sequential Recommendation +1

ALWOD: Active Learning for Weakly-Supervised Object Detection

1 code implementation ICCV 2023 Yuting Wang, Velibor Ilic, Jiatong Li, Branislav Kisacanin, Vladimir Pavlovic

In this work, we propose ALWOD, a new framework that addresses this problem by fusing active learning (AL) with weakly and semi-supervised object detection paradigms.

Active Learning Object +4

Beyond Static Datasets: A Deep Interaction Approach to LLM Evaluation

no code implementations8 Sep 2023 Jiatong Li, Rui Li, Qi Liu

Existing LLM evaluation methods are mainly supervised signal-based which depends on static datasets and cannot evaluate the ability of LLMs in dynamic real-world scenarios where deep interaction widely exists.

Code Generation Machine Translation

Towards the Identifiability and Explainability for Personalized Learner Modeling: An Inductive Paradigm

no code implementations1 Sep 2023 Jiatong Li, Qi Liu, Fei Wang, Jiayu Liu, Zhenya Huang, Fangzhou Yao, Linbo Zhu, Yu Su

However, we notice that this paradigm leads to the inevitable non-identifiability and explainability overfitting problem, which is harmful to the quantification of learners' cognitive states and the quality of web learning services.

cognitive diagnosis

Conflicts, Villains, Resolutions: Towards models of Narrative Media Framing

1 code implementation3 Jun 2023 Lea Frermann, Jiatong Li, Shima Khanehzar, Gosia Mikolajczak

Despite increasing interest in the automatic detection of media frames in NLP, the problem is typically simplified as single-label classification and adopts a topic-like view on frames, evading modelling the broader document-level narrative.

Retrieval

Generative Diffusion Models on Graphs: Methods and Applications

1 code implementation6 Feb 2023 Chengyi Liu, Wenqi Fan, Yunqing Liu, Jiatong Li, Hang Li, Hui Liu, Jiliang Tang, Qing Li

Given the great success of diffusion models in image generation, increasing efforts have been made to leverage these techniques to advance graph generation in recent years.

Denoising Graph Generation +2

Improving Chinese Named Entity Recognition by Search Engine Augmentation

no code implementations23 Oct 2022 Qinghua Mao, Jiatong Li, Kui Meng

In this paper, we propose a neural-based approach to perform semantic augmentation using external knowledge from search engine for Chinese NER.

Chinese Named Entity Recognition named-entity-recognition +2

ExtrudeNet: Unsupervised Inverse Sketch-and-Extrude for Shape Parsing

1 code implementation30 Sep 2022 Daxuan Ren, Jianmin Zheng, Jianfei Cai, Jiatong Li, Junzhe Zhang

This paper studies the problem of learning the shape given in the form of point clouds by inverse sketch-and-extrude.

Exploring Effective Information Utilization in Multi-Turn Topic-Driven Conversations

no code implementations1 Sep 2022 Jiatong Li, Bin He, Fei Mi

In order to expand the information that PLMs can utilize, we encode topic and dialogue history information using certain prompts with multiple channels of Fusion-in-Decoder (FiD) and explore the influence of three different channel settings.

Dialogue Generation

MFE-NER: Multi-feature Fusion Embedding for Chinese Named Entity Recognition

no code implementations16 Sep 2021 Jiatong Li, Kui Meng

In this paper, we propose a new method, Multi-Feature Fusion Embedding for Chinese Named Entity Recognition (MFE-NER), to strengthen the language pattern of Chinese and handle the character substitution problem in Chinese Named Entity Recognition.

Chinese Named Entity Recognition named-entity-recognition +2

Understanding and Improving Early Stopping for Learning with Noisy Labels

1 code implementation NeurIPS 2021 Yingbin Bai, Erkun Yang, Bo Han, Yanhua Yang, Jiatong Li, Yinian Mao, Gang Niu, Tongliang Liu

Instead of the early stopping, which trains a whole DNN all at once, we initially train former DNN layers by optimizing the DNN with a relatively large number of epochs.

Learning with noisy labels Memorization

Algorithmic subsampling under multiway clustering

no code implementations28 Feb 2021 Harold D. Chiang, Jiatong Li, Yuya Sasaki

This paper proposes a novel method of algorithmic subsampling (data sketching) for multiway cluster dependent data.

Clustering

CHEF: Cross-modal Hierarchical Embeddings for Food Domain Retrieval

1 code implementation4 Feb 2021 Hai X. Pham, Ricardo Guerrero, Jiatong Li, Vladimir Pavlovic

Despite the abundance of multi-modal data, such as image-text pairs, there has been little effort in understanding the individual entities and their different roles in the construction of these data instances.

Cross-Modal Retrieval Retrieval

Extended T: Learning with Mixed Closed-set and Open-set Noisy Labels

no code implementations2 Dec 2020 Xiaobo Xia, Tongliang Liu, Bo Han, Nannan Wang, Jiankang Deng, Jiatong Li, Yinian Mao

The traditional transition matrix is limited to model closed-set label noise, where noisy training data has true class labels within the noisy label set.

Picture-to-Amount (PITA): Predicting Relative Ingredient Amounts from Food Images

no code implementations17 Oct 2020 Jiatong Li, Fangda Han, Ricardo Guerrero, Vladimir Pavlovic

Increased awareness of the impact of food consumption on health and lifestyle today has given rise to novel data-driven food analysis systems.

Nutrition

Deep Cooking: Predicting Relative Food Ingredient Amounts from Images

no code implementations26 Sep 2019 Jiatong Li, Ricardo Guerrero, Vladimir Pavlovic

In this paper, we study the novel problem of not only predicting ingredients from a food image, but also predicting the relative amounts of the detected ingredients.

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