A5038246814- Topic Modeling
- Natural Language Processing Techniques
- Online Learning and Analytics
- Multimodal Machine Learning Applications
- Advanced Graph Neural Networks
- Data Quality and Management
- Text Readability and Simplification
- Intelligent Tutoring Systems and Adaptive Learning
- Semantic Web and Ontologies
- Software Engineering Research
- Speech and dialogue systems
- Human Pose and Action Recognition
- Advanced Text Analysis Techniques
- Video Analysis and Summarization
- Educational Technology and Assessment
- Text and Document Classification Technologies
- Explainable Artificial Intelligence (XAI)
- Advanced Computational Techniques and Applications
- Machine Learning and Data Classification
- Data Stream Mining Techniques
- Business Process Modeling and Analysis
- Software System Performance and Reliability
- Data Mining Algorithms and Applications
- Computational and Text Analysis Methods
- Biomedical Text Mining and Ontologies
Tsinghua University
2018-2025
Beijing Academy of Artificial Intelligence
2019-2023
Beihang University
2023
Renmin University of China
2022
Tencent (China)
2021
Jifan Yu, Gan Luo, Tong Xiao, Qingyang Zhong, Yuquan Wang, Wenzheng Feng, Junyi Chenyu Lei Hou, Juanzi Li, Zhiyuan Liu, Jie Tang. Proceedings of the 58th Annual Meeting Association for Computational Linguistics. 2020.
Recent works on knowledge base question answering (KBQA) retrieve subgraphs for easier reasoning. The desired subgraph is crucial as a small one may exclude the answer but large might introduce more noises. However, existing retrieval either heuristic or interwoven with reasoning, causing reasoning partial subgraphs, which increases bias when intermediate supervision missing. This paper proposes trainable retriever (SR) decoupled from subsequent process, enables plug-and-play framework to...
Shulin Cao, Jiaxin Shi, Zijun Yao, Xin Lv, Jifan Yu, Lei Hou, Juanzi Li, Zhiyuan Liu, Jinghui Xiao. Proceedings of the 60th Annual Meeting Association for Computational Linguistics (Volume 1: Long Papers). 2022.
Current large language models (LLMs) often struggle to produce accurate responses on the first attempt for complex reasoning tasks like code generation. Prior research tackles this challenge by generating multiple candidate solutions and validating them with LLM-generated unit tests. The execution results of tests serve as reward signals identify correct solutions. As LLMs always confidently make mistakes, these are not reliable, thereby diminishing quality signals. Motivated observation...
Current inference scaling methods, such as Self-consistency and Best-of-N, have proven effective in improving the accuracy of LLMs on complex reasoning tasks. However, these methods rely heavily quality candidate responses are unable to produce correct answers when all candidates incorrect. In this paper, we propose a novel strategy, CoT-based Synthesizer, which leverages CoT synthesize superior by analyzing complementary information from multiple responses, even flawed. To enable...
Metacognitive education plays a crucial role in cultivating students' self-regulation and reflective thinking, providing essential support for those with learning difficulties through academic advising. Simulating students insufficient capabilities using large language models offers promising approach to refining pedagogical methods without ethical concerns. However, existing simulations often fail authentically represent struggles face challenges evaluation due the lack of reliable metrics...
Existing Large Vision-Language Models (LVLMs) can process inputs with context lengths up to 128k visual and text tokens, yet they struggle generate coherent outputs beyond 1,000 words. We find that the primary limitation is absence of long output examples during supervised fine-tuning (SFT). To tackle this issue, we introduce LongWriter-V-22k, a SFT dataset comprising 22,158 examples, each multiple input images, an instruction, corresponding ranging from 0 10,000 Moreover, achieve maintain...
Self-supervised entity alignment (EA) aims to link equivalent entities across different knowledge graphs (KGs) without the use of pre-aligned pairs. The current state-of-the-art (SOTA) self-supervised EA approach draws inspiration from contrastive learning, originally designed in computer vision based on instance discrimination and loss, suffers two shortcomings. Firstly, it puts unidirectional emphasis pushing sampled negative far away rather than pulling positively aligned pairs close, as...
We present GLM-Dialog, a large-scale language model (LLM) with 10B parameters capable of knowledge-grounded conversation in Chinese using search engine to access the Internet knowledge. GLM-Dialog offers series applicable techniques for exploiting various external knowledge including both helpful and noisy knowledge, enabling creation robust dialogue LLMs limited proper datasets. To evaluate more fairly, we also propose novel evaluation method allow humans converse multiple deployed bots...
The prosperity of massive open online courses provides fodder for plentiful research efforts on adaptive learning. However, current open-access educational datasets are still far from sufficient to meet the need various topics Existing released often cover only small-scale data, lack fine-grained knowledge concepts. They even difficult curate and supplement due platform limitations. In this work, we construct MOOCCubeX, a large, knowledge-centered repository consisting 4,216 courses, 230,263...
Numerous benchmarks have been established to assess the performance of foundation models on open-ended question answering, which serves as a comprehensive test model's ability understand and generate language in manner similar humans. Most these works focus proposing new datasets, however, we see two main issues within previous benchmarking pipelines, namely testing leakage evaluation automation. In this paper, propose novel framework, Language-Model-as-an-Examiner, where LM knowledgeable...
The unprecedented performance of large language models (LLMs) necessitates improvements in evaluations. Rather than merely exploring the breadth LLM abilities, we believe meticulous and thoughtful designs are essential to thorough, unbiased, applicable Given importance world knowledge LLMs, construct a Knowledge-oriented Assessment benchmark (KoLA), which carefully design three crucial factors: (1) For ability modeling, mimic human cognition form four-level taxonomy knowledge-related...
Despite the recent emergence of video captioning models, how to generate vivid, fine-grained descriptions based on background knowledge (i.e., long and informative commentary about domain-specific scenes with appropriate reasoning) is still far from being solved, which however has great applications such as automatic sports narrative. Based soccer game videos synchronized data, we present GOAL, a benchmark over 8.9k clips, 22k sentences, 42k triples for proposing challenging new task setting...
We introduce TableLLM, a robust large language model (LLM) with 13 billion parameters, purpose-built for proficiently handling tabular data manipulation tasks, whether they are embedded within documents or spreadsheets, catering to real-world office scenarios. propose distant supervision method training, which comprises reasoning process extension strategy, aiding in training LLMs understand patterns more effectively as well cross-way validation ensuring the quality of automatically...
While there are abundant researches about evaluating ChatGPT on natural language understanding and generation tasks, few studies have investigated how ChatGPT's behavior changes over time. In this paper, we collect a coarse-to-fine temporal dataset called ChatLog, consisting of two parts that update monthly daily: ChatLog-Monthly is 38,730 question-answer pairs collected every month including questions from both the reasoning classification tasks. ChatLog-Daily, other hand, consists...
As Massive Open Online Courses (MOOCs) become increasingly popular, it is promising to automatically provide extracurricular knowledge for MOOC users. Suffering from semantic drifts and lack of guidance, existing methods can not effectively expand course concepts in complex environments. In this paper, we first build a novel boundary during searching new via external base then utilize heterogeneous features verify the high-quality results. addition, involve human efforts our model, design an...
Large-scale pre-trained language models (PLMs) have shown promising advances on various downstream tasks, among which dialogue is one of the most concerned. However, there remain challenges for individual developers to create a knowledge-grounded system upon such big because expensive cost collecting knowledge resources supporting as well tuning these large task. To tackle obstacles, we propose XDAI, that equipped with prompt-aware tuning-free PLM exploitation and supported by ready-to-use...
Student modeling, the task of inferring a student's learning characteristics through their interactions with coursework, is fundamental issue in intelligent education. Although recent attempts from knowledge tracing and cognitive diagnosis propose several promising directions for improving usability effectiveness current models, existing public datasets are still insufficient to meet need these potential solutions due ignorance complete exercising contexts, fine-grained concepts, labels. In...
Teaching assistants have played essential roles in the long history of education. However, few MOOC platforms are providing human or virtual teaching to support learning for massive online students due complexity real-world education scenarios and lack training data. In this paper, we present a assistant, LittleMu with minimum labeled data, provide question answering chit-chat services. Consisting two interactive modules heterogeneous retrieval language model prompting, first integrates...
Recent advancements in pretraining have demonstrated that modern Large Language Models (LLMs) possess the capability to effectively learn arithmetic operations. However, despite acknowledging significance of digit order computation, current methodologies predominantly rely on sequential, step-by-step approaches for teaching LLMs arithmetic, resulting a conclusion where obtaining better performance involves fine-grained step-by-step. Diverging from this conventional path, our work introduces...
Applying large language models (LLMs) for academic API usage shows promise in reducing researchers' information seeking efforts. However, current LLM API-using methods struggle with complex coupling commonly encountered queries. To address this, we introduce SoAy, a solution-based methodology seeking. It uses code solution as the reasoning method, where is pre-constructed calling sequence. The addition of reduces difficulty model to understand relationships between APIs. Code improves...
Large language models (LLMs) have been employed in various intelligent educational tasks to assist teaching. While preliminary explorations focused on independent LLM-empowered agents for specific tasks, the potential LLMs within a multi-agent collaborative framework simulate classroom with real user participation remains unexplored. In this work, we propose SimClass, simulation involving participation. We recognize representative class roles and introduce novel control mechanism automatic...