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Guoping Hu

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9 papers

2 author rows

ICML Conference 2025 Conference Paper

Evaluating LLMs Across Multi-Cognitive Levels: From Medical Knowledge Mastery to Scenario-Based Problem Solving

Yuxuan Zhou 0002
Xien Liu
Chenwei Yan
Chen Ning
Xiao Zhang 0001
Boxun Li
Xiangling Fu
Shijin Wang 0001

Large language models (LLMs) have demonstrated remarkable performance on various medical benchmarks, but their capabilities across different cognitive levels remain underexplored. Inspired by Bloom’s Taxonomy, we propose a multi-cognitive-level evaluation framework for assessing LLMs in the medical domain in this study. The framework integrates existing medical datasets and introduces tasks targeting three cognitive levels: preliminary knowledge grasp, comprehensive knowledge application, and scenario-based problem solving. Using this framework, we systematically evaluate state-of-the-art general and medical LLMs from six prominent families: Llama, Qwen, Gemma, Phi, GPT, and DeepSeek. Our findings reveal a significant performance decline as cognitive complexity increases across evaluated models, with model size playing a more critical role in performance at higher cognitive levels. Our study highlights the need to enhance LLMs’ medical capabilities at higher cognitive levels and provides insights for developing LLMs suited to real-world medical applications.

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NeurIPS Conference 2025 Conference Paper

FACT: Mitigating Inconsistent Hallucinations in LLMs via Fact-Driven Alternating Code-Text Training

Xinxin You
Qixin Sun
Chenwei Yan
Xiao Zhang
Chen Ning
Xiangling Fu
Si Liu
Guoping Hu

Inconsistent hallucinations remain a major challenge for large language models (LLMs), undermining the accuracy and reliability of fact-based reasoning in real-world applications. Existing approaches often rely on task-specific training or adaptation, such as hand-crafted synthetic datasets for domain tasks or solutions mainly focused on numerical reasoning, thereby limiting generalizability to broader, unseen NLP tasks. Inspired by the structural rigor and logical consistency of programming languages, we observe that fact-based texts can be mapped to programming structures due to their inherent patterns. We further propose FACT, a novel Fact-driven Alternating Code-text Training framework that alternates between text-to-code and code-to-text prediction. FACT is the first task-agnostic paradigm that embeds code and natural language in a shared semantic space, thereby transferring the logical consistency of code to LLM outputs in NLP tasks. Experiments show that with only a small subset of Wiki-40B-en for training, FACT reduces inconsistent hallucinations by 2. 7%–8. 0% and improves overall performance by 2. 5%–6. 1% in three leading LLMs and four diverse datasets covering QA and summarization tasks. This framework offers a new perspective on addressing challenging hallucinations in LLMs, contributing to more reliable AI.

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NeurIPS Conference 2025 Conference Paper

Investigating and Mitigating Catastrophic Forgetting in Medical Knowledge Injection through Internal Knowledge Augmentation Learning

Yuxuan Zhou
Xien Liu
Xiao Zhang
Chen Ning
Shijin Wang
Guoping Hu
Ji Wu

Large Language Models (LLMs) are expected to possess comprehensive medical knowledge to support real-world clinical applications. While domain-specific fine-tuning effectively injects medical knowledge into LLMs, it often causes catastrophic forgetting of previously acquired knowledge and instruction-following capabilities. In this paper, we investigate this issue and reveal a pattern of proximity-dependent forgetting: knowledge that is semantically or topically close to the injected content is more likely to be forgotten, while unrelated knowledge shows minimal degradation. Moreover, we observe that existing mitigation techniques fail to address this type of forgetting effectively. Motivated by this observation and inspired by human learning mechanisms, we proposeInternAL (\Internal Knowledge Augmentation Learning), a novel approach that leverages LLMs' own internal knowledge to mitigate forgetting. InternAL first probes internal knowledge closely related to the injection by prompting the model with questions derived from the injected knowledge. This knowledge is then used to augment the original injection dataset, guiding the model to retain related prior knowledge during training. Experimental results on multiple LLMs (LLaMA, Qwen) demonstrate that InternAL significantly mitigates proximity-related forgetting while maintaining strong knowledge injection performance. Our findings provide new insights into the nature of catastrophic forgetting in medical knowledge injection and highlight a promising direction for robust domain adaptation in LLMs. Code and datasets are available at https: //github. com/THUMLP/InternAL.

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AAAI Conference 2020 Conference Paper

Discriminative Sentence Modeling for Story Ending Prediction

Yiming Cui
Wanxiang Che
Wei-Nan Zhang
Ting Liu
Shijin Wang
Guoping Hu

Story Ending Prediction is a task that needs to select an appropriate ending for the given story, which requires the machine to understand the story and sometimes needs commonsense knowledge. To tackle this task, we propose a new neural network called Diff-Net for better modeling the differences of each ending in this task. The proposed model could discriminate two endings in three semantic levels: contextual representation, story-aware representation, and discriminative representation. Experimental results on the Story Cloze Test dataset show that the proposed model siginiﬁcantly outperforms various systems by a large margin, and detailed ablation studies are given for better understanding our model. We also carefully examine the traditional and BERT-based models on both SCT v1. 0 and v1. 5 with interesting ﬁndings that may potentially help future studies.

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AAAI Conference 2019 Conference Paper

Convolutional Spatial Attention Model for Reading Comprehension with Multiple-Choice Questions

Zhipeng Chen
Yiming Cui
Wentao Ma
Shijin Wang
Guoping Hu

Machine Reading Comprehension (MRC) with multiplechoice questions requires the machine to read given passage and select the correct answer among several candidates. In this paper, we propose a novel approach called Convolutional Spatial Attention (CSA) model which can better handle the MRC with multiple-choice questions. The proposed model could fully extract the mutual information among the passage, question, and the candidates, to form the enriched representations. Furthermore, to merge various attention results, we propose to use convolutional operation to dynamically summarize the attention values within the different size of regions. Experimental results show that the proposed model could give substantial improvements over various state-of- the-art systems on both RACE and SemEval-2018 Task11 datasets.

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AAAI Conference 2018 Conference Paper

Exercise-Enhanced Sequential Modeling for Student Performance Prediction

Yu Su
Qingwen Liu
Qi Liu
Zhenya Huang
Yu Yin
Enhong Chen
Chris Ding
Si Wei

In online education systems, for offering proactive services to students (e. g. , personalized exercise recommendation), a crucial demand is to predict student performance (e. g. , scores) on future exercising activities. Existing prediction methods mainly exploit the historical exercising records of students, where each exercise is usually represented as the manually labeled knowledge concepts, and the richer information contained in the text descriptions of exercises is still underexplored. In this paper, we propose a novel Exercise-Enhanced Recurrent Neural Network (EERNN) framework for student performance prediction by taking full advantage of both student exercising records and the text of each exercise. Specifically, for modeling the student exercising process, we ﬁrst design a bidirectional LSTM to learn each exercise representation from its text description without any expertise and information loss. Then, we propose a new LSTM architecture to trace student states (i. e. , knowledge states) in their sequential exercising process with the combination of exercise representations. For making ﬁnal predictions, we design two strategies under EERNN, i. e. , EERNNM with Markov property and EERNNA with Attention mechanism. Extensive experiments on large-scale real-world data clearly demonstrate the effectiveness of EERNN framework. Moreover, by incorporating the exercise correlations, EERNN can well deal with the cold start problems from both student and exercise perspectives.

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TIST Journal 2018 Journal Article

Fuzzy Cognitive Diagnosis for Modelling Examinee Performance

Qi Liu
Runze Wu
Enhong Chen
Guandong Xu
Yu Su
Zhigang Chen
Guoping Hu

Recent decades have witnessed the rapid growth of educational data mining (EDM), which aims at automatically extracting valuable information from large repositories of data generated by or related to people’s learning activities in educational settings. One of the key EDM tasks is cognitive modelling with examination data, and cognitive modelling tries to profile examinees by discovering their latent knowledge state and cognitive level (e.g. the proficiency of specific skills). However, to the best of our knowledge, the problem of extracting information from both objective and subjective examination problems to achieve more precise and interpretable cognitive analysis remains underexplored. To this end, we propose a fuzzy cognitive diagnosis framework (FuzzyCDF) for examinees’ cognitive modelling with both objective and subjective problems. Specifically, to handle the partially correct responses on subjective problems, we first fuzzify the skill proficiency of examinees. Then we combine fuzzy set theory and educational hypotheses to model the examinees’ mastery on the problems based on their skill proficiency. Finally, we simulate the generation of examination score on each problem by considering slip and guess factors. In this way, the whole diagnosis framework is built. For further comprehensive verification, we apply our FuzzyCDF to three classical cognitive assessment tasks, i.e., predicting examinee performance, slip and guess detection, and cognitive diagnosis visualization. Extensive experiments on three real-world datasets for these assessment tasks prove that FuzzyCDF can reveal the knowledge states and cognitive level of the examinees effectively and interpretatively.

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AAAI Conference 2017 Conference Paper

Question DifÞculty Prediction for READING Problems in Standard Tests

Zhenya Huang
Qi Liu
Enhong Chen
Hongke Zhao
Mingyong Gao
Si Wei
Yu Su
Guoping Hu

Standard tests aim to evaluate the performance of examinees using different tests with consistent difﬁculties. Thus, a critical demand is to predict the difﬁculty of each test question before the test is conducted. Existing studies are usually based on the judgments of education experts (e. g. , teachers), which may be subjective and labor intensive. In this paper, we propose a novel Test-aware Attention-based Convolutional Neural Network (TACNN) framework to automatically solve this Question Difﬁculty Prediction (QDP) task for READ- ING problems (a typical problem style in English tests) in standard tests. Speciﬁcally, given the abundant historical test logs and text materials of questions, we ﬁrst design a CNNbased architecture to extract sentence representations for the questions. Then, we utilize an attention strategy to qualify the difﬁculty contribution of each sentence to questions. Considering the incomparability of question difﬁculties in different tests, we propose a test-dependent pairwise strategy for training TACNN and generating the difﬁculty prediction value. Extensive experiments on a real-world dataset not only show the effectiveness of TACNN, but also give interpretable insights to track the attention information for questions.

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IJCAI Conference 2015 Conference Paper

Cognitive Modelling for Predicting Examinee Performance

Runze Wu
Qi Liu
Yuping Liu
Enhong Chen
Yu Su
Zhigang Chen
Guoping Hu

Cognitive modelling can discover the latent characteristics of examinees for predicting their performance (i. e. scores) on each problem. As cognitive modelling is important for numerous applications, e. g. personalized remedy recommendation, some solutions have been designed in the literature. However, the problem of extracting information from both objective and subjective problems to get more precise and interpretable cognitive analysis is still underexplored. To this end, we propose a fuzzy cognitive diagnosis framework (FuzzyCDF) for examinees’ cognitive modelling with both objective and subjective problems. Specifically, to handle the partially correct responses on subjective problems, we first fuzzify the skill proficiency of examinees. Then, we combine fuzzy set theory and educational hypotheses to model the examinees’ mastery on the problems. Further, we simulate the generation of examination scores by considering both slip and guess factors. Extensive experiments on three realworld datasets prove that FuzzyCDF can predict examinee performance more effectively, and the output of FuzzyCDF is also interpretative.

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