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Researcher profile

Weixin Liu

Weixin Liu contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
astro-ph.SRComputation and LanguageArtificial IntelligenceMachine Learning

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Published work

5 published item(s)

preprint2026arXiv

MHGraphBench: Knowledge Graph-Grounded Benchmarking of Mental Health Knowledge in Large Language Models

Large language models (LLMs) are increasingly used in the mental health domain, yet it remains unclear how well they capture related biomedical knowledge and how reliably they apply it to clinically salient structured judgments. Here, we present a knowledge-graph (KG)-grounded benchmark for assessing LLMs on mental-health entity recognition, relation judgment, and two-hop reasoning. The benchmark is derived from PrimeKG and comprises nine task families with KG-supported answers and controlled negative options. Experiments across 15 closed- and open-source LLMs reveal a persistent recognition-to-judgment gap: leading models achieve near-ceiling performance on entity typing and on the small relation-typing subset, yet they still struggle with relation prediction and two-hop reasoning. Additionally, short KG-derived snippets benefit some models but degrade performance for others. Moreover, output-format reliability can substantially influence measured performance under constrained multiple-choice settings, highlighting the critical role of response validity in benchmark-based evaluation. MHGraphBench should therefore be interpreted as evaluating agreement with a curated mental-health slice of PrimeKG under a constrained multiple-choice interface, rather than as a direct assessment of real-world clinical safety.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

ERNIE 3.0 Tiny: Frustratingly Simple Method to Improve Task-Agnostic Distillation Generalization

Task-agnostic knowledge distillation attempts to address the problem of deploying large pretrained language model in resource-constrained scenarios by compressing a large pretrained model called teacher into a smaller one called student such that the student can be directly finetuned on downstream tasks and retains comparable performance. However, we empirically find that there is a generalization gap between the student and the teacher in existing methods. In this work, we show that we can leverage multi-task learning in task-agnostic distillation to advance the generalization of the resulted student. In particular, we propose Multi-task Infused Task-agnostic Knowledge Distillation (MITKD). We first enhance the teacher by multi-task training it on multiple downstream tasks and then perform distillation to produce the student. Experimental results demonstrate that our method yields a student with much better generalization, significantly outperforms existing baselines, and establishes a new state-of-the-art result on in-domain, out-domain, and low-resource datasets in the setting of task-agnostic distillation. Moreover, our method even exceeds an 8x larger BERT$_{\text{Base}}$ on SQuAD and four GLUE tasks. In addition, by combining ERNIE 3.0, our method achieves state-of-the-art results on 10 Chinese datasets.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Learning Weakly-Supervised Contrastive Representations

We argue that a form of the valuable information provided by the auxiliary information is its implied data clustering information. For instance, considering hashtags as auxiliary information, we can hypothesize that an Instagram image will be semantically more similar with the same hashtags. With this intuition, we present a two-stage weakly-supervised contrastive learning approach. The first stage is to cluster data according to its auxiliary information. The second stage is to learn similar representations within the same cluster and dissimilar representations for data from different clusters. Our empirical experiments suggest the following three contributions. First, compared to conventional self-supervised representations, the auxiliary-information-infused representations bring the performance closer to the supervised representations, which use direct downstream labels as supervision signals. Second, our approach performs the best in most cases, when comparing our approach with other baseline representation learning methods that also leverage auxiliary data information. Third, we show that our approach also works well with unsupervised constructed clusters (e.g., no auxiliary information), resulting in a strong unsupervised representation learning approach.

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preprint2021arXiv

Population of Bright Plume Threads in Solar Polar Coronal Holes

Coronal holes are well accepted to be source regions of the fast solar wind. As one of the common structures in coronal holes, coronal plumes might contribute to the origin of the nascent solar wind. To estimate the contribution of coronal plumes to the nascent solar wind, we make the first attempt to estimate their populations in solar polar coronal holes. By comparing the observations viewed from two different angles taken by the twin satellites of STEREO and the results of Monte Carlo simulations, we estimate about 16--27 plumes rooted in an area of $4\times10^4$ arcsec$^2$ of the polar coronal holes near the solar minimum, which occupy about 2--3.4% of the area. Based on these values, the contribution of coronal plumes to the nascent solar wind has also been discussed. A further investigation indicates that more precise number of coronal plumes can be worked out with observations from three or more viewing angles.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Statistical properties of Hα jets in the polar coronal hole and their implications in coronal activities

Dynamic features, such as chromospheric jets, transition region network jets, coronal plumes and coronal jets, are abundant in the network regions of the solar polar coronal holes. We investigate the relationship between chromospheric jets and coronal activities (e.g., coronal plumes and jets).We analyze observations of a polar coronal hole including the filtergrams that were taken by the New Vacuum Solar Telescope (NVST) at the Hα-0.6 Åto study the Hα jets,and the Atmospheric Imaging Assembly (AIA) 171 Å images to follow the evolution of coronal activities. Hα jets are persistent in the network regions, only some regions (denoted as R1-R5) are rooted with discernible coronal plumes.With an automated method, we identify and track 1 320 Hα jets in the network regions. We find that the average lifetime, height and ascending speed of the Hα jets are 75.38 s, 2.67 Mm, 65.60 km s$^{-1}$, respectively. The Hα jets rooted in R1-R5 are higher and faster than those in the others. We also find that propagating disturbances (PDs) in coronal plumes have a close connection with the Hα jets. The speeds of 28 out of 29 Hα jets associated with PDs are about 50 km s$^{-1}$ . In a case of coronal jet, we find that the speeds of both the coronal jet and the Hα jet are over 150 km s$^{-1}$, suggesting that both cool and hot jets can be coupled together. Based on our analyses, it is evident that more dynamic Hα jets could release the energies to the corona, which might be the results of the development of Kelvin-Helmholtz instability (KHi) or small-scaled magnetic activities. We suggest that chromospheric jets, transition region network jets and ray-like features in the corona are coherent phenomena, and they are important tunnels for cycling energy and mass in the solar atmosphere.

0 citations0 reviewsNo code linkedOpen access