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Found 23 papers, 16 papers with code
BrowseComp-ZH: Benchmarking Web Browsing Ability of Large Language Models in Chinese
We benchmark over 20 state-of-the-art language models and agentic search systems on our proposed BrowseComp-ZH.
Riemannian Geometry for the classification of brain states with intracortical brain-computer interfaces
This study investigates the application of Riemannian geometry-based methods for brain decoding using invasive electrophysiological recordings.
A Survey of Large Language Models in Psychotherapy: Current Landscape and Future Directions
Mental health remains a critical global challenge, with increasing demand for accessible, effective interventions.
Detecting Conversational Mental Manipulation with Intent-Aware Prompting
Mental manipulation severely undermines mental wellness by covertly and negatively distorting decision-making.
Applying and Evaluating Large Language Models in Mental Health Care: A Scoping Review of Human-Assessed Generative Tasks
This scoping review aimed to assess the current generative applications of LLMs in mental health care, focusing on studies where these models were tested with human participants in real-world scenarios.
Vision-Language Models Meet Meteorology: Developing Models for Extreme Weather Events Detection with Heatmaps
Real-time detection and prediction of extreme weather protect human lives and infrastructure.
Large Language Models in the Clinic: A Comprehensive Benchmark
The adoption of large language models (LLMs) to assist clinicians has attracted remarkable attention.
Large Language Models in Mental Health Care: a Scoping Review
There is a need to systematically review the application outcomes and delineate the advantages and limitations in clinical settings.
A Survey of Large Language Models in Medicine: Progress, Application, and Challenge
Therefore, this review aims to provide a detailed overview of the development and deployment of LLMs in medicine, including the challenges and opportunities they face.
Exploring Recommendation Capabilities of GPT-4V(ision): A Preliminary Case Study
Large Multimodal Models (LMMs) have demonstrated impressive performance across various vision and language tasks, yet their potential applications in recommendation tasks with visual assistance remain unexplored.
Continuous Training and Fine-tuning for Domain-Specific Language Models in Medical Question Answering
This work demonstrates a method using continuous training and instruction fine-tuning to rapidly adapt Llama 2 base models to the Chinese medical domain.
Qilin-Med-VL: Towards Chinese Large Vision-Language Model for General Healthcare
This method enhances the model's ability to generate medical captions and answer complex medical queries.
Qilin-Med: Multi-stage Knowledge Injection Advanced Medical Large Language Model
In the CPT and SFT phases, Qilin-Med achieved 38. 4% and 40. 0% accuracy on the CMExam test set, respectively.
Streamlining Social Media Information Retrieval for COVID-19 Research with Deep Learning
Conclusions: This study advances public health research by implementing a novel, systematic pipeline for curating symptom lexicons from social media data.
Benchmarking Large Language Models on CMExam -- A Comprehensive Chinese Medical Exam Dataset
To the best of our knowledge, CMExam is the first Chinese medical exam dataset to provide comprehensive medical annotations.
Rethinking Multi-Interest Learning for Candidate Matching in Recommender Systems
To address these issues, we propose the REMI framework, consisting of an Interest-aware Hard Negative mining strategy (IHN) and a Routing Regularization (RR) method.
Exploring Social Media for Early Detection of Depression in COVID-19 Patients
Secondly, We conducted an extensive analysis of this dataset to investigate the characteristic of COVID-19 patients with a higher risk of depression.
GreenPLM: Cross-Lingual Transfer of Monolingual Pre-Trained Language Models at Almost No Cost
Large pre-trained models have revolutionized natural language processing (NLP) research and applications, but high training costs and limited data resources have prevented their benefits from being shared equally amongst speakers of all the world's languages.
Equivariant Contrastive Learning for Sequential Recommendation
Therefore, we propose Equivariant Contrastive Learning for Sequential Recommendation (ECL-SR), which endows SR models with great discriminative power, making the learned user behavior representations sensitive to invasive augmentations (e. g., item substitution) and insensitive to mild augmentations (e. g., featurelevel dropout masking).
METS-CoV: A Dataset of Medical Entity and Targeted Sentiment on COVID-19 Related Tweets
To further investigate tweet users' attitudes toward specific entities, 4 types of entities (Person, Organization, Drug, and Vaccine) are selected and annotated with user sentiments, resulting in a targeted sentiment dataset with 9, 101 entities (in 5, 278 tweets).
Using Twitter Data to Understand Public Perceptions of Approved versus Off-label Use for COVID-19-related Medications
Time-trend analysis indicated that Hydroxychloroquine and Ivermectin were discussed more than Molnupiravir and Remdesivir, particularly during COVID-19 surges.
Annotating the Tweebank Corpus on Named Entity Recognition and Building NLP Models for Social Media Analysis
We release the dataset and make both the Stanza pipeline and BERTweet-based models available "off-the-shelf" for use in future Tweet NLP research.
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