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Found 14 papers, 9 papers with code
A Three-step Method for Multi-Hop Inference Explanation Regeneration
Then, a selection module is employed to choose those most relative facts in an autoregressive manner, giving a preliminary order for the retrieved facts.
MedDM:LLM-executable clinical guidance tree for clinical decision-making
To address this issue, we first propose LLM-executavle clinical guidance tree(CGT), which can be directly used by large language models, and construct medical diagnostic decision-making dataset (MedDM), from flowcharts in clinical practice guidelines.
Time-LLM: Time Series Forecasting by Reprogramming Large Language Models
We begin by reprogramming the input time series with text prototypes before feeding it into the frozen LLM to align the two modalities.
LLM-Mini-CEX: Automatic Evaluation of Large Language Model for Diagnostic Conversation
Despite their alluring technological potential, there is no unified and comprehensive evaluation criterion, leading to the inability to evaluate the quality and potential risks of medical LLMs, further hindering the application of LLMs in medical treatment scenarios.
EasyTPP: Towards Open Benchmarking Temporal Point Processes
In this paper, we present EasyTPP, the first central repository of research assets (e. g., data, models, evaluation programs, documentations) in the area of event sequence modeling.
MidMed: Towards Mixed-Type Dialogues for Medical Consultation
To mitigate this challenge, we propose a novel task and create a human-to-human mixed-type medical consultation dialogue corpus, termed MidMed, covering five dialogue types: task-oriented dialogue for diagnosis, recommendation, knowledge-grounded dialogue, QA, and chitchat.
Language Models Can Improve Event Prediction by Few-Shot Abductive Reasoning
Large language models have shown astonishing performance on a wide range of reasoning tasks.
MedGPTEval: A Dataset and Benchmark to Evaluate Responses of Large Language Models in Medicine
The responses generated by chatbots based on LLMs are recorded for blind evaluations by five licensed medical experts.
Full Scaling Automation for Sustainable Development of Green Data Centers
The rapid rise in cloud computing has resulted in an alarming increase in data centers' carbon emissions, which now accounts for >3% of global greenhouse gas emissions, necessitating immediate steps to combat their mounting strain on the global climate.
A Graph Regularized Point Process Model For Event Propagation Sequence
Point process is the dominant paradigm for modeling event sequences occurring at irregular intervals.
AF Adapter: Continual Pretraining for Building Chinese Biomedical Language Model
Continual pretraining is a popular way of building a domain-specific pretrained language model from a general-domain language model.
HYPRO: A Hybridly Normalized Probabilistic Model for Long-Horizon Prediction of Event Sequences
In this paper, we tackle the important yet under-investigated problem of making long-horizon prediction of event sequences.
A Meta Reinforcement Learning Approach for Predictive Autoscaling in the Cloud
Predictive autoscaling (autoscaling with workload forecasting) is an important mechanism that supports autonomous adjustment of computing resources in accordance with fluctuating workload demands in the Cloud.
Bellman Meets Hawkes: Model-Based Reinforcement Learning via Temporal Point Processes
We consider a sequential decision making problem where the agent faces the environment characterized by the stochastic discrete events and seeks an optimal intervention policy such that its long-term reward is maximized.
