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Found 6 papers, 3 papers with code
Large Language Models Can Plan Your Travels Rigorously with Formal Verification Tools
We evaluate our framework with TravelPlanner and achieve a success rate of 97%.
PRompt Optimization in Multi-Step Tasks (PROMST): Integrating Human Feedback and Preference Alignment
Prompt optimization aims to find the best prompt to a large language model (LLM) for a given task.
Physics-Enhanced Multi-fidelity Learning for Optical Surface Imprint
Human fingerprints serve as one unique and powerful characteristic for each person, from which policemen can recognize the identity.
AutoTAMP: Autoregressive Task and Motion Planning with LLMs as Translators and Checkers
Rather than using LLMs to directly plan task sub-goals, we instead perform few-shot translation from natural language task descriptions to an intermediate task representation that can then be consumed by a TAMP algorithm to jointly solve the task and motion plan.
NL2TL: Transforming Natural Languages to Temporal Logics using Large Language Models
Then, we finetune T5 models on the lifted versions (i. e., the specific Atomic Propositions (AP) are hidden) of the NL and TL.
FedSSC: Shared Supervised-Contrastive Federated Learning
We propose Supervised Contrastive Federated Learning in which devices can share the learned class-wise feature spaces with each other and add the supervised-contrastive learning loss as a regularization term to foster the feature space learning.
