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Found 6 papers, 2 papers with code
MANGO: A Benchmark for Evaluating Mapping and Navigation Abilities of Large Language Models
The task is question-answering: for each maze, a large language model reads the walkthrough and answers hundreds of mapping and navigation questions such as "How should you go to Attic from West of House?"
Personality Understanding of Fictional Characters during Book Reading
As readers engage with a story, their understanding of a character evolves based on new events and information; and multiple fine-grained aspects of personalities can be perceived.
DeepTree: Modeling Trees with Situated Latents
We use a neural network pipeline to train a situated latent space that allows us to locally predict branch growth only based on a single node in the branch graph of a tree model.
Can Large Language Models Play Text Games Well? Current State-of-the-Art and Open Questions
Large language models (LLMs) such as ChatGPT and GPT-4 have recently demonstrated their remarkable abilities of communicating with human users.
Source-Target Unified Knowledge Distillation for Memory-Efficient Federated Domain Adaptation on Edge Devices
Moreover, to prevent the compact model from forgetting the knowledge of the source data during knowledge distillation, a collaborative knowledge distillation (Co-KD) method is developed to unify the source data on the server and the target data on the edge device to train the compact model.
A Communication Efficient Federated Kernel $k$-Means
Theoretical analysis shows: 1) DSPGD with CEM converges with an $O(1/T)$ rate, where $T$ is the number of iterations; 2) the communication cost of DSPGD with CEM is unrelated to the number of data samples; 3) the clustering loss of the federated kernel $k$-means can approach that of the centralized kernel $k$-means.
