Search Results for author:
Found 7 papers, 4 papers with code
Interpreting Key Mechanisms of Factual Recall in Transformer-Based Language Models
In this paper, we deeply explore the mechanisms employed by Transformer-based language models in factual recall tasks.
Is it Possible to Edit Large Language Models Robustly?
Q3: Which knowledge features are correlated with the performance and robustness of editing?
Baichuan 2: Open Large-scale Language Models
Large language models (LLMs) have demonstrated remarkable performance on a variety of natural language tasks based on just a few examples of natural language instructions, reducing the need for extensive feature engineering.
Knowledge-Guided Exploration in Deep Reinforcement Learning
This paper proposes a new method to drastically speed up deep reinforcement learning (deep RL) training for problems that have the property of state-action permissibility (SAP).
Evaluation Framework For Large-scale Federated Learning
Federated learning is proposed as a machine learning setting to enable distributed edge devices, such as mobile phones, to collaboratively learn a shared prediction model while keeping all the training data on device, which can not only take full advantage of data distributed across millions of nodes to train a good model but also protect data privacy.
Guided Exploration in Deep Reinforcement Learning
This paper proposes a new method to drastically speed up deep reinforcement learning (deep RL) training for problems that have the property of \textit{state-action permissibility} (SAP).
A novel DDPG method with prioritized experience replay
Recently, a state-of-the-art algorithm, called deep deterministic policy gradient (DDPG), has achieved good performance in many continuous control tasks in the MuJoCo simulator.
