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Found 10 papers, 8 papers with code
Sibyl: Simple yet Effective Agent Framework for Complex Real-world Reasoning
To address these limitations, we introduce Sibyl, a simple yet powerful LLM-based agent framework designed to tackle complex reasoning tasks by efficiently leveraging a minimal set of tools.
Flooding Spread of Manipulated Knowledge in LLM-Based Multi-Agent Communities
The rapid adoption of large language models (LLMs) in multi-agent systems has highlighted their impressive capabilities in various applications, such as collaborative problem-solving and autonomous negotiation.
SPO: Multi-Dimensional Preference Sequential Alignment With Implicit Reward Modeling
Human preference alignment is critical in building powerful and reliable large language models (LLMs).
Interpreting Key Mechanisms of Factual Recall in Transformer-Based Language Models
In this paper, we delve into several mechanisms employed by Transformer-based language models (LLMs) for factual recall tasks.
On the Robustness of Editing Large Language Models
RQ1: Can edited LLMs behave consistently resembling communicative AI in realistic situations?
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.
