This study introduces an approach to optimize Parameter Efficient Fine Tuning (PEFT) for Pretrained Language Models (PLMs) by implementing a Shared Low Rank Adaptation (ShareLoRA). By strategically deploying ShareLoRA across different layers and adapting it for the Query, Key, and Value components of self-attention layers, we achieve a substantial reduction in the number of training parameters and memory usage. Importantly, ShareLoRA not only maintains model performance but also exhibits robustness in both classification and generation tasks across a variety of models, including RoBERTa, GPT-2, LLaMA and LLaMA2. It demonstrates superior transfer learning capabilities compared to standard LoRA applications and mitigates overfitting by sharing weights across layers. Our findings affirm that ShareLoRA effectively boosts parameter efficiency while ensuring scalable and high-quality performance across different language model architectures.

Deploying Large Language Models (LLMs) locally on mobile devices presents a significant challenge due to their extensive memory requirements. In this paper, we introduce LinguaLinked, a system for decentralized, distributed LLM inference on mobile devices. LinguaLinked enables collaborative execution of the inference task across multiple trusted devices. LinguaLinked ensures data privacy by processing information locally. LinguaLinked uses three key strategies. First, an optimized model assignment technique segments LLMs and uses linear optimization to align segments with each device's capabilities. Second, an optimized data transmission mechanism ensures efficient and structured data flow between model segments while also maintaining the integrity of the original model structure. Finally, LinguaLinked incorporates a runtime load balancer that actively monitors and redistributes tasks among mobile devices to prevent bottlenecks, enhancing the system's overall efficiency and responsiveness. We demonstrate that LinguaLinked facilitates efficient LLM inference while maintaining consistent throughput and minimal latency through extensive testing across various mobile devices, from high-end to low-end Android devices. In our evaluations, compared to the baseline, LinguaLinked achieves an inference performance acceleration of $1.11\times$ to $1.61\times$ in single-threaded settings, $1.73\times$ to $2.65\times$ with multi-threading. Additionally, runtime load balancing yields an overall inference acceleration of $1.29\times$ to $1.32\times$.

Automatic code generation from natural language descriptions can be highly beneficial during the process of software development. In this work, we propose GAP-Gen, a Guided Automatic Python Code Generation method based on Python syntactic constraints and semantic constraints. We first introduce Python syntactic constraints in the form of Syntax-Flow, which is a simplified version of Abstract Syntax Tree (AST) reducing the size and high complexity of Abstract Syntax Tree but maintaining crucial syntactic information of Python code. In addition to Syntax-Flow, we introduce Variable-Flow which abstracts variable and function names consistently through out the code. In our work, rather than pretraining, we focus on modifying the finetuning process which reduces computational requirements but retains high generation performance on automatic Python code generation task. GAP-Gen fine-tunes the transformer based language models T5 and CodeT5 using the Code-to-Docstring datasets CodeSearchNet, CodeSearchNet AdvTest and Code-Docstring Corpus from EdinburghNLP. Our experiments show that GAP-Gen achieves better results on automatic Python code generation task than previous works.

As the rapid development of Artifical Intelligence in the current technology field, Reinforcement Learning has been proven as a powerful technique that allows autonomous agents to learn optimal behaviors (called policies) in unknown and complex environments through models of rewards and penalization. However, in order to make this technique (Reinforcement Learning) work correctly and get the precise reward function, which returns the feedback to the learning agent about when the agent behaves correctly or not, the reward function needs to be thoroughly specified. As a result, in real-world complex environments, such as autonomous driving, specifying a correct reward function could be one of the hard tasks to tackle for the Reinforcement Learning model designers. To this end, Apprenticeship Learning techniques, in which the agent can infer a reward function from expert behaviors, are of high interest due to the fact that they could result in highly specified reward function efficiently. However, for critical tasks such as autonomous driving, we need to critically consider about the safety-related issues, so as to we need to build techniques to automatically check and ensure that the inferred rewards functions and policies resulted from the Reinforcement Learning model fulfill the needed safety requirements of the critical tasks that we have mentioned previously. In order to have a well-designed Reinforcement Learning model, which is able to generate the highly-specified reward function satisfying the safety-related considerations, the technique called "Safety-Aware Apprenticeship Learning" was built in 2018[23], which would be introduced in detail in the later sections. Although the technique "Safety-Aware Apprenticeship Learning" has been built, it only considers Single-Agent scenario. In the other word, the current "Safety-Aware Apprenticeship Learning" technique can only be applied to one agent running in an isolated environment, a fact which limits the potential implementation of this technique.