Breaking Language Barriers with a LEAP: Learning Strategies for Polyglot LLMs

Large language models (LLMs) are at the forefront of transforming numerous domains globally. However, their inclusivity and effectiveness remain limited for non-Latin scripts and low-resource languages. This paper tackles the imperative challenge of enhancing the multilingual performance of LLMs, specifically focusing on Generative models. Through systematic investigation and evaluation of diverse languages using popular question-answering (QA) datasets, we present novel techniques that unlock the true potential of LLMs in a polyglot landscape. Our approach encompasses three key strategies that yield remarkable improvements in multilingual proficiency. First, by meticulously optimizing prompts tailored for polyglot LLMs, we unlock their latent capabilities, resulting in substantial performance boosts across languages. Second, we introduce a new hybrid approach that synergizes GPT generation with multilingual embeddings and achieves significant multilingual performance improvement on critical tasks like QA and retrieval. Finally, to further propel the performance of polyglot LLMs, we introduce a novel learning algorithm that dynamically selects the optimal prompt strategy, LLM model, and embeddings per query. This dynamic adaptation maximizes the efficacy of LLMs across languages, outperforming best static and random strategies. Our results show substantial advancements in multilingual understanding and generation across a diverse range of languages.

Adaptive Streaming Perception using Deep Reinforcement Learning

Executing computer vision models on streaming visual data, or streaming perception is an emerging problem, with applications in self-driving, embodied agents, and augmented/virtual reality. The development of such systems is largely governed by the accuracy and latency of the processing pipeline. While past work has proposed numerous approximate execution frameworks, their decision functions solely focus on optimizing latency, accuracy, or energy, etc. This results in sub-optimum decisions, affecting the overall system performance. We argue that the streaming perception systems should holistically maximize the overall system performance (i.e., considering both accuracy and latency simultaneously). To this end, we describe a new approach based on deep reinforcement learning to learn these tradeoffs at runtime for streaming perception. This tradeoff optimization is formulated as a novel deep contextual bandit problem and we design a new reward function that holistically integrates latency and accuracy into a single metric. We show that our agent can learn a competitive policy across multiple decision dimensions, which outperforms state-of-the-art policies on public datasets.