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Machine translation, an integral branch of Natural Language Processing, is continually evolving to bridge language gaps across the globe. One persistent challenge is the translation of low-resource languages, which often need more substantial data for training robust models. Traditional translation models, primarily based on large language models (LLMs), perform well with languages abundant in data…
There has been a notable discrepancy between the global distribution of language speakers and the predominant language of online material, which is English. Even while English is used in up to 60% of internet information, only 18.8% of people worldwide speak it, and just 5.1% of people use it as their first language. For non-English…
Large language models (LLMs) face a hurdle in handling long contexts due to their constrained window length. Although the context window length can be extended through fine-tuning, this incurs significant training and inference time costs, adversely affecting the LLM’s core capabilities. Current LLMs, such as Llama-1 and Llama-2, have fixed context lengths, hindering real-world applications….
LLMs are trained on vast amounts of web data, which can lead to unintentional memorization and reproduction of sensitive or private information. This raises significant legal and ethical concerns, especially regarding violating individual privacy by disclosing personal details. To address these concerns, the concept of unlearning has emerged. This approach involves modifying models after training…
LLMs have been at the forefront of recent technological advances, demonstrating remarkable capabilities in various domains. However, enhancing these models’ reflective thinking and self-correction abilities is a significant challenge in AI development. Earlier methods, relying heavily on external feedback, often fail to enable LLMs to self-correct effectively. The Zhejiang University and OPPO Research Institute research…
Drug discovery is an essential process with applications across various scientific domains. However, Drug discovery is a very complex and time-consuming process. The traditional drug discovery approaches require extensive collaboration among teams spanning many years. Also, it involved scientists from various scientific fields working together to identify new drugs that can help the medical domain….
In sequence processing, one of the biggest challenges lies in optimizing attention mechanisms for computational efficiency. Linear attention has proven to be an efficient attention mechanism with its ability to process tokens in linear computational complexities. It has recently emerged as a promising alternative to conventional softmax attention. This theoretical advantage allows it to handle…
