Large Language Models (LLMs) have become a cornerstone of artificial intelligence, driving advancements in natural language processing and decision-making tasks. However, their extensive power demands, resulting from high computational overhead and frequent external memory access, significantly hinder their scalability and deployment, especially in energy-constrained environments such as edge devices. This escalates the cost of operation…
Despite the transformative potential of large language models (LLMs), these models face significant challenges in generating contextually accurate responses faithful to the provided input. Ensuring factuality in LLM outputs is particularly critical in tasks requiring responses grounded in lengthy, complex documents, which form the basis for advancing their applications in research, education, and industry. One…
For education research, access to high-quality educational resources is critical for learners and educators. Often perceived as one of the most challenging subjects, mathematics requires clear explanations and well-structured resources to make learning more effective. However, creating and curating datasets focusing on mathematical education remains a formidable challenge. Many datasets for training machine learning models…
Autoregressive protein language models (pLMs) have become transformative tools for designing functional proteins with remarkable diversity, demonstrating success in creating enzyme families like lysozymes and carbonic anhydrases. These models generate protein sequences by sampling from learned probability distributions, uncovering intrinsic patterns within training datasets. Despite their ability to explore high-quality subspaces of the sequence landscape,…
The rapid development of Large Language Models (LLMs) has transformed natural language processing (NLP). Proprietary models like GPT-4 and Claude 3 have set high standards in terms of performance but often come with drawbacks such as high costs, limited accessibility, and opaque methodologies. Meanwhile, many so-called open-source models fail to fully embody the ideals of…
Natural Language processing uses large language models (LLMs) to enable applications such as language translation, sentiment analysis, speech recognition, and text summarization. These models depend on human feedback-based supervised data, but relying on unsupervised data becomes necessary as they surpass human capabilities. However, the issue of alignment arises as the models get more complex and…
Large Language Models (LLMs) and neural architectures have significantly advanced capabilities, particularly in processing longer contexts. These improvements have profound implications for various applications. Enhanced context handling enables models to generate more accurate and contextually relevant responses by utilizing comprehensive information. The expanded context capacity has significantly strengthened in-context learning capabilities, allowing models to utilize…
Large Language Models (LLMs) play a vital role in many AI applications, ranging from text summarization to conversational AI. However, evaluating these models effectively remains a significant challenge. Human evaluations, while reliable, often suffer from inconsistency, high costs, and long turnaround times. Automated evaluation tools, particularly those that are closed-source, frequently lack transparency and fail…