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Artificial Intelligence

CS-Bench: A Dual-language (Chinese-English) Standard for Assessing the Efficiency of LLMs in the Field of Computer Science.

Artificial Intelligence (AI) continues to evolve rapidly, with large language models (LLMs) demonstrating vast potential across diverse fields. However, optimizing the potential of LLMs in the field of computer science has been a challenge due to the lack of comprehensive assessment tools. Researchers have conducted studies within computer science, but they often either broadly evaluate…

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Reducing Memory Reliance in Language Models: The Goldfish Loss Method

Language learning models (LLMs) are capable of memorizing and reproducing their training data, which can create substantial privacy and copyright issues, particularly in commercial environments. These concerns are especially important for models that generate code as they may unintentionally reuse code snippets verbatim, thereby conflicting with licensing terms that restrict commercial use. Moreover, models may…

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CS-Bench: A Dual-Language (Chinese-English) Standard for Assessing the Effectiveness of Language Models in Computer Science

The realm of artificial intelligence has been widely influenced by the emergence of large language models (LLMs), with their potential being seen across multiple fields. However, the task of enabling these models to efficiently utilize knowledge of computer science and to benefit humanity remains a challenge. Although many studies have been conducted across various disciplines,…

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Designing domestic robots to possess a bit of general knowledge.

As robots are increasingly being deployed for complex household tasks, engineers at MIT are trying to equip them with common-sense knowledge allowing them to swiftly adapt when faced with disruptions. A newly developed method by the researchers merges robot motion data and common-sense knowledge from extensive language models (LLMs). The new approach allows a robot to…

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Fireworks AI has unveiled Firefunction-v2: A freely accessible weights function calling model featuring function calling capacity that matches GPT4o. Interestingly, it operates at a speed that’s 2.5 times faster and costs just a tenth of the price.

Fireworks AI recently launched Firefunction-v2, an open-source function-calling model aiming to deliver superior performance in real-world applications. The model integrates with multi-turn conversations, instruction following, and parallel function calling, providing a powerful and effective solution comparable to more advanced models such as GPT-4o, but with increased speed, better functionality, and lower costs. Firefunction-v2's robustness and…

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