Artificial Intelligence (AI) advancements have significantly evolved voice interaction technology with the primary goal to make the interaction between humans and machines more intuitive and human-like. Recent developments have led to the attainment of high-precision speech recognition, emotion detection, and natural speech generation. Despite these advancements, voice interaction needs to improve latency, multilingual support, and…
Large Language Models (LLMs) are pivotal for numerous applications including chatbots and data analysis, chiefly due to their ability to efficiently process high volumes of textual data. The progression of AI technology has amplified the need for superior quality training data, critical for the models' function and enhancement.
A major challenge in AI development is guaranteeing…
Recent research into Predictive Large Models (PLM) aims to align the models with human values, avoiding harmful behaviors while maximising efficiency and applicability. Two significant methods used for alignment are supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF). RLHF, notably, commoditizes the reward model to new prompt-response pairs. However, this approach often faces…
The field of large language models (LLMs), such as GPT, Claude, and Gemini, has seen rapid advancement, enabling the creation of autonomous agents capable of natural language interactions and executing diverse tasks. These AI agents are increasingly benefiting from the integration of external tools and knowledge sources, which expand their capacity to access and use…
Software engineering is a rapidly evolving field aimed at systematic design, development, testing, and maintenance of software systems. In recent times, large language models (LLMs) such as GPT-3 have been employed to automate and optimize various software engineering tasks. However, the use of autonomous LLM-based agents has its challenges given their cost and complexity, and…
Language modelling, an essential tool in developing effective natural language processing (NLP) and artificial intelligence (AI) applications, has significantly benefited from advancements in algorithms that understand, generate, and manipulate human language. These advancements have catalyzed large models that can undertake tasks such as translation, summarization, and question answering. However, they face notable challenges, including difficulties…
The generation of personalized reviews within recommender systems is a burgeoning area of interest, especially in creating bespoke reviews based on users' past interactions and choices. This process involves leveraging data from users’ previous purchases and feedback to produce reviews that genuinely reflect their unique preferences and experiences, thereby improving the competency of recommender systems.
Several…
Numina has released a new language model optimized for solving mathematical problems: NuminaMath 7B TIR. With its 6.91 billion parameters, the model efficiently handles intricate mathematical queries through a specialized tool-integrated reasoning (TIR) system. Comprising a sequence of steps - creating a reasoning pathway for problem-solving, translating it into Python code, running the code in…
In recent years, the advancement of technology has allowed for the development of computer-verifiable formal languages, further advancing the field of mathematical reasoning. One of these languages, known as Lean, is an instrument employed to validate mathematical theorems, thereby ensuring accuracy and consistency in mathematical outcomes. Scholars are increasingly using Large Language Models (LLMs), specifically…
Chinese AI tech giant, SenseTime, announced a major upgrade for their flagship product SenseNova 5.5 at the 2024 World Artificial Intelligence Conference & High-Level Meeting on Global AI Governance. The update incorporates the first real-time multimodal model in China, SenseNova 5o, and demonstrates a commitment to providing innovative and practical applications in various industries.
SenseNova 5o…
Retrieval-augmented generation (RAG) is a technique that enhances large language models’ capacity to handle specific expertise, offer recent data, and tune to specific domains without changing the model’s weight. RAG, however, has its difficulties. It struggles with handling different chunked contexts efficiently, often doing better with a lesser number of highly relevant contexts. Similarly, ensuring…
A recent study by Innodata assessed various large language models (LLMs), including Llama2, Mistral, Gemma, and GPT for their factuality, toxicity, bias, and hallucination tendencies. The research used fourteen original datasets to evaluate the safety of these models based on their ability to generate factual, unbiased, and appropriate content. Ultimately, the study sought to help…