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Large Language Models (LLMs) have shown remarkable reasoning functions in mathematical problem solving, logical inference and programming. However, their effectiveness is often conditioned by two approaches: monitored fine tuning (SFT) with human-annoted reasoning chains and Search strategies in inference time Guided by external verifiers. While monitored fine -tuning offers structured reasoning, it requires significant annotation … Read more

The development of transformer-based large language models (LLMs) has significantly advanced AI-driven applications, especially conversation agents. However, these models face inherent limitations due to their fixed context windows, which can lead to loss of relevant information over time. While methods of recycling-augmented generation (RAG) provide external knowledge to supplement LLMs, they are often dependent on … Read more

Large -scale Language Models (LLMS) has put forward the area of ​​artificial intelligence as they are used in many applications. While they can almost perfectly simulate human language, they tend to lose in response diversity. This limitation is particularly problematic in tasks that require creativity, such as synthetic data recovery and storytelling, where different outputs … Read more