sommario
Large Language Models (LLMs) have gained significant popularity in recent years due to their remarkable question answering capabilities. However, when tackling a large corpus of text, the quality of the answers varies, largely due to the model’s inability to focus on contextualized information. This may lead to less accurate answers, poor handling of long-tail questions1 and exposure bias to the data it was pre-trained on. I present a creative approach to tackle these challenges by employing data-agents powered through LLMs. These agents employ complex workflows to intelligently perform operations over the knowledge base. These operations can be characterized as follows: 1) decompose the task into a series of function calls (thoughts), 2) employ multiple fetch operations over the knowledge base to retrieve relevant information (actions), 3) summarize at each step the extracted information to facilitate the final aggregation (observations) and 4) synthesise a final answer by combining the results. The project supports the adoption of Open LLMs, making the library usable freely without the financial burden of using proprietary providers. The library is available at https://github.com/atomwalk12/librarian.
prodotti
