Focus: Solve complex tasks through multi-turn interactions using tools and leveraging natural language feedback Numbers of Evaluation Categories/Subcategories: 3/- Evaluation Category: Code generation, Decision making, Reasoning Domain: Multi-turn interactions PromptBench 2023-6 | All | EN | CI | Pap...
Properly designed benchmark tasks and datasets are a crucial resource to assess the capability of LLMs, however, the current 6 benchmark tasks have yet to address more complex financial NLP tasks. In this section, we present 8 advanced benchmark tasks and compile associated datasets for each. ...
Although Large Language Models (LLMs) andLarge Multimodal Models(LMMs) exhibit impressive skills in various domains, their ability for mathematical reasoning within visual contexts has not been formally examined. Equipping LLMs and LMMs with this capability is vital for general-purpose AI assistants an...
Iterative steps with precise scoring significantly improve performance for complex reasoning tasks. 📚 References: Merge LLMs with mergekit by Maxime Labonne: Tutorial about model merging using mergekit. Smol Vision by Merve Noyan: Collection of notebooks and scripts dedicated to small multimodal ...
better understand the structure of language, laying the groundwork for the development of small language models. Improvements in ML techniques,GPUsand other AI-related technology in the years that followed enabled developers to create more intricate language models that could handle more complex tasks....
The book goes beyond the basics, giving a closer look at the technical side of things like word embeddings and large language models, and discusses how to spot the use of Generative AI. The future of Generative AI is exciting, and the book doesn't stop at theory. It offers over 75 ...
This results in more accurate thematic representations, especially in complex texts where the meaning of a word can vary depending on its context. Additionally, bge-large-en-v1.5’s adaptability across different usage contexts is evidenced by its robust performance in various evaluation tasks. In ...
[Snipped… the actual reply contained more informative stuff] Not bad for a model running on my MacBook M1 Max. It also mixed the sums with XORs. In this case, the model was certainly helped by the fact that I provided clues about the problem to solve, but it was the model that ...
Labelbox's new fact-checking and prompt-rating tools improve LLM accuracy and reasoning capabilities by allowing users to evaluate responses, correct errors, and flag bad prompts. Michał Jóźwiak•December 12, 2024 Inside the matrix: A look into the math behind AI Matrices are crucial in ...
A survey on rag meeting llms: Towards retrieval-augmented large language models In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2024), pp. 6491-6501 CrossrefView in ScopusGoogle Scholar [46] S. Zeng et al.,“The good and the bad: Exploring privacy...