Large Language Model
DDCoT: Duty-Distinct Chain-of-Thought Prompting for Multimodal Reasoning in Language Models Ge Zheng 1 Bin Y ang 1 Jiajin T ang 1 Hong-Y u Zhou 2
As shown in Figure 1(d) and (e), basic science questions that are out-of-distribution pose a challenge for these models.
CiteME: CanLanguageModels AccuratelyCiteScientificClaims?
Scientific discoveries areadvancing atanever-growing rate, with tensofthousands ofnewpapers added just to arXiv every month [4]. This rapid progress has led to information overload within communities, making it nearly impossible for scientists to read all relevant papers.
SheetCopilot: Bringing Software Productivity to the Next Level through Large Language Models
Our SheetCopilot correctly completes 44.3% of tasks for
MAUVE_Evaluating_Open_Ended_Text_Generation(4)
That issettoaconstant see [22, Appendix MAUVE-Lattice.
2 Preliminaries Computational graphLet A be a deterministic algorithm and letFA be a set of deterministic primitiveoperations that can be used byA during execution. Given an inputx, wedefine the
We analyze the capabilities of Transformer language models in learning compositional discrete tasks. To this end, we evaluate training LLaMA models and prompting GPT-4 and Gemini on four tasks demanding to learn a composition of several discrete sub-tasks. In particular, we measure how well these models can reuse primitives observable in the sub-tasks to learn the composition task.
