Large Language Models (LLMs) have demonstrated proficiency in their reasoning abilities,
yet their large size presents scalability challenges and limits any further customization. In …

Large Language Models (LLMs) prompted to generate chain-of-thought (CoT) exhibit
impressive reasoning capabilities. Recent attempts at prompt decomposition toward solving …

Large Language Models (LLMs) can solve complex reasoning tasks by generating
rationales for their predictions. Distilling these capabilities into a smaller, compact model can …

Recent methods have demonstrated that Large Language Models (LLMs) can solve
reasoning tasks better when they are encouraged to solve subtasks of the main task first. In …

Large language models (LLMs) are typically fine-tuned on diverse and extensive datasets
sourced from various origins to develop a comprehensive range of skills, such as writing …

Large language models (LLMs) have recently shown impressive performance on tasks
involving reasoning, leading to a lively debate on whether these models possess reasoning …

Large Language Models (LLMs) are showcasing impressive ability in handling complex
reasoning tasks. In real-world situations, problems often span a spectrum of complexities …

Large Language Models (LLMs) have been achieving competent performance on a wide
range of downstream tasks, yet existing work shows that inference on structured data is …

Large Language Models (LLMs) have demonstrated impressive performance across various
downstream tasks. When training these models, there is a growing inclination to process …

Orca 1 learns from rich signals, such as explanation traces, allowing it to outperform
conventional instruction-tuned models on benchmarks like BigBench Hard and AGIEval. In …