Solving mathematical problems requires advanced reasoning abilities and presents notable
challenges for large language models. Previous works usually synthesize data from …

Instruction tuning improves the reasoning abilities of large language models (LLMs), with
data quality and scalability being the crucial factors. Most instruction tuning data come from …

Large language models (LLMs) often exhibit deficient reasoning or generate hallucinations.
To address these, studies prefixed with" Self-" such as Self-Consistency, Self-Improve, and …

The success of Large Language Models (LLMs) is inherently linked to the availability of vast,
diverse, and high-quality data for training and evaluation. However, the growth rate of high …

Going beyond mimicking limited human experiences, recent studies show initial evidence
that, like humans, large language models (LLMs) are capable of improving their abilities …

The building sector is a significant energy consumer, making building energy optimization
crucial for reducing energy demand. Automating energy optimization tasks eases the …

Reward modeling is crucial for aligning large language models (LLMs) with human
preferences, especially in reinforcement learning from human feedback (RLHF). However …

The remarkable performance of models like the OpenAI o1 can be attributed to their ability to
emulate human-like long-time thinking during inference. These models employ extended …

Large Language Models (LLMs) have emerged as powerful tools for Text-to-SQL tasks,
exhibiting remarkable reasoning capabilities. Different from tasks such as math word …

LLMs-as-a-judge is a recently popularized method which replaces human judgements in
task evaluation (Zheng et al. 2024) with automatic evaluation using LLMs. Due to …