Data annotation generally refers to the labeling or generating of raw data with relevant
information, which could be used for improving the efficacy of machine learning models. The …

Modern artificial intelligence (AI) systems are powered by foundation models. This paper
presents a new set of foundation models, called Llama 3. It is a herd of language models …

Mathematical reasoning serves as a cornerstone for assessing the fundamental cognitive
capabilities of human intelligence. In recent times, there has been a notable surge in the …

Since the advent of personal computing devices, intelligent personal assistants (IPAs) have
been one of the key technologies that researchers and engineers have focused on, aiming …

As Large Language Models (LLMs) have shown significant intelligence, the progress to
leverage LLMs as planning modules of autonomous agents has attracted more attention …

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 …

Direct Preference Optimisation (DPO) is effective at significantly improving the performance
of large language models (LLMs) on downstream tasks such as reasoning, summarisation …

Mathematical capabilities were previously believed to emerge in common language models
only at a very large scale or require extensive math-related pre-training. This paper shows …

Confidence estimation aiming to evaluate output trustability is crucial for the application of
large language models (LLM), especially the black-box ones. Existing confidence estimation …

Common self-improvement approaches for large language models (LLMs), such as STaR
(Zelikman et al., 2022), iteratively fine-tune LLMs on self-generated solutions to improve …