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 …

Large language models (LLMs) have emerged as pivotal contributors in contemporary
natural language processing and are increasingly being applied across a diverse range of …

Since the advent of ChatGPT, Large Language Models (LLMs) have excelled in various
tasks but remain as black-box systems. Consequently, the reasoning bottlenecks of LLMs …

Expert-designed close-ended benchmarks are indispensable in assessing the knowledge
capacity of large language models (LLMs). Despite their widespread use, concerns have …

The rapid evolution of large language models (LLMs) has transformed the competitive
landscape in natural language processing (NLP), particularly for English and other data-rich …

Refusal-Aware Instruction Tuning (RAIT) aims to enhance Large Language Models (LLMs)
by improving their ability to refuse responses to questions beyond their knowledge, thereby …

Traditional Mixture-of-Experts (MoE) networks benefit from utilizing multiple smaller expert
models as opposed to a single large network. However, these experts typically operate …

As the application of Large Language Models (LLMs) expands, the demand for reliable
evaluations increases. Existing LLM evaluation benchmarks primarily rely on static datasets …

Natural Language Processing has moved rather quickly from modelling specific tasks to
taking more general pre-trained models and fine-tuning them for specific tasks, to a point …

Large language models (LLMs) have demonstrated performance approaching human levels
in tasks such as long-text comprehension and mathematical reasoning, but they remain …