Large language models (LLMs) such as ChatGPT have shown remarkable capabilities in
code generation. Despite significant achievements, they rely on enormous training data to …

Recent advances in few-shot question answering (QA) mostly rely on the power of pre-
trained large language models (LLMs) and fine-tuning in specific settings. Although the pre …

Large language models present opportunities for innovative Question Answering over
Knowledge Graphs (KGQA). However, they are not inherently designed for query …

Personalized review generation is significant for e-commerce applications, such as
providing explainable recommendation and assisting the composition of reviews. With the …

Conversational Tree Search (V\" ath et al., 2023) is a recent approach to controllable dialog
systems, where domain experts shape the behavior of a Reinforcement Learning agent …

Few-shot extractive question answering (EQA) is a challenging task in natural language
processing, whose current methods are mainly based on pretrained language models …

In the era of large models, low-resource question-answering tasks lag, emphasizing the
importance of data augmentation-a key research avenue in natural language processing …

In Natural Language Processing, creating training data for question answering (QA) systems
typically requires significant effort and expertise. This challenge is amplified in few-shot …

Current Large Language Model (LLM) agents demonstrate strong reasoning and tool use
capabilities, but often lack self-awareness, failing to balance these approaches effectively …

In recent years, prompt-based tuning and instruction-based tuning have emerged as popular
approaches for natural language processing. In this paper, we investigate the application of …