Natural Language Generation (NLG) has improved exponentially in recent years thanks to
the development of sequence-to-sequence deep learning technologies such as Transformer …

Large language models (LLMs) have shown promise for generative and knowledge-
intensive tasks including question-answering (QA) tasks. However, the practical deployment …

The rise of Large Language Models (LLMs) has significantly advanced many applications
on software engineering tasks, particularly in code generation. Despite the promising …

The goal of information-seeking dialogue is to respond to seeker queries with natural
language utterances that are grounded on knowledge sources. However, dialogue systems …

Ideally, dialogue systems should generate responses that are faithful to the knowledge
contained in relevant documents. However, many models generate hallucinated responses …

Reasoning is critical for large language models (LLMs) to excel in a wide range of tasks.
While methods like Chain-of-Thought (CoT) reasoning enhance LLM performance by …

Today's large language models (LLMs) can solve challenging question-answering tasks,
and prompt engineering techniques, such as chain-of-thought (CoT), have gained attention …

Product attribute extraction is an emerging field in information extraction and e-commerce,
with applications including knowledge base construction, product recommendation, and …

In this paper, we address the hallucination problem commonly found in natural language
generation tasks. Language models often generate fluent and convincing content but can …

Abstract The advent of Generative Artificial Intelligence (Generative AI or GAI) marks a
significant inflection point in AI development. Long viewed as the epitome of reasoning and …