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Document-grounded dialogue (DGD) uses documents as external knowledge for dialogue generation. Correctly understanding the dialogue context is crucial for selecting knowledge from the document and generating proper responses. In this paper,…

Computation and Language · Computer Science 2024-10-22 Longxuan Ma , Jiapeng Li , Mingda Li , Wei-Nan Zhang , Ting Liu

Existing conversational models are handled by a database(DB) and API based systems. However, very often users' questions require information that cannot be handled by such systems. Nonetheless, answers to these questions are available in…

Computation and Language · Computer Science 2023-04-03 Raja Kumar

Counter-speech generation is at the core of many expert activities, such as fact-checking and hate speech, to counter harmful content. Yet, existing work treats counter-speech generation as pure text generation task, mainly based on Large…

Computation and Language · Computer Science 2025-10-15 Greta Damo , Elena Cabrio , Serena Villata

Recently, topic-grounded dialogue system has attracted significant attention due to its effectiveness in predicting the next topic to yield better responses via the historical context and given topic sequence. However, almost all existing…

Computation and Language · Computer Science 2022-10-18 Xiaofei Wen , Wei Wei , Xian-Ling Mao

Retrieval augmented generation (RAG) exhibits outstanding performance in promoting the knowledge capabilities of large language models (LLMs) with retrieved documents related to user queries. However, RAG only focuses on improving the…

Information Retrieval · Computer Science 2024-06-25 Dongyang Li , Junbing Yan , Taolin Zhang , Chengyu Wang , Xiaofeng He , Longtao Huang , Hui Xue , Jun Huang

Automated insight generation is a common tactic for helping knowledge workers, such as data scientists, to quickly understand the potential value of new and unfamiliar data. Unfortunately, automated insights produced by large-language…

Software Engineering · Computer Science 2024-05-06 Ananya Singha , Bhavya Chopra , Anirudh Khatry , Sumit Gulwani , Austin Z. Henley , Vu Le , Chris Parnin , Mukul Singh , Gust Verbruggen

Generating follow-up questions on the fly could significantly improve conversational survey quality and user experiences by enabling a more dynamic and personalized survey structure. In this paper, we proposed a novel task for…

Computation and Language · Computer Science 2023-10-16 Yubin Ge , Ziang Xiao , Jana Diesner , Heng Ji , Karrie Karahalios , Hari Sundaram

Knowledge graph-based dialogue systems are capable of generating more informative responses and can implement sophisticated reasoning mechanisms. However, these models do not take into account the sparseness and incompleteness of knowledge…

Computation and Language · Computer Science 2020-04-21 Hongcai Xu , Junpeng Bao , Gaojie Zhang

In today's rapidly evolving landscape of Artificial Intelligence, large language models (LLMs) have emerged as a vibrant research topic. LLMs find applications in various fields and contribute significantly. Despite their powerful language…

Computation and Language · Computer Science 2024-09-10 Tuan Bui , Oanh Tran , Phuong Nguyen , Bao Ho , Long Nguyen , Thang Bui , Tho Quan

Question Generation (QG) is a Natural Language Processing (NLP) task that aids advances in Question Answering (QA) and conversational assistants. Existing models focus on generating a question based on a text and possibly the answer to the…

Computation and Language · Computer Science 2019-10-31 Junmo Kang , Haritz Puerto San Roman , Sung-Hyon Myaeng

Open-domain dialogue systems aim to generate relevant, informative and engaging responses. Seq2seq neural response generation approaches do not have explicit mechanisms to control the content or style of the generated response, and…

Artificial Intelligence · Computer Science 2020-08-26 Behnam Hedayatnia , Karthik Gopalakrishnan , Seokhwan Kim , Yang Liu , Mihail Eric , Dilek Hakkani-Tur

This paper proposes a novel approach to evaluate Counter Narrative (CN) generation using a Large Language Model (LLM) as an evaluator. We show that traditional automatic metrics correlate poorly with human judgements and fail to capture the…

Computation and Language · Computer Science 2024-11-05 Irune Zubiaga , Aitor Soroa , Rodrigo Agerri

Large Language Models (LLMs) can be seen as compressed knowledge bases, but it remains unclear what knowledge they truly contain and how far their knowledge boundary extends. Existing benchmarks are mostly static and provide limited support…

Machine Learning · Computer Science 2026-05-27 Yuheng Yang , Siqi Zhu , Tao Feng , Ge Liu , Jiaxuan You

Open-domain question answering (OpenQA) represents a cornerstone in natural language processing (NLP), primarily focused on extracting answers from unstructured textual data. With the rapid advancements in Large Language Models (LLMs),…

Computation and Language · Computer Science 2025-10-28 Tingjia Shen , Hao Wang , Chuan Qin , Ruijun Sun , Yang Song , Defu Lian , Hengshu Zhu , Enhong Chen

Large Language Models (LLMs) have significantly enhanced conversational Artificial Intelligence(AI) chatbots; however, domain-specific accuracy and the avoidance of factual inconsistencies remain pressing challenges, particularly for large…

Information Retrieval · Computer Science 2025-09-24 Olalekan K. Akindele , Bhupesh Kumar Mishra , Kenneth Y. Wertheim

Large pre-trained language models have been shown to store factual knowledge in their parameters, and achieve state-of-the-art results when fine-tuned on downstream NLP tasks. However, their ability to access and precisely manipulate…

Alignment of large language models (LLMs) with human preferences typically relies on supervised reward models or external judges that demand abundant annotations. However, in fields that rely on professional knowledge, such as medicine and…

Artificial Intelligence · Computer Science 2025-11-18 Yiyang Zhao , Huiyu Bai , Xuejiao Zhao

Keyphrases, that concisely summarize the high-level topics discussed in a document, can be categorized into present keyphrase which explicitly appears in the source text, and absent keyphrase which does not match any contiguous subsequence…

Computation and Language · Computer Science 2021-10-14 Jing Zhao , Junwei Bao , Yifan Wang , Youzheng Wu , Xiaodong He , Bowen Zhou

Large Language Models (LLMs) present massive inherent knowledge and superior semantic comprehension capability, which have revolutionized various tasks in natural language processing. Despite their success, a critical gap remains in…

Computation and Language · Computer Science 2025-02-11 Ben Liu , Jihai Zhang , Fangquan Lin , Cheng Yang , Min Peng

While large language models (LLMs) such as ChatGPT and PaLM have demonstrated remarkable performance in various language understanding and generation tasks, their capabilities in complex reasoning and intricate knowledge utilization still…

Computation and Language · Computer Science 2023-10-11 Haodi Zhang , Min Cai , Xinhe Zhang , Chen Jason Zhang , Rui Mao , Kaishun Wu
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