Domain-specific Question Answering Research Articles

Improving the Capabilities of Large Language Model based Marketing Analytics Copilots with Semantic Search and Fine-Tuning

Artificial intelligence (AI) is widely deployed to solve problems related to marketing attribution and budget optimization. However, AI models can be quite complex, and it can be difficult to understand model workings and insights without extensive implementation teams. In principle, recently developed large language models (LLMs), like GPT-4, can be deployed to provide marketing insights, reducing the time and effort required to make critical decisions. In practice, there are substantial challenges that need to be overcome to reliably usesuch models. We focus on domain-specific question-answering, SQL generation needed for data retrieval, and tabular analysis and show how a combination of semantic search, prompt engineering, and fine-tuning can be applied to dramatically improve the ability of LLMs to execute these tasks accurately. We compare both proprietary models, like GPT-4, and open-source models, like Llama-2-70b, as well as various embedding methods. These models are tested on sample use cases specific to marketing mix modeling and attribution.

FAAQA-QAD: A Frequently Asked Arabic Question Answering Dataset for Diseases Detection System

Domain-specific question-answering (QA) systems are a specialized field in Natural Language Processing. Their objective is to generate answers to questions within a specific domain, such as healthcare. The existing Arabic datasets are often characterized by their low quality, resulting from inadequate preprocessing or inaccurate annotation because that translated from another language.

DGC-CRL: Dependency Graph Convolution based Contrastive Representation Learning for Chinese Medical Question Matching

As one kind of domain-specific question answering (QA) systems, the medical QA systems require much more stability, fast system speed and response accuracy. Therefore, the retrieval based QA systems are more suitable, among which the deep semantic matching models become prevalent to be studied and they are playing the very important role on the quality of retrieval based medical QA systems. In this paper, we propose a two-stage solution (named with Dependency Graph Convolution based Contrastive Representation Learning) which includes a dependency graph convolution module to explicitly capture the semantic similarity between Chinese questions. At the first stage, we adopt the contrastive learning to further distinguish the similarity within the domain-specific corpus itself and learn discriminative textual representations. At the second stage, the down-streaming question matching task is benefited by using the newly-learned representations. In our experiments, we collect two Chinese medical datasets (CBLUE-STS and COVID-19) and the results can demonstrate that our proposed method is effective and general to different medical QA corpora. Also the ablation experiments indicate the proposed Dependency Graph Convolution module and contrastive learning method are both efficient.

FactQA: question answering over domain knowledge graph based on two-level query expansion

PurposeWith the advent of the era of Big Data, the scale of knowledge graph (KG) in various domains is growing rapidly, which holds huge amount of knowledge surely benefiting the question answering (QA) research. However, the KG, which is always constituted of entities and relations, is structurally inconsistent with the natural language query. Thus, the QA system based on KG is still faced with difficulties. The purpose of this paper is to propose a method to answer the domain-specific questions based on KG, providing conveniences for the information query over domain KG.Design/methodology/approachThe authors propose a method FactQA to answer the factual questions about specific domain. A series of logical rules are designed to transform the factual questions into the triples, in order to solve the structural inconsistency between the user’s question and the domain knowledge. Then, the query expansion strategies and filtering strategies are proposed from two levels (i.e. words and triples in the question). For matching the question with domain knowledge, not only the similarity values between the words in the question and the resources in the domain knowledge but also the tag information of these words is considered. And the tag information is obtained by parsing the question using Stanford CoreNLP. In this paper, the KG in metallic materials domain is used to illustrate the FactQA method.FindingsThe designed logical rules have time stability for transforming the factual questions into the triples. Additionally, after filtering the synonym expansion results of the words in the question, the expansion quality of the triple representation of the question is improved. The tag information of the words in the question is considered in the process of data matching, which could help to filter out the wrong matches.Originality/valueAlthough the FactQA is proposed for domain-specific QA, it can also be applied to any other domain besides metallic materials domain. For a question that cannot be answered, FactQA would generate a new related question to answer, providing as much as possible the user with the information they probably need. The FactQA could facilitate the user’s information query based on the emerging KG.

Keywords based Closed Domain Question Answering System for Indian Penal Code Sections and Indian Amendment Laws

In information retrieval, Question Answering (QA) is the task of answering a question posed in natural language (NL) using either a pre-structured database or a collection of natural language documents without human intervention. Question Answering systems are categorized on their available resource for answers. The domain specific Question Answering System gives more exact and correct answers than web based Question Answering system as it is limited for only one domain resource to answer. This paper proposes the closed domain Question Answering System for handling the legal documents of Indian Penal Code (IPC) sections and Indian Amendment Laws to retrieve more precise answers. This system tries to retrieve the exact answers from stored knowledge-base for the query related to Indian Penal Code (IPC) sections and Indian Amendment Laws asked by user. This Keyword based Question Answering System works on structured, unstructured and non-question form queries. The closed domain Question Answering system gives more accurate answer than other open domain system as it restricted single resource. Keywords from both queries and answer corpus play important role for extracting answer.

A Tri-Role Topic Model for Domain-Specific Question Answering

Stack Overflow and MedHelp are examples of domain-specific community-based question answering (CQA) systems. Different from CQA systems for general topics (e.g., Yahoo! Answers, Baidu Knows), questions and answers in domain-specific CQA systems are mostly in the same topical domain, enabling more comprehensive interaction between users on fine-grained topics. In such systems, users are more likely to ask questions on unfamiliar topics and to answer questions matching their expertise. Users can also vote answers based on their judgements. In this paper, we propose a Tri-Role Topic Model (TRTM) to model the tri-roles of users (i.e., as askers, answerers, and voters, respectively) and the activities of each role including composing question, selecting question to answer, contributing and voting answers. The proposed model can be used to enhance CQA systems from many perspectives. As a case study, we conducted experiments on ranking answers for questions on Stack Overflow, a CQA system for professional and enthusiast programmers. Experimental results show that TRTM is effective in facilitating users getting ideal rankings of answers, particularly for new and less popular questions. Evaluated on nDCG, TRTM outperforms state-of-the-art methods.

Object-Relational Database Based Category Data Model for Natural Language Interface to Database

Domain specific question answering technique allows users to use natural language to express their queries so that users need not have the knowledge about the structures of the information source. For such an application relational model is not suitable as it is not a natural way to represent real world knowledge. Using relational model for representing information source results in scattered relations of data about the real world objects. In this paper an effective category model is presented to organize information according to their contents based on object-relational database. For the discussion of the category model railway domain is used in the paper.