Retrieval Augmented Generation (RAG) has been pivotal in the utilization of Large Language Models (LLM) to improve the factuality of long-form question answering systems in industrial settings. Knowledge graphs (KG) represent a linking of disparate information sources that potentially yield useful information for mitigating the issues of insufficient knowledge and hallucination within the LLM-RAG pipeline. However, the creation of domain-specific KG is costly and usually requires a domain expert. To alleviate the above challenges, this work proposes QuARK, a novel domain-specific question answering framework to enhance the knowledge capabilities of LLM by integrating structured KG, thereby significantly reducing the reliance on the “generic” latent knowledge of LLMs. Here, we showcase how LLMs can be deployed to not only act in dynamic information retrieval and in answer generating frameworks, but also as flexible agents to automatically extract relevant entities and relations for the automated construction of domain-specific KGs. Crucially we propose how the pairing of question decomposition and semantic triplet retrieval within RAG can enable optimal subgraph retrieval. Experimental evaluations of our framework on financial domain public dataset, demonstrate that it enables a robust pipeline incorporating schema-free KG within a RAG framework to improve the overall accuracy by nearly 13%.

Automated Frequently Asked Question (FAQ) retrieval provides an effective procedure to provide prompt responses to natural language based queries, providing an efficient platform for large-scale service-providing companies for presenting readily available information pertaining to customers’ questions. We propose DTAFA, a novel multi-lingual FAQ retrieval system that aims at improving the top-1 retrieval accuracy with the least number of parameters. We propose two decoupled deep learning architectures trained for (i) candidate generation via text classification for a user question, and (ii) learning fine-grained semantic similarity between user questions and the FAQ repository for candidate refinement. We validate our system using real-life enterprise data as well as open source dataset. Empirically we show that DTAFA achieves better accuracy compared to existing state-of-the-art while requiring nearly 30× lesser number of training parameters.

Most of the current anti money laundering (AML) systems, using handcrafted rules, are heavily reliant on existing structured databases, which are not capable of effectively and efficiently identifying hidden and complex ML activities, especially those with dynamic and time-varying characteristics, resulting in a high percentage of false positives. Therefore, analysts are engaged for further investigation which significantly increases human capital cost and processing time. To alleviate these issues, this paper presents a novel framework for the next generation AML by applying and visualizing deep learning-driven natural language processing (NLP) technologies in a distributed and scalable manner to augment AML monitoring and investigation. The proposed distributed framework performs news and tweet sentiment analysis, entity recognition, relation extraction, entity linking and link analysis on different data sources (e.g. news articles and tweets) to provide additional evidence to human investigators for final decision-making. Each NLP module is evaluated on a task-specific data set, and the overall experiments are performed on synthetic and real-world datasets. Feedback from AML practitioners suggests that our system can reduce approximately 30% time and cost compared to their previous manual approaches of AML investigation.