Insights from LWDA Conference Presentation – Combining Information Retrieval and Large Language Models for a Chatbot that Generates Reliable, Natural-style Answers

Author: Andreas Lommatzsch

Chatbots stand as one of the premier communication channels in the digital age, essential in several different sectors for their instantaneous, round-the-clock interaction. Conventional (rule-based) chatbots (like Alice) have exhibited significant limitations, particularly in adapting to context, addressing specific inquiries, and supporting follow-up questions, often leading to a fragmented and unsatisfactory user experience. The use of Large Language Models (LLMs) can help to overcome the problem since LLMs can highly adapt to the context and the concrete user questions – but LLMs generated answers might be unreliable and build based on unverified knowledge.

Our research focuses on combining the potential of LLMs with reliable semantic knowledge sources. Our paper introduces a novel methodology, combining LLMs with dedicated (nosql-) databases bases to ground answer generation in verifiable information, enhancing both reliability and context-relevance of the responses.
We have implemented a prototype aimed at answering questions pertaining to local administration services. This real-world application underscores our commitment to not only advancing theoretical understanding but also solving tangible challenges within the scenario.
Initial evaluations are promising, indicating effective function and a significant leap over traditional chatbot capabilities. Nevertheless, there is substantial room for refinement. The necessity for fine-tuning became evident to ensure comprehensive and reliable responses to all user queries.

We presented our research work on the this years LWDA conference in Marburg (Germany). The presentation in the knowledge management track and in the poster session was very fruitful. We are grateful for the engaging dialogues and the intellectual exchange at LWDA conference; we anticipate further refining our approach through collaborative insights and continued research.

Visual Impressions from the LWDA conference give the following photos.