Stefan Hillmann

Adnan Ahmad, Philine Kowol, Stefan Hillmann et al.

In this paper, we provide an extensive analysis of multi-label intent classification using Large Language Models (LLMs) that are open-source, publicly available, and can be run in consumer hardware. We use the MultiWOZ 2.1 dataset, a benchmark in the dialogue system domain, to investigate the efficacy of three popular open-source pre-trained LLMs, namely LLama2-7B-hf, Mistral-7B-v0.1, and Yi-6B. We perform the classification task in a few-shot setup, giving 20 examples in the prompt with some instructions. Our approach focuses on the differences in performance of these models across several performance metrics by methodically assessing these models on multi-label intent classification tasks. Additionally, we compare the performance of the instruction-based fine-tuning approach with supervised learning using the smaller transformer model BertForSequenceClassification as a baseline. To evaluate the performance of the models, we use evaluation metrics like accuracy, precision, and recall as well as micro, macro, and weighted F1 score. We also report the inference time, VRAM requirements, etc. The Mistral-7B-v0.1 outperforms two other generative models on 11 intent classes out of 14 in terms of F-Score, with a weighted average of 0.50. It also has relatively lower Humming Loss and higher Jaccard Similarity, making it the winning model in the few-shot setting. We find BERT based supervised classifier having superior performance compared to the best performing few-shot generative LLM. The study provides a framework for small open-source LLMs in detecting complex multi-intent dialogues, enhancing the Natural Language Understanding aspect of task-oriented chatbots.

Adnan Ahmad, Stefan Hillmann, Sebastian Möller

In this study, we explore the application of Large Language Models (LLMs) for generating synthetic users and simulating user conversations with a task-oriented dialogue system and present detailed results and their analysis. We propose a comprehensive novel approach to user simulation technique that uses LLMs to create diverse user profiles, set goals, engage in multi-turn dialogues, and evaluate the conversation success. We employ two proprietary LLMs, namely GPT-4o and GPT-o1 (Achiam et al., 2023), to generate a heterogeneous base of user profiles, characterized by varied demographics, multiple user goals, different conversational styles, initial knowledge levels, interests, and conversational objectives. We perform a detailed analysis of the user profiles generated by LLMs to assess the diversity, consistency, and potential biases inherent in these LLM-generated user simulations. We find that GPT-o1 generates more heterogeneous user distribution across most user attributes, while GPT-4o generates more skewed user attributes. The generated set of user profiles are then utilized to simulate dialogue sessions by interacting with a task-oriented dialogue system.

Ingo Siegert, Jan Nehring, Aranxa Márquez Ampudia et al.

In recent times, discussions on social media platforms have increasingly come under scrutiny due to the proliferation of science denial and fake news. Traditional solutions, such as regulatory actions, have been implemented to mitigate the spread of misinformation; however, these measures alone are not sufficient. To complement these efforts, educational approaches are becoming essential in empowering users to critically engage with misinformation. Conversation training, through serious games or personalized methods, has emerged as a promising strategy to help users handle science denial and toxic conversation tactics. This paper suggests an interdisciplinary seminar to explore the suitability of Large Language Models (LLMs) acting as a persona of a science denier to support people in identifying misinformation and improving resilience against toxic interactions. In the seminar, groups of four to five students will develop an AI-based chatbot that enables realistic interactions with science-denial argumentation structures. The task involves planning the setting, integrating a Large Language Model to facilitate natural dialogues, implementing the chatbot using the RASA framework, and evaluating the outcomes in a user study. It is crucial that users understand what they need to do during the interaction, how to conclude it, and how the relevant information is conveyed. The seminar does not aim to develop chatbots for practicing debunking but serves to teach AI technologies and test the feasibility of this idea for future applications. The chatbot seminar is conducted as a hybrid, parallel master's module at the participating educational institutions.