# Using Large Language Models for Goal-Oriented Dialogue Systems
## 4 Results
### Visualization of Dialogue Graphs with Intent Sequences  
We fed the locally deployed LlaMA model the MultiWOZ 2.2 dataset and asked it to recognize the user/system intent for each conversational utterance. We obtained a labeled  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Dialogue Utterances** & **Intent Sequence (Isolated Utterances)** & **Intent Sequence (Current Dialogue Context as an Input)** & **Intent Sequence (Whole Dialogue Context as an Input)** \\ \hline \hline  & Prompt: & Prompt: & Prompt: \\  & Prompt: & What is the user intent in the text? & What is the user intent in the text? & What is the user intent in the text? \\  & What is the user intent in the text? & Describe it with one or two words. & Describe it with one or two words. & Use the following pieces of context to answer the question. & Use the following pieces of context to answer the question. \\  & text: “[input]” & text: “[context]” & text: “[context]” & text: “[context]” \\ \hline - Guten tag, I am staying & & & \\   overnight in Cambridge and need & & & \\   a place to sleep. I need free & & Accommodation inquiry & Accommodation request & Accommodation inquiry \\  & parking and internet. & & & \\ - I have 4 different options for you. & & & \\   I have two cheaper gustabues & Preference gathering & Preference inquiry & Presenting options \\   and two expensive htoels. Do you & & & \\   have a preference? & & & \\  - No, but I’d really like to be on & & & \\   the south end of the city. Do any & & Location preference & Location query & Location preference \\  & of those fit the bill? & & & \\ - Sure. Dose price matter? We can & & & \\   narrow it down and find exactly & & Clarification & Preference clarification & Inquiry and assistance \\  & what you need. & & & \\ - No I don’t care about the price. & & & & \\   Which one do you recommend? & & & & \\ - I would recommend Aylesbray & & & & \\   Lodge Guest House. Would you & Recommendation, booking & Booking assistance & Recommendation and booking \\   like me to book that for you? & & & \\ - Yes, book it for 4 people and 4 nights starting from Tuesday. & & & & \\ - The booking was unsuccessful. & & & & \\   Would you like another day or a shorter stay? & & & & \\ - How about for 3 nights? & & & & \\ - Booked! Reference number is: & & & & \\  84 ESPe5 & & & & \\ - Great. I am all set then. Have a & & & & \\   nice day. Bye. & & & & \\ - Have a nice stay. Bye. & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Construction of intent sequence with various prompts for the ChatGPT 3.5 LLM.  
dataset of 113,747 records. The intent column contained over 10,000 unique intents, many of which were close in meaning (e.g., \"Acceptance\", \"Acceptance agreement\", \"Acceptance and gratitude\", \"Acceptance approval\", etc.). Using clustering, we combined similar intents into groups. Using the networkx 3.4.2 library in Python, for each conversation, we could visualize the dialogue graph based on the intent sequence labeled by LlaMA. Examples of several dialogue graphs are shown in Figure 6. The average length of a dialogue graph was 6 utterances, the longest dialogue consisted of 15 utterances, and the shortest dialogue consisted of 2 utterances.