# Using Large Language Models for Goal-Oriented Dialogue Systems
## 2 Literature Review
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**LLM** & **Dataset/Database** & **Language** & **RAG** & **Dialogue Generation** & **Metric** & **Ref.** \\ \hline \multirow{4}{*}{LlaMA 2} & Knowledge database & & & User’s statement, dialogue history, user count, and language are fed to the LIM; GPT-3.5 is used to synthesize plural responses to provide linguistically nuanced responses & - & [34] \\  & consisting of 222 potential user intents & German, English & + & Regular expressions are applied to the text blocks to define data types; the user’s chat history is used to provide context to the LIM & Answer precision on a scale of 1–5 & [36] \\  & & & & & \\ \hline \multirow{4}{*}{XLM-RoBERTa-base} & \multirow{4}{*}{e-commerce question-int datasets} & Portuguese, & & Multi-turn intent classification chain-of-intent method to generate intent-aware dialogues & Contrastive loss & [37] \\  & & & & & \\ \hline \multirow{4}{*}{XLM-RoBERTa-base} & \multirow{4}{*}{e-commerce question-int datasets} & Portuguese, & & Multi-turn intent classification chain-of-intent method to generate intent-aware dialogues & Contrastive loss & [37] \\  & & & & & \\ \hline \multirow{4}{*}{XLM-RoBERTa-base} & \multirow{4}{*}{e-commerce question-int datasets} & Portuguese, & & Multi-turn intent classification chain-of-intent method to generate intent-aware dialogues & Contrastive loss & [37] \\  & & & & & \\ \hline \multirow{4}{*}{Chatgpt-3.5-turbo} & \multirow{4}{*}{MultiWOZ 2.0} & \multirow{4}{*}{English} & - & Adaptive prompt generation & Inform, success, BLEU & [38] \\  & & & & & \\ \hline \multirow{4}{*}{gpt+4-0613, gpt-3.5-turbo, gpt+4-turbo-2024-04-09} & \multirow{4}{*}{LLM-TOD dataset} & \multirow{4}{*}{English} & - & Proactive question asking, users’ guidance, dialogue state maintenance & Round count, completion rate, response quality, comparison score & [39] \\  & & & & & \\ \hline \multirow{4}{*}{GPT-3.5-turbo} & \multirow{4}{*}{MultiWoZ 2.1} & \multirow{4}{*}{English} & - & Proactively goal-driven LLM-induced approach, future dialogue actions and goal-oriented reward & Inform, success & [40] \\ \hline \multirow{4}{*}{test-davinci-001, text-davinci-002, text-davinci003, gpt-3.5-turbo} & \multirow{4}{*}{Context-open-question dataset} & \multirow{4}{*}{English} & \multirow{4}{*}{-} & Question generation to generate a variety of questions related to the context of the dialogue; extra knowledge retrieval; enhance the LLM response & BLEU, ROUGE, human evaluate & [41] \\ \hline \multirow{4}{*}{TS, Flan-TS} & \multirow{4}{*}{LUCID dataset} & \multirow{4}{*}{English} & \multirow{4}{*}{-} & Generation of intents, a conversational planner, turn-by-turn generation of conversations, and validation procedure & Inent accuracy, joint goal accuracy & [42] \\ \hline \multirow{4}{*}{ChatGPt; ChatGLM, BlenderBot, BART} & \multirow{4}{*}{MSC and CC datasets} & \multirow{4}{*}{English} & \multirow{4}{*}{-} & Historical event perception, dynamic person extraction, response generation based on retrieved relevant memories & BLEU-N, ROUGE-L, METEOR, accuracy, human evaluation & [43] \\ \hline \multirow{4}{*}{ChnGPT, GPT3.5} & \multirow{4}{*}{Multiwoz 2.0 and 2.2, RADDLE and STAR datasets} & \multirow{4}{*}{English} & \multirow{4}{*}{-} & LLM to generate with user, DST promoter to retrieve database items, policy prompt to elicit proper responses adhering to the provided dialogue policy & Inform, success, BLEU, combined, BERTScore & [44] \\ \hline \multirow{4}{*}{TS} & \multirow{4}{*}{NUCC, Livedoor news summarization, dolly-15k-ja} & \multirow{4}{*}{Japanese} & \multirow{4}{*}{-} & Dialogue summarization-based prompt design with context database & ROUGE-1, ROUGE-2, BART score, Sentence-BERT & [45] \\ \hline \multirow{4}{*}{ChnGPT, GPT3.5} & \multirow{4}{*}{Multiwoz 2.0 and 2.2, RADDLE and STAR datasets} & \multirow{4}{*}{English} & \multirow{4}{*}{-} & LLM to generate with user, DST promoter to retrieve database items, policy prompt to elicit proper responses adhering to the provided dialogue policy & Inform, success, BLEU, combined, BERTScore & [44] \\ \hline \multirow{4}{*}{TS} & \multirow{4}{*}{NUCC, Livedoor news summarization, dolly-15k-ja} & \multirow{4}{*}{Japanese} & \multirow{4}{*}{-} & Dialogue summarization-based prompt design with context database & ROUGE-1, ROUGE-2, BART score, Sentence-BERT & [45] \\ \hline \multirow{4}{*}{OpenAI GPT-4o-mini} & \multirow{4}{*}{Train ticket booking system, interactive troubleshooting Copilot data} & \multirow{4}{*}{English, Italian} & \multirow{4}{*}{+} & Conversation routine-based embedded business logic within LLM prompts & [46] \\  & & & & & \\ \hline \multirow{4}{*}{GPT-4o, Llama 3.3} & \multirow{4}{*}{-} & English & - & Single-prompt dialogue generation, two-agent dialogue generation & Agreement rate & [47] \\ \cline{1-1} \cline{3-4}  & & & & & \\ \hline \end{tabular}
\end{table}