Large Language Models show impressive results in many diferent applications, most notably in the context of questionanswering and open dialog situations. However, it is still an open question how to use those models for task-oriented dialogs such as booking or customer information systems, and such. In this work, we propose Dynamic Prompting, an architecture for task-oriented dialog, integrating the benefits of Large Language Models and showcasing the approach on the MultiWOZ 2.2 dataset. Our architecture leads to a high task success rate, provides sensible and specific answers, and is resistant to hallucinations. Further, we show that Dynamic Prompting is able to answer questions that were not anticipated by the dialog systems designer and that it can correct several types of errors and other characteristics of the system.
and DST also but, unlike our work, they used a static
prompt. Other approaches to LLMs for task-oriented
diTask-Oriented Dialog Systems (TODS) assist users in alog are presented by Cao [11], Hu et al. [12], Wei et al.
completing a task within a conversation [
In contrast, Large Language Models (LLMs) are very 2.1. Dataset good at open-domain dialog and provide fluent and con- For our experiments, we use the Multi-domain Wizard-ofvincing messages in diferent styles. However, those Oz 2.2 (MultiWOZ) dataset [15, 16]. The dataset contains answers might be misleading and even false (hallucina- conversations between users and task-oriented chatbots tion) [6, 7, 8]. In task-oriented dialog, the model could that were collected using the Wizard-of-Oz [17] method. possibly ‘break out’ of the given dialog task. For our setup, we only use conversations about the restau
Using LLMs for task-oriented dialog is still in its in- rant domain. In the conversations, users ask chatbots fancy. Madotto et al. [9] used LLMs for the whole pipeline about restaurants in Cambridge, England. The systems of Natural Language Understanding, Dialog State Track- can recommend various restaurants based on attributes ing, Dialog Policy and Natural Language Generation. such as the type of food, location or price range. Also, Hudeček and Dusek [10] expand on this idea by eval- the system can perform bookings. uating the abilities of LLMs to generate complete task- Table 1 shows an example dialog from the dataset. oriented multi-turn dialogs. They used LLMs for NLU The restaurant domain encompasses two intents (find, book) and ten slots. For each user utterance, the dataset contains annotations for intent, slots in the utterance and dialog state (detected slots over the whole dialog). Bot:
Bot:
Bot:
panasian food? i haven’t found any expensive panasain restaurants. should i try another price range?
2.2. Dialog System Architecture
ditionally, of the following main components [
We replace/extend the Dialog Manager and Response Generation with a Prompt Generation and an LLM. The prompt generation uses input from the DST and NLU and uses a series of rules, similar to a dialog manager. Also, it fetches data from a database and generates a prompt. It uses prompting templates that consist of three parts: 1) A general task description, 2) content from the database, NLU states in JSON format, and 3) the previous conversation in a theater script style. Table 2 shows an example prompt of our system in the restaurant domain with the detected intent ‘book restaurant’. The prompt is then sent to a LLM to generate a corresponding user reply. In our experiment, we use GPT-3.5-Turbo (ChatGPT) by accessing the model via API calls, as it has demonstrated leading performance in the results presented by Hudeček and Dusek [10].
Appendix A shows the prompting templates and the rules that we developed for our prompt generator. Our prompt construction approach involved multiple iterations of ‘trial and error’ process on the on training set, evaluating their efectiveness based on the system’s task completion and relevance to the conversation. Initially, we introduced single instructions in the prompts. However, scenarios such as having no available restaurants, multiple options, or booking a restaurant required more specific instructions. This led us to implement dynamic prompts with tailored rules for each situation.
the restaurant is available to confirm a successful booking. Provide a reference number when the restaurant name, bookday, bookpeople, and booktime are given.
2.3. Evaluation In our experiment, the chatbot generates a response using our dynamic prompting system for each dialog turn. We evaluated its performance on the test split of the dataset’s restaurant domain. To evaluate the responses on diferent levels, we label them by two human annotators, given the following criteria. The annotation guidelines can be found in the supplementary materials.
• Task Success Rate describes the ratio of successful dialogs to the total number of dialogs. Following the definition of Wen et al. [19] and Nekvinda and Dušek [20], we mark a dialog as successful if 1) the system’s recommendation aligns with the user’s requests (such as price range, location, and cuisine) and 2) the system adequately addressed subsequent requests by the user, such as providing the telephone number or confirming a successful booking. • Prompt instruction performance, a binary scale to assess whether responses aligned with the prompt instructions. • Information Extraction Performance, a binary scale, if the system is able to fetch the relevant address from the JSON information. • Response slot accuracy, the ratio of correctly predicted slot values and the number of slot values in the response. It measures if our system is able to return all desired slots to the user. We compute ratios across all annotated turns from these metrics. • Sensibleness describes if the utterance makes sense given the context [21, 22]. • Specificity describes if the utterance is specific regarding the context [21, 22]. LMs are used to generate unspecific answers such as “this is great”, which are sensible but not desired. • Interestingness describes if the utterance captures someone’s attention, arouses curiosity or exhibits traits such as unexpectedness, wit, or insightfulness [22]. Interestingness contributes to a compelling and engaging user experience.
Table 3 shows the task success rate of our system compared to other TODS on the MultiWOZ 2.2 dataset. Although the other systems use the whole dataset and, thus, are not perfectly comparable to ours, it still shows that Dynamic Prompting has a similar performance compared to SOTA systems. This is remarkable, particularly as we use a relatively simple NLU component, which by itself might produce errors. However, if we do not use the NLU system of our pipeline but instead use the entity annotations from the dataset, we get a ‘perfect’ NLU without any errors. In this case, our Dynamic Prompting achieves a task Success Rate of 0.94 - which highlights the eficiency of the LLM solution.
Table 5 shows further performance metrics. The dialog success rate is supported by the high sensibility and speciifcity scores, which indicate that the system answers on point and does not deviate from the dialog’s goal. However, the response slot accuracy is only 80% and needs to be improved - but this is not the focus of this work. Extracting information from the database works almost 3.1. Qualitative Analysis In the following, we analyze the conversations and, particularly, the generated responses of our Dynamic Prompting in more detail.
In one situation the user asked to send the information via email, which the designers of the original dataset did not anticipate. In those situations, traditional dialog systems then can only answer with “I did not understand”. Our approach instead was able to produce a sensible response, 3.1.6. Error Analysis although it has never been trained for this case (see Table 4).
Yang et al. [23] Lee [3] Su et al. [24]
in the database, although the correct format would be “CB1 7AA” in the Cambridge area. Our approach consistently fixes these errors out of the box.
Table 3 Dynamic Prompting produces responses that are, on avComparison of Task Success Rates on MultiWOZ 2.2 data, with erage, 2.41 times longer and more diverse than the rean inter-annotator agreement of 1 for Dynamic Prompting. sponses of the crowd workers in the WOZ dataset, with lexical diversity measured by an MTLD score [25] of 80.41 compared to 72.26 for the WOZ dataset. We assume that perfectly (Information Extraction Performance=0.98). Al- the crowd workers were interested in providing fast and though the system does not always follow all instructions minimalistic answers. However, while diverse replies from the prompt (Prompt Instruction Performance=0.82), might be considered as positive as they make the interthe task success is still quite high, so we assume that only action more enjoyable, it needs to be determined if long minor errors cause the relatively low Prompt Instruction answers are preferable over short answers containing all Performance. necessary information. Table 7 in the appendix shows examples of this behavior.
Similar to our findings in Section 3.1.4, the responses of our system are not only longer but also more engaging compared to the ground truth. For example, in one situation, our system produced an answer such as “You’re welcome! If you have any more questions or need further assistance, feel free to ask. Have a great day too!” while the crowd worker wrote only “Thank you. Goodbye”. Overall, we counted ‘polite’ phrases in the responses and found out that dynamic prompting uses them more often than the ground truth, such as “enjoy your meal” (15.5 more often), “have a great day” (2.2), “you’re welcome” (4.8), “certainly!” (61.0), “great!” (20.0). Table 9 in the appendix shows more detailed examples.
The database entries are formulated in a diferent format. Names are often lowercase, and the crowd workers did not correct this issue when they wrote the system responses. Also, postcodes are stored in the format “cb17aa”
We detected a user dialog in which the NLU failed to detect the correct area. The user asked for a restaurant in the center of the city, but due to the NLU error, the prompt generator wrote two restaurants in the prompt, one in the center and one in the south. The LLM was able to detect that the user wanted a restaurant in the center and presented only the correct restaurant in its response (see Table 8 in the Appendix).
Our system produced two types of problems. First, it was not able to display the total count of available restaurants correctly. Although the total count of available restaurants is encoded in the JSON prompt, the LLM opted to count the number of elements within the JSON options array instead.
The second common problem was the generation of fake booking reference numbers. At the end of the dialog, users often ask for a booking reference number. We do not include booking reference numbers in the prompt. Therefore, the system hallucinated booking reference numbers. We evaluated including reference numbers in the JSON format in the database section of the prompt on ten samples, and we eliminated all occurrences of fake reference number generation in our small sample set. Still, we did not include this in the whole dataset, so our results serve as an indicator only that this approach could solve the problem of hypothetical booking reference number generation.
Assist the user in finding a restaurant. A single restaurant matches the user’s criteria. Provide requested details of the restaurant. If no specific request is made, ofer general information like food type, area, and price range. Respond in a coherent, non-numbered, and fluent text format based only on the conversation history, state, and database information.
State: restaurant {“area": “centre", “food": “italian", “name": “stazione restaurant and cofee bar", “bookday": “saturday", “bookpeople": “1", “booktime": “12:45"} Database: restaurant {Count:1, Options: {“address": “Market Hill City Centre", “area": “centre", “food": “italian", “id": “19240", “name": “stazione restaurant and cofee bar", “phone": “01223352607", “postcode": “cb23nj", “pricerange": “expensive", “type": “restaurant"}}
I apologize, but as a language model AI, I don’t have the capability to send emails. However, you can easily save or take a screenshot of this conversation for future reference. Remember to have a great time at stazione restaurant and cofee bar on Saturday at 12:45!
We presented Dynamic Prompting, a technique integrating LLMs for task-oriented dialog. The results show high sensibility and specificity values, which indicate that the system answers on point and does not deviate from the dialog’s goal. The relatively low Prompt Extraction Performance and Response Slot Accuracy values still result in excellent task success. The high values in the performance metrics Prompt Instruction Performance and Information Extraction Performance indicate that the LLM follows the task-oriented guidance of the dynamic prompts. The Information Extraction Performance of 0.98 shows that the system could very well reuse the database information embedded in the prompt in the JSON format.
In addition, our system shows various ways to correct errors, such as NLU errors, user requests not anticipated by the designer of DS, and errors in the format of the database entries. Moreover, the generated system answers are more diverse (Section 3.1.4) and more polite (Section 3.1.2) than the human-generated responses in the dataset. We would like to examine these qualitative results in future research in a more quantitative way.
Overall, we find that the widespread problem of hallucinations in LLMs is not an issue in our system as long as we present the correct information to the LLM. As soon as the user asks the system for information that is not present in the prompt, such as the booking reference numbers, the LLM starts to hallucinate.
Although we assess the system’s performance solely on the restaurant domain, the dynamic prompting method can be extended to other domains in the MultiWOZ 2.2 dataset, such as hotel, taxi, and train. Expanding to new domains will require updating the prompt generation module to accommodate new intents and state values, ensuring smooth integration with these additional domains.
This work has been supported by the Federal Joint Committee of Germany (Gemeinsamer Bundesausschuss) as part of the project smartNTX (01NVF21116). policy injection, Proceedings of the AAAI Confer- systems, 2020. arXiv:2008.06239. ence on Artificial Intelligence (2022). [10] V. Hudeček, O. Dusek, Are large language models [3] Y. Lee, Improving end-to-end task-oriented dia- all you need for task-oriented dialogue?, in: S. Stoylog system with a simple auxiliary task, in: M.- anchev, S. Joty, D. Schlangen, O. Dusek, C. KenningF. Moens, X. Huang, L. Specia, S. W.-t. Yih (Eds.), ton, M. Alikhani (Eds.), Proceedings of the 24th Findings of the Association for Computational Lin- Annual Meeting of the Special Interest Group on guistics: EMNLP 2021, Association for Computa- Discourse and Dialogue, Association for Computational Linguistics, Punta Cana, Dominican Repub- tional Linguistics, Prague, Czechia, 2023, pp. 216– lic, 2021, pp. 1296–1303. URL: https://aclanthology. 228. URL: https://aclanthology.org/2023.sigdial-1. org/2021.findings-emnlp.112. doi: 10.18653/v1/ 21. doi:10.18653/v1/2023.sigdial-1.21. 2021.findings-emnlp.112. [11] L. Cao, Diaggpt: An llm-based and multi-agent [4] H. Sun, J. Bao, Y. Wu, X. He, Mars: Modeling context dialogue system with automatic topic manage& state representations with contrastive learning ment for flexible task-oriented dialogue, 2024. for end-to-end task-oriented dialog, in: A. Rogers, arXiv:2308.08043.
J. Boyd-Graber, N. Okazaki (Eds.), Findings of the [12] Z. Hu, Y. Feng, Y. Deng, Z. Li, S.-K. Ng, Association for Computational Linguistics: ACL A. T. Luu, B. Hooi, Enhancing large language 2023, Association for Computational Linguistics, model induced task-oriented dialogue systems Toronto, Canada, 2023, pp. 11139–11160. URL: https: through look-forward motivated goals, 2023. //aclanthology.org/2023.findings-acl.708. doi: 10. arXiv:2309.08949.
18653/v1/2023.findings-acl.708. [13] J. Wei, S. Kim, H. Jung, Y.-H. Kim, Leveraging large [5] Q. Wu, D. Alnuhait, D. Chen, Z. Yu, Using tex- language models to power chatbots for collecting tual interface to align external knowledge for user self-reported data, 2023. arXiv:2301.05843. end-to-end task-oriented dialogue systems, 2023. [14] Z. Li, B. Peng, P. He, M. Galley, J. Gao, X. Yan, arXiv:2305.13710. Guiding large language models via directional [6] W. Sun, Z. Shi, S. Gao, P. Ren, M. de Rijke, Z. Ren, stimulus prompting, in: A. Oh, T. Neumann, Contrastive learning reduces hallucination in con- A. Globerson, K. Saenko, M. Hardt, S. Levine versations, in: Proceedings of the Thirty-Seventh (Eds.), Advances in Neural Information Processing AAAI Conference on Artificial Intelligence and Systems, volume 36, Curran Associates, Inc., 2023, Thirty-Fifth Conference on Innovative Applications pp. 62630–62656. URL: https://proceedings. of Artificial Intelligence and Thirteenth Symposium neurips.cc/paper_files/paper/2023/file/ on Educational Advances in Artificial Intelligence, c5601d99ed028448f29d1dae2e4a926d-Paper-Conference. AAAI’23/IAAI’23/EAAI’23, AAAI Press, 2023. URL: pdf. https://doi.org/10.1609/aaai.v37i11.26596. doi:10. [15] P. Budzianowski, T.-H. Wen, B.-H. Tseng, 1609/aaai.v37i11.26596. I. Casanueva, S. Ultes, O. Ramadan, M. Gašić, [7] Y. Bang, S. Cahyawijaya, N. Lee, W. Dai, D. Su, MultiWOZ - a large-scale multi-domain Wizard-ofB. Wilie, H. Lovenia, Z. Ji, T. Yu, W. Chung, Q. V. Oz dataset for task-oriented dialogue modelling, Do, Y. Xu, P. Fung, A multitask, multilingual, mul- in: E. Rilof, D. Chiang, J. Hockenmaier, J. Tsujii timodal evaluation of ChatGPT on reasoning, hal- (Eds.), Proceedings of the 2018 Conference lucination, and interactivity, in: J. C. Park, Y. Arase, on Empirical Methods in Natural Language B. Hu, W. Lu, D. Wijaya, A. Purwarianti, A. A. Kris- Processing, Association for Computational nadhi (Eds.), Proceedings of the 13th International Linguistics, Brussels, Belgium, 2018, pp. 5016– Joint Conference on Natural Language Processing 5026. URL: https://aclanthology.org/D18-1547. and the 3rd Conference of the Asia-Pacific Chapter doi:10.18653/v1/D18-1547. of the Association for Computational Linguistics [16] X. Zang, A. Rastogi, S. Sunkara, R. Gupta, J. Zhang, (Volume 1: Long Papers), Association for Computa- J. Chen, MultiWOZ 2.2 : A Dialogue Dataset with tional Linguistics, Nusa Dua, Bali, 2023, pp. 675–718. Additional Annotation Corrections and State TrackURL: https://aclanthology.org/2023.ijcnlp-main.45. ing Baselines, in: Proceedings of the 2nd Workdoi:10.18653/v1/2023.ijcnlp-main.45. shop on Natural Language Processing for Conver[8] Z. Ji, N. Lee, R. Frieske, T. Yu, D. Su, Y. Xu, E. Ishii, sational AI, Association for Computational LinY. J. Bang, A. Madotto, P. Fung, Survey of hal- guistics, Online, 2020, pp. 109–117. URL: https: lucination in natural language generation, ACM //aclanthology.org/2020.nlp4convai-1.13. doi:10. Comput. Surv. 55 (2023). URL: https://doi.org/10. 18653/v1/2020.nlp4convai-1.13. 1145/3571730. doi:10.1145/3571730. [17] J. F. Kelley, An iterative design methodology for [9] A. Madotto, Z. Liu, Z. Lin, P. Fung, Language mod- user-friendly natural language ofice information els as few-shot learner for task-oriented dialogue applications, ACM Trans. Inf. Syst. 2 (1984) 26–41. URL: https://doi.org/10.1145/357417.357420. doi:10. diversity assessment, Behavior research methods 1145/357417.357420. 42 (2010) 381–92. doi:10.3758/BRM.42.2.381. [18] T. Bunk, D. Varshneya, V. Vlasov, A. Nichol, DIET:
Lightweight language understanding for dialogue systems, 2020. arXiv:2004.09936. Appendix [19] T.-H. Wen, D. Vandyke, N. Mrkšić, M. Gašić, L. M.
Rojas-Barahona, P.-H. Su, S. Ultes, S. Young, A network-based end-to-end trainable task-oriented A. Prompting templates dialogue system, in: M. Lapata, P. Blunsom, A. Koller (Eds.), Proceedings of the 15th Confer- Table 6 shows some prompting templates that have been ence of the European Chapter of the Association developed for our system. for Computational Linguistics: Volume 1, Long Papers, Association for Computational Linguistics, B. Example Conversations Valencia, Spain, 2017, pp. 438–449. URL: https: //aclanthology.org/E17-1042. Tables 7 - 9 show diferent examples conversations. [20] T. Nekvinda, O. Dušek, Shades of BLEU, flavours of success: The case of MultiWOZ, in: A. Bosselut, E. Durmus, V. P. Gangal, S. Gehrmann, Y. Jernite, L. Perez-Beltrachini, S. Shaikh, W. Xu (Eds.), Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021), Association for Computational Linguistics, Online, 2021, pp. 34–46. URL: https://aclanthology.org/2021.
gem-1.4. doi:10.18653/v1/2021.gem-1.4. [21] D. Adiwardana, M.-T. Luong, D. R. So, J. Hall,
N. Fiedel, R. Thoppilan, Z. Yang, A. Kulshreshtha, G. Nemade, Y. Lu, Q. V. Le, Towards a human-like open-domain chatbot, 2020. arXiv:2001.09977. [22] R. Thoppilan, D. De Freitas, J. Hall, N. Shazeer,
A. Kulshreshtha, H.-T. Cheng, A. Jin, T. Bos, L. Baker, Y. Du, et al., LaMDA: Language Models for Dialog Applications, arXiv preprint arXiv:2201.08239 (2022). [23] Y. Yang, Y. Li, X. Quan, Ubar: Towards fully endto-end task-oriented dialog system with gpt-2, in: Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, February 2-9, 2021, AAAI Press, 2021, pp. 14230–14238. URL: https://ojs.aaai.org/ index.php/AAAI/article/view/17674. [24] Y. Su, L. Shu, E. Mansimov, A. Gupta, D. Cai, Y.-A.
Lai, Y. Zhang, Multi-task pre-training for plug-andplay task-oriented dialogue system, in: S. Muresan, P. Nakov, A. Villavicencio (Eds.), Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Association for Computational Linguistics, Dublin, Ireland, 2022, pp. 4661–4676. URL: https://aclanthology.org/2022.acl-long.319. doi:10.
18653/v1/2022.acl-long.319. [25] P. Mccarthy, S. Jarvis, Mtld, vocd-d, and hd-d: A validation study of sophisticated approaches to lexical intent == find_restaurant: len(database) ≥ 5:
Assist the user in finding a restaurant. Numerous restaurants match the user’s criteria. Ask for missing details of food type, area, or price range to narrow down the search before providing the restaurant options. Respond in a coherent, non-numbered, and fluent text format based only on the conversation history, state, and database information.
{State + Database + Conversation History} len(database) ≥ 1:
Assist the user in finding a restaurant. Multiple restaurants match the user’s criteria. Provide requested details from the available restaurant options. If no specific request is made, ofer general information like food type, area, and price range. Respond in a coherent, non-numbered, and fluent text format based only on the conversation history, state, and database information.
{State + Database + Conversation History} len(database) == 1:
Assist the user in finding a restaurant. A single restaurant matches the user’s criteria. Provide requested details of the restaurant. If no specific request is made, ofer general information like food type, area, and price range. Respond in a coherent, non-numbered, and fluent text format based only on the conversation history, state, and database information.
{State + Database + Conversation History} len(database) == 0:
Assist the user in finding a restaurant. No restaurant matches the user’s criteria. Suggest trying diferent food types, areas, or prices. Respond in a coherent, non-numbered, and fluent text format based only on the conversation history, state, and database information.
{State + Database + Conversation History} intent == book_restaurant:
Assist the user in booking a restaurant. Always assume the restaurant is available to confirm a successful booking. Provide a reference number when the restaurant name, bookday, bookpeople, and booktime are given. Prompt if these details are missing. Omit information about fictional bookings. {State + Conversation history} intent == none:
You’re assisting the user in finding and booking a restaurant. Provide helpful information and guidance based on their requests.
{Conversation history}
Assist the user in finding a restaurant. No restaurant matches the user’s criteria. Suggest trying diferent food types, areas, or prices. Respond in a coherent, non-numbered, and fluent text format based only on the conversation history, state, and database information.
I’m sorry, there aren’t any Mexican restaurants in the centre of town. Would you like to try a diferent cuisine? Assist the user in finding a restaurant. No restaurant matches the user’s criteria. Suggest trying diferent food types, areas, or prices. Respond in a coherent, non-numbered, and fluent text format based only on the conversation history, state, and database information.
I apologize, but it seems that there are no restaurants in our database that serve Venetian food in the centre at the moment. However, I can suggest a few alternatives for you. You might consider trying some other types of cuisine such as Italian, seafood, or Mediterranean, which are commonly found in the centre. Alternatively, you could try searching for Venetian restaurants in nearby areas or expanding your search to include restaurants that are not strictly in the centre.
Unfortunately, I don’t see any restaurants that serve Venetian in the centre of town. Would you like to try a diferent area or type of cuisine?
Assist the user in finding a restaurant. Numerous restaurants match the user’s criteria. Ask for missing details of food type, area, or price range to narrow down the search before providing the restaurant options. Respond in a coherent, non-numbered, and fluent text format based only on the conversation history, state, and database information.
Database: restaurant {Count:5, Options: {“address": “152 - 154 Hills Road", “area": “south", “food": “modern european", “id": “14731", “name": “restaurant alimentum", “phone": “01223413000", “postcode": “cb28pb", “pricerange": “moderate", “signature": “slowroast sirloin of beef red onion celeriac and garlic", “type": “restaurant"}, {“address": “83 Regent Street", “area": “centre", “food": “modern european", “id": “3697", “name": “de luca cucina and bar", “phone": “01223356666", “postcode": “cb21aw", “pricerange": “moderate", “signature": “roasted barbary duck breast served with sweet potato wedges and mange tout with a red wine sauce", “type": “restaurant"}
Yes, there is a modern European restaurant called “De Luca Cucina and Bar" located in the centre at 83 Regent Street. They ofer a variety of dishes including their signature dish of roasted Barbary duck breast served with sweet potato wedges and mange tout with a red wine sauce. The price range is moderate. Would you like more information about this restaurant?
Yes, there are 2: De Luca Cucina and Bar & Riverside Brasserie. Would you like further information?
Yes, there are restaurants that serve Mediterranean food. The options available are “La Mimosa" and “Shiraz Restaurant." Both of them are located in the center area of town. “La Mimosa" is situated on Thompsons Lane in Fen Ditton, and you can reach them at 01223362525. On the other hand, “Shiraz Restaurant" is located at 84 Regent Street in the City Centre, with a contact number of 01223307581. Both restaurants ofer Mediterranean cuisine and fall under the expensive price range. Let me know if you need any additional information!