@InProceedings{Venkateswaran2024_1216,
<br/>   author = {Siddarth Venkateswaran and Ronald Böck},
<br/>   booktitle = {Studientexte zur Sprachkommunikation: Elektronische Sprachsignalverarbeitung 2024},
<br/>   title = {Can Language Models Behave Like Wine Sommeliers? Using Multiple Agents To Evaluate The Quality of Wine Descriptors Generated By Llama 2},
<br/>   year = {2024},
<br/>   editor = {Timo Baumann},
<br/>   month = mar,
<br/>   pages = {141--148},
<br/>   publisher = {TUDpress, Dresden},
<br/>   abstract  = {Wines are complex beverages whose taste can be described either numerically
or textually, with the former involving the rating of the intensities of
different aroma characteristics often with the help of a wine tasting wheel, and
the latter with the help of crisp terms often in a poetic fashion. These are often
done with the help of wine sommeliers who with one sniff can describe the wine.
Usually, each sommelier has a unique style when it comes to textually describing
a wine, research has shown that such differences have no negative impact in correctly
classifying wines on the basis of their color, grape variety, region etc. Given
the recent advancements in the field of Natural Language Processing, especially
with the emergence of Large Language Models, we aim to check the capability
of Llama 2 in its ability to generate texts pertaining to a specific color of a wine,
given a list of aroma intensities as input prompts. In our experiments, we relied
on data from Meininger and Falstaff, and on a combination of domain adaptation
and pseudo-labeling techniques to create the corpus to train the Llama 2 model on.
Also, we relied on a voting scheme of three differently trained classifiers to evaluate
the wine-color specific text generation capabilities of Llama 2. Additionally,
we employed the services of domain experts to evaluate the quality of a sample set
of texts that was generated by Llama 2.},
<br/>   isbn = {978-3-95908-325-6},
<br/>   issn = {0940-6832},
<br/>   keywords = {Large Language Models},
<br/>   url = {https://www.essv.de/pdf/2024_141_148.pdf},
<br/>   doi = {10.35096/othr/pub-7091},
<br/>}