# Leveraging Large Language Models for Sensor Data Retrieval
## 2 Related Work
### Word Embeddings for Table Retrieval  
Word embeddings are dense vectors that represent a word's meaning by positioning it as a point within a semantic space. They capture the distributional meaning of words, wherein similar representations are acquired for words found in analogous contexts [18]. Notable examples of such word representations include Word2 vec [19], fastText [20], and Glove [21]. In recent years, word embeddings have gained extensive popularity for various semantic tasks within the field of NLP, including text classification [22], sentiment analysis [23], machine translation [24], and dialogue systems [25]. Typically, word embedding vectors have between 50 and 1000 dimensions, which is significantly less than standard sparse semantic vectors. The reduction in dimensions relies on generalisations that encapsulate the semantic relationships of words according to their context of occurrence. Word embedding methodologies create a comprehensive lexicon from the unique terms within texts, giving every word a singular representation while overlooking their potential to convey various meanings in different scenarios. These are tagged as _static_ models, which fail to encapsulate a word's multiple interpretations. Conversely, contemporary contextual word embeddings have the capacity to capture the multiple meanings of polysemous words, as each vector signifies not just a word but a specific sense. Consequently, each word can be depicted using distinct word embeddings, each tailored to a particular context in which the word may be encountered. Notable examples include ELMo [26], ULMFit [27], and BERT [12], among others [28]. Recent methodologies for table retrieval utilise word embeddings to represent tabular data, employing vector similarity metrics to assess the relevance among tables. Depending on the nature of the search query, table retrieval can be classified into keyword-based or table-based searches [29]. In keyword-based search, the query consists of a set of keywords, resembling the conventional approach used by search engines like Google. In contrast, in table-based searches, the query itself takes the form of a table, and the goal is to determine a similarity score between the input table and potential candidate tables. In the experimentsdescribed in Section 5, tables function as queries to retrieve relevant tables, conducting a table-based search within the dataset. The study conducted by [PERSON] et al. [30] employed contextual word embeddings to enable table-based searches. They utilised a pretrained version of BERT, incorporating various types of data present in the table, including textual and numerical information, to provide context to BERT. This context was derived from the table's title, caption, column headings, and cell values. In Ref. [31], the authors propose a method to preserve table structure and context in semantic representations. The proposed ConvTab method, utilising Convolutional Neural Networks, enhances table classification and semantic feature generation for query-table similarity, demonstrating significant improvements in standard table retrieval metrics over existing methods. In this proposal, tables are treated as 2D images. In the approach proposed in Ref. [32], image and graphs were combined. The authors used WordNet to represent cell content, constructing a graph to capture the similarities of the table content. In the study by [PERSON] et al. [33], Word2 vec was utilised by the authors to generate semantic vectors. Table information was segmented into four semantic spaces: description, schema, records, and facets. The proposal included using multilayer perceptrons (MLP) for schema, recurrent convolutional neural networks for description, and 3D convolutional neural networks to represent records and facets. [PERSON] et al. [34] proposed a method to retrieve tables compatible with an input table. To this end, the approach tries to assess whether the content of the table belongs to the same domain.