# Domain-Specific Few-Shot Table Prompt Question Answering via Contrastive Exemplar Selection
## 2 Related Work  
### Table QA on General Domains  
Before the emergence of pre-trained language models, researchers primarily conducted table question answering studies using seq2 seq models [17]. The seq2 seq neural network consists of an encoder and a decoder. The encoder is responsible for encoding the input natural language query and table schema, capturing their semantic and structural information, while the decoder generates the predicted SQL statement. With the widespread application of pre-trained language models such as BERT [5] and GPT [18], the field of table question answering has also gradually explored methods based on pre-trained models. Among them, TABERT [6] encodes natural language texts and structured tables jointly to obtain semantic representations of natural language questions and tables. It introduces the masked column prediction (MCP) and cell value recovery (CVR) training objectives specific to table schemas, effectively improving the performance of general-domain table question answering tasks. Tapas [19] extends the MLM training objective to structured data and predicts answers by selecting subsets of cells and their aggregation operations. Tapas concatenates the question and serialized table as input, and two classification layers predict the selected cell subset and the aggregation operator, respectively. The aggregation operation is then performed on the selected cell subset to obtain the final answer.  
However, Tapas cannot handle large tables or databases containing multiple tables, since it processes the entire table in memory as context. TAPEX [7] simulates SQL executors through pre-training, with the actual results of executing SQL queries on tables serving as supervised signals to train the model's generated results. [PERSON] et al. [20] propose an execution-guided strategy to detect and exclude erroneous programs during the decoding process using partially generated SQL, further improving performance. This mechanism can be applied to any autoregressive generation model, assisting most semantic parsing pre-training models in achieving further performance improvements. [PERSON] et al. [21] propose a novel Chinese few-shot text classification method called CIPLUD that combines an improved prompt learning method and existing unlabeled data, which are used for the classification of a small amount of Chinese text data. They used the Chinese pre-trained language model to generate prompt prefixes with multiple masks and constructs suitable prompt templates for Chinese labels. Then, the one-class support vector machine-based unlabeled data leveraging (OCSVM-UDL) module to establish an OCSVM model in the trained text vector space, selects reasonable pseudo-label data for each category from a large amount of unlabeled data. [PERSON] et al. [22] propose to enhance the verbalizer and construct the refined external knowledge into a prompt-tuning (REKP) model. They employ external knowledge bases to increase the mapping space of tagged terms and design three refinement methods to remove noise data.  
Currently, table question answering techniques based on pre-trained language models are becoming increasingly mature, with evaluation results on datasets such as WiK-iSQL [23] surpassing human accuracy. However, existing methods mainly focus on general domains, and further research is needed for table question answering methods targeting specific domains.