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## Analysis of results and outputs

### Baseline (SOLOIST)
The baseline SOLOIST is fine-tuned on different data splits to evaluate the performance of belief state predictions task under low-resource settings. As the results show that the baseline SOLOIST model did perform well when *fine-tuned* on relatively large data samples, however, it performed poorly under low-resource training data (esp. 25 & 50 dialogs). 

The belief state prediction task of SOLOIST utilizes *top-k* and *top-p* sampling to generate the belief state slots and values. Since the baseline SOLOIST uses open-ended generation, it's susceptible to generating random slot-value pairs that are not relevant to the dialog history. Below is an example of how the baseline model generated a slot-value pair that's not relevant to user goals and it completely missed two correct slot-value pairs.

| History | True belief states | Generated belief states |
| ----- | ----- | ----- |
|  **user:** we need to find a guesthouse of moderate price. <br  />**system:** do you have any special area you would like to stay?<br/>or possibly a star request for the guesthouse?<br  />**user:** i would like it to have a 3 star rating. | type = guesthouse<br/>pricerange = moderate<br/>stars = 3 | parking = yes<br/>stars = 3 |


### Prompt-based Methods

#### Value-based prompt

#### destination vs departure

#### Duplicate values

#### Multi-prompt methods

### Value Extraction