Update README.md
Browse files
README.md
CHANGED
@@ -83,3 +83,93 @@ configs:
|
|
83 |
- split: test
|
84 |
path: physics/test-*
|
85 |
---
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
83 |
- split: test
|
84 |
path: physics/test-*
|
85 |
---
|
86 |
+
|
87 |
+
# Prerequisite RElation LEARNing (PRELEARN)
|
88 |
+
|
89 |
+
Original Paper: https://ceur-ws.org/Vol-2765/paper164.pdf
|
90 |
+
|
91 |
+
This dataset contains a collection of binary-labelled concept pairs (A,B) extracted from textbooks on four domains: **data mining**, **geometry**, **physics** and **precalculus**.
|
92 |
+
Then, domain experts were asked to manually annotate if pairs of concepts showed a prerequisite relation or not, therefore the dataset consists of both positive and negative concept pairs.
|
93 |
+
|
94 |
+
We obtained the data from the original repository, making only one modification: undersampling the training data. To evaluate generative models in in-context learning, it's essential to have a balanced distribution for sampling examples in a few-shot setting. The undersampling process was carried out randomly, and separately for each domain.
|
95 |
+
|
96 |
+
## Example
|
97 |
+
|
98 |
+
Here you can see the structure of the single sample in the present dataset.
|
99 |
+
|
100 |
+
```json
|
101 |
+
{
|
102 |
+
"concept_A": string, # text of the concept A
|
103 |
+
"wikipedia_passage_concept_A": string, # paragraph of wikipedia corresponding to concept A
|
104 |
+
"concept_B": string, # text of the concept B
|
105 |
+
"wikipedia_passage_concept_B": string, # paragraph of wikipedia corresponding to concept B
|
106 |
+
"target": int, # 0: B non è preconcetto di A, 1: B è preconcetto di A
|
107 |
+
}
|
108 |
+
```
|
109 |
+
|
110 |
+
## Statitics
|
111 |
+
|
112 |
+
| PRELEARN Data Mining | 0 | 1 |
|
113 |
+
| :--------: | :----: | :----: |
|
114 |
+
| Training | 109 | 109 |
|
115 |
+
| Test | 50 | 49 |
|
116 |
+
|
117 |
+
| PRELEARN Physics | 0 | 1 |
|
118 |
+
| :--------: | :----: | :----: |
|
119 |
+
| Training | 315 | 315 |
|
120 |
+
| Test | 100 | 100 |
|
121 |
+
|
122 |
+
| PRELEARN Geometry | 0 | 1 |
|
123 |
+
| :--------: | :----: | :----: |
|
124 |
+
| Training | 332 | 332 |
|
125 |
+
| Test | 100 | 100 |
|
126 |
+
|
127 |
+
| PRELEARN Precalculus | 0 | 1 |
|
128 |
+
| :--------: | :----: | :----: |
|
129 |
+
| Training | 408 | 408 |
|
130 |
+
| Test | 100 | 100 |
|
131 |
+
|
132 |
+
## Proposed Prompts
|
133 |
+
|
134 |
+
Here we will describe the prompt given to the model over which we will compute the perplexity score, as model's answer we will chose the prompt with lower perplexity.
|
135 |
+
Moreover, for each subtask, we define a description that is prepended to the prompts, needed by the model to understand the task.
|
136 |
+
|
137 |
+
Description of the task: "Dati due concetti, indica se primo concetto è un prerequisito o meno per il secondo.\nUn concetto A è prerequisito per un concetto B, se per comprendere B devo prima aver compreso A.\nI seguenti concetti appartengono al dominio: {{domain}}.\n\n"
|
138 |
+
|
139 |
+
### Cloze Style:
|
140 |
+
|
141 |
+
Label (**B non è prerequisito di A**): "{{concept_B}} non è un prerequisito per {{concept_A}}"
|
142 |
+
|
143 |
+
Label (**B è prerequisito di A**): "{{concept_B}} è un prerequisito per {{concept_A}}"
|
144 |
+
|
145 |
+
### MCQA Style:
|
146 |
+
|
147 |
+
```
|
148 |
+
Domanda: il concetto {{concept_B}} è un prerequisito per la comprensione del concetto {{concept_A}}? Rispondi sì o no:
|
149 |
+
```
|
150 |
+
|
151 |
+
## Some Results
|
152 |
+
|
153 |
+
The following results are given by the Cloze-style prompting over some english and italian-adapted LLMs.
|
154 |
+
|
155 |
+
| PRELEARN (AVG) | ACCURACY (15-shots) |
|
156 |
+
| :-----: | :--: |
|
157 |
+
| Gemma-2B | 60.12 |
|
158 |
+
| QWEN2-1.5B | 57.00 |
|
159 |
+
| Mistral-7B | 64.50 |
|
160 |
+
| ZEFIRO | 64.76 |
|
161 |
+
| Llama-3-8B | 60.63 |
|
162 |
+
| Llama-3-8B-IT | 63.76 |
|
163 |
+
| ANITA | 63.77 |
|
164 |
+
|
165 |
+
## Aknwoledge
|
166 |
+
|
167 |
+
We want to thanks this resource's authors for publicly releasing such an interesting dataset.
|
168 |
+
|
169 |
+
Further, We want to thanks the student of [MNLP-2024 course](https://naviglinlp.blogspot.com/), where with their first homework tried different interesting prompting strategies.
|
170 |
+
|
171 |
+
The data is freely available through this [link](https://live.european-language-grid.eu/catalogue/corpus/8084).
|
172 |
+
|
173 |
+
## License
|
174 |
+
|
175 |
+
The data come under the license [Creative Commons Attribution Non Commercial Share Alike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)
|