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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.06574 |
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| _version_ | 1866917689814941696 |
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| author | de Dampierre, Charles Mogoutov, Andrei Baumard, Nicolas |
| author_facet | de Dampierre, Charles Mogoutov, Andrei Baumard, Nicolas |
| contents | LLMs are now responsible for making many decisions on behalf of humans: from answering questions to classifying things, they have become an important part of everyday life. While computation and model architecture have been rapidly expanding in recent years, the efforts towards curating training datasets are still in their beginnings. This underappreciation of training datasets has led LLMs to create biased and low-quality content. In order to solve that issue, we present Bunka, a software that leverages AI and Cognitive Science to improve the refinement of textual datasets. We show how Topic Modeling coupled with 2-dimensional Cartography can increase the transparency of datasets. We then show how the same Topic Modeling techniques can be applied to Preferences datasets to accelerate the fine-tuning process and increase the capacities of the model on different benchmarks. Lastly, we show how using Frame Analysis can give insights into existing biases in the training corpus. Overall, we argue that we need better tools to explore and increase the quality and transparency of LLMs training datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_06574 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards Transparency: Exploring LLM Trainings Datasets through Visual Topic Modeling and Semantic Frame de Dampierre, Charles Mogoutov, Andrei Baumard, Nicolas Computation and Language Artificial Intelligence LLMs are now responsible for making many decisions on behalf of humans: from answering questions to classifying things, they have become an important part of everyday life. While computation and model architecture have been rapidly expanding in recent years, the efforts towards curating training datasets are still in their beginnings. This underappreciation of training datasets has led LLMs to create biased and low-quality content. In order to solve that issue, we present Bunka, a software that leverages AI and Cognitive Science to improve the refinement of textual datasets. We show how Topic Modeling coupled with 2-dimensional Cartography can increase the transparency of datasets. We then show how the same Topic Modeling techniques can be applied to Preferences datasets to accelerate the fine-tuning process and increase the capacities of the model on different benchmarks. Lastly, we show how using Frame Analysis can give insights into existing biases in the training corpus. Overall, we argue that we need better tools to explore and increase the quality and transparency of LLMs training datasets. |
| title | Towards Transparency: Exploring LLM Trainings Datasets through Visual Topic Modeling and Semantic Frame |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2406.06574 |