Research on Fine-Tuning Optimization Strategies for Large Language Models in Tabular Data Processing
Xiaoyong Zhao, Xingxin Leng, Lei Wang, Ningning Wang · 2024
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Description
Xiaoyong Zhao and co-authors published this study in 2024 on teaching large language models to handle tables. The models shine at prose but stumble on rows and columns of data. The team tested three tweaks to fine-tuning. They cut extra digits after decimal points, mixed several data sets in training, and shuffled the order of key-value pairs in JSON records.
Each tweak helped in its own way. Trimming digits cut noise and sped learning. Mixing data sets made the model steadier on new tables. Shuffling key order taught it to cope when a table's layout shifts. The authors say such plain data hygiene can lift model work on tables a great deal. They offer the findings as a base for later work on business and science data.
Catalog record
| CREATOR | Xiaoyong Zhao, Xingxin Leng, Lei Wang, Ningning Wang |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 38 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |