Continued pre-training with LoRA on simplified triple-only documents improves LLM performance on the paper's own insight-extraction benchmarks, but those benchmarks are built from the same documents used for training.
BUSTER: a "BUSiness Transaction Entity Recognition" dataset
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abstract
Albeit Natural Language Processing has seen major breakthroughs in the last few years, transferring such advances into real-world business cases can be challenging. One of the reasons resides in the displacement between popular benchmarks and actual data. Lack of supervision, unbalanced classes, noisy data and long documents often affect real problems in vertical domains such as finance, law and health. To support industry-oriented research, we present BUSTER, a BUSiness Transaction Entity Recognition dataset. The dataset consists of 3779 manually annotated documents on financial transactions. We establish several baselines exploiting both general-purpose and domain-specific language models. The best performing model is also used to automatically annotate 6196 documents, which we release as an additional silver corpus to BUSTER.
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Learning Beyond the Surface: How Far Can Continual Pre-Training with LoRA Enhance LLMs' Domain-Specific Insight Learning?
Continued pre-training with LoRA on simplified triple-only documents improves LLM performance on the paper's own insight-extraction benchmarks, but those benchmarks are built from the same documents used for training.