Summary of Transformllm: Adapting Large Language Models Via Llm-transformed Reading Comprehension Text, by Iftach Arbel et al.
TransformLLM: Adapting Large Language Models via LLM-Transformed Reading Comprehension Text
by Iftach Arbel, Yehonathan Refael, Ofir Lindenbaum
First submitted to arxiv on: 28 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper presents two novel large language models (LLMs), Phi-2-Legal and Mistral-Legal-7B, specifically designed for legal applications. These models leverage continued pre-training with over 500 million tokens of legal texts to improve capabilities in legal tasks. The authors demonstrate the effectiveness of their approach by using LLMs to convert raw training data into reading comprehension text, achieving superior performance in legal benchmarks even when trained on smaller datasets. This work highlights the potential of domain-adaptive pre-training and reading comprehension for developing highly effective domain-specific language models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study creates two special language models that are great at understanding law-related texts. They take existing language models and make them better by training them on lots of legal texts. This helps the models get really good at tasks like reading comprehension. The results show that these models can do even better than bigger models that were trained on a lot more data. This is important because it means we can use this approach to make models that are super good at understanding different types of text, not just law-related ones. |
