Summary of Telcolm: Collecting Data, Adapting, and Benchmarking Language Models For the Telecommunication Domain, by Camille Barboule et al.
TelcoLM: collecting data, adapting, and benchmarking language models for the telecommunication domain
by Camille Barboule, Viet-Phi Huynh, Adrien Bufort, Yoan Chabot, Géraldine Damnati, Gwénolé Lecorvé
First submitted to arxiv on: 20 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper investigates how Large Language Models (LLMs) can be adapted to tackle highly technical domains, particularly telecommunications (telco). The authors collect a massive corpus of domain-specific data and perform adaptation using various methodologies. They benchmark their results against larger generalist models in downstream tasks that require extensive knowledge of telco. The experiments show that domain-adapted LLMs, such as the Llama-2-7b model, can challenge large generalist models. Additionally, the authors find that adaptation can be restricted to a unique instruction-tuning step, eliminating the need for fine-tuning on raw texts beforehand. This research has significant implications for industrial applications and highlights the potential of domain-adapted LLMs in telco. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how computers can better understand technical information about telecommunications (telco). Right now, even very smart language models struggle with this type of data. The authors create a huge dataset of telco-related text and use it to adapt these language models. They then test the adapted models against bigger general-purpose models to see which one performs best. The results show that the adapted models can be just as good or even better than the more general ones. This is important because it could help companies in the telco industry use computers to analyze and understand their data more effectively. |
Keywords
» Artificial intelligence » Fine tuning » Instruction tuning » Llama
