Summary of Towards Practical Tool Usage For Continually Learning Llms, by Jerry Huang et al.
Towards Practical Tool Usage for Continually Learning LLMs
by Jerry Huang, Prasanna Parthasarathi, Mehdi Rezagholizadeh, Sarath Chandar
First submitted to arxiv on: 14 Apr 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 The proposed paper investigates the capabilities and limitations of large language models (LLMs) in solving language-based tasks. The research suggests that LLMs struggle with adapting to non-stationary environments due to their static knowledge stored within their parameters. However, the study proposes using tools as a means to offload work and enable continual learning (CL). A synthetic benchmark is developed and existing NLP tasks are aggregated to create a more realistic testing scenario. The results show that scaling model size does not improve performance, but CL techniques can enable tool LLMs to adapt faster while forgetting less. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are super smart at solving language-based problems. But they have a big problem: they can’t keep up with new information or tools. They’re like students who memorize facts for a test and then forget them after it’s over. The solution is to use tools that help the model solve tasks, kind of like how we use calculators instead of doing math in our heads. This way, the model doesn’t have to remember everything, it just has to learn when to use each tool. The researchers created a special test to see if this works and found that it does! They think this could be really helpful for models that need to keep learning over time. |
Keywords
» Artificial intelligence » Continual learning » Nlp
