Summary of Asynchronous Llm Function Calling, by in Gim et al.
Asynchronous LLM Function Calling
by In Gim, Seung-seob Lee, Lin Zhong
First submitted to arxiv on: 9 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 A novel system called AsyncLM is proposed for large language models (LLMs) to improve operational efficiency by enabling concurrent function execution. The current approach to LLM function calling is synchronous, where each call blocks LLM inference, limiting its operation and concurrent function execution. AsyncLM introduces an interrupt mechanism to asynchronously notify the LLM when function calls return, allowing it to generate and execute function calls concurrently. This leads to a reduction in end-to-end task completion latency by 1.6x-5.4x compared to synchronous function calling on benchmark tasks from the Berkeley function calling leaderboard (BFCL). The system also includes an in-context protocol for function calls and interrupts, as well as a fine-tuning strategy to adapt LLMs to interrupt semantics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AsyncLM is a new way for large language models (LLMs) to work more efficiently. Right now, when an LLM needs to use data from outside itself, it has to stop what it’s doing and wait until the job is done. This can slow down the entire process. AsyncLM lets the LLM keep working on other tasks while it waits for the external data. This makes things happen much faster – up to 5 times faster in some cases! The new system also includes special instructions and a way to adjust how well the LLM understands these new ways of communicating. |
Keywords
» Artificial intelligence » Fine tuning » Inference » Semantics
