Yale researchers propose AsyncLM: an artificial intelligence system for asynchronous calls to LLM functions

LLMs enable interactions with external tools and data sources, such as weather APIs or calculators, through function calls, unlocking various applications such as autonomous ai agents and neurosymbolic reasoning systems. However, the current synchronous approach to function calls, where LLMs pause token generation until each call completes execution, could be more resource-intensive and efficient. This process blocks LLM inference (one of the most computationally demanding steps) and limits concurrency since function calls must be completed sequentially. These inefficiencies grow with task complexity, making synchronous function calls impractical for handling multiple or complex operations.

Recent efforts to improve the efficiency of LLM function calls include parallelizing function executions, combining sequential calls, and optimizing function syntax. While these strategies reduce overhead, the fundamental challenge of synchronous interaction remains. Asynchronous function calls have been proposed, allowing LLMs to continue generating tokens while the function calls execute in the background. This approach allows execution and inference to overlap, improving resource utilization and reducing latency. Studies such as ReWOO have further explored consolidating function calls into single sessions, offering more efficient alternatives to traditional synchronous methods without relying on specific reasoning strategies, thus improving scalability across applications.

Researchers at Yale University propose AsyncLM, a system for asynchronous LLM function calls that improves efficiency by allowing LLMs to generate and execute function calls simultaneously. AsyncLM introduces an interrupt mechanism, which allows the LLM to receive ongoing notifications when calls from a function return, thus avoiding resource downtime. Using a domain-specific language (CML) and tuning strategies, AsyncLM ensures seamless integration of interrupts and precise handling of dependencies. Benchmark tests on the Berkeley Function Calling Leaderboard show that AsyncLM manages to complete tasks up to 5.4 times faster than synchronous methods while maintaining accuracy. Additionally, it enables novel applications of ai, including interactions between humans and LLM.

The CML is a domain-specific interface that allows asynchronous interactions between an LLM and an executor. It uses tokens such as (CALL), (INTR), (TRAP), (END), and (HEAD) to structure function calls, interrupts, and traps. LLMs launch tasks using CML, allowing parallel execution without blocking token generation. Interrupts notify the LLM of completed tasks, while traps temporarily pause generation when dependencies are not met. AsyncLM employs tuning with simulated data sets to optimize function scheduling, minimize task completion time, and handle interruptions effectively. The system integrates components such as token monitors, an executor, and an interrupt manager to manage asynchronous workflows efficiently.

The evaluation focuses on two key aspects: latency and correctness. Latency examines the effectiveness of asynchronous function calls in reducing task completion time compared to synchronous methods, while correctness evaluates their impact on generating accurate function calls. The Berkeley Function Call Leaderboard (BFCL) covered various real-world tasks, such as travel reservations and API interactions, with datasets for various scenarios, including a multi-step custom dataset for complex tasks. AsyncLM, tested in on-premises (using Llama models) and in the cloud (GPT-4o) settings, demonstrated latency reductions of up to 5.4x compared to synchronous methods. The results showed the efficiency of Async in parallelizing tasks and optimizing token generation cycles.

In conclusion, AsyncLM is designed to enable asynchronous function calls for LLM, allowing models and function executors to work independently. Unlike traditional synchronous methods, where LLM inference is blocked until a function call completes, AsyncLM uses an interrupt mechanism to notify the LLM during execution. Key innovations include an in-context interface for asynchronous interactions, LLM tuning to handle interrupt semantics, and efficient implementation within the inference process. Empirical results on BFCL show that AsyncLM reduces task completion latency by 1.6× to 5.4x, enabling more efficient LLM interactions with tools, data, and humans.

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Sana Hassan, a consulting intern at Marktechpost and a dual degree student at IIT Madras, is passionate about applying technology and artificial intelligence to address real-world challenges. With a strong interest in solving practical problems, he brings a new perspective to the intersection of ai and real-life solutions.