SwissNYF: Tool Grounded LLM Agents for Black Box Setting
Somnath Sendhil Kumar Dhruv Jain Eshaan Agarwal Raunak Pandey
While Large Language Models (LLMs) have demonstrated enhanced capabilities in
function-calling, these advancements primarily rely on accessing the functions'
responses. This methodology is practical for simpler APIs but faces scalability
issues with irreversible APIs that significantly impact the system, such as a
database deletion API. Similarly, processes requiring extensive time for each
API call and those necessitating forward planning, like automated action
pipelines, present complex challenges. Furthermore, scenarios often arise where
a generalized approach is needed because algorithms lack direct access to the
specific implementations of these functions or secrets to use them. Traditional
tool planning methods are inadequate in these cases, compelling the need to
operate within black-box environments. Unlike their performance in tool
manipulation, LLMs excel in black-box tasks, such as program synthesis.
Therefore, we harness the program synthesis capabilities of LLMs to strategize
tool usage in black-box settings, ensuring solutions are verified prior to
implementation. We introduce TOPGUN, an ingeniously crafted approach leveraging
program synthesis for black box tool planning. Accompanied by SwissNYF, a
comprehensive suite that integrates black-box algorithms for planning and
verification tasks, addressing the aforementioned challenges and enhancing the
versatility and effectiveness of LLMs in complex API interactions. The public
code for SwissNYF is available at https://github.com/iclr-dummy-user/SwissNYF.