RE-GAINS & EnCHANT: Intelligent Tool Manipulation Systems For Enhanced
  Query Responses
Sahil Girhepuje Siva Sankar Sajeev Purvam Jain Arya Sikder Adithya Rama Varma Ryan George Akshay Govind Srinivasan Mahendra Kurup Ashmit Sinha Sudip Mondal
Despite the remarkable success of LLMs, they still suffer from tool
invocation and tool chaining due to inadequate input queries and/or tool
argument descriptions. We propose two novel frameworks, RE-GAINS and EnCHANT,
enabling LLMs to tackle tool manipulation for solving complex user queries by
making API calls. EnCHANT is an open-source solution that makes use of an LLM
format enforcer, an LLM(OpenChat 3.5) and a retriever(ToolBench's API
Retriever). RE-GAINS is based on OpenAI models and embeddings using a special
prompt based on the RAP paper. Both solutions cost less than $0.01 per query
with minimal latency, therefore showcasing the usefulness of the frameworks.