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[PaperReading] ToolDec: Using Finite-State-Machine to Eliminate Hallucinations in LLM Decoding

less than 1 minute read

Published: December 06, 2023

Using FSM to enhance tool-augmented LLM.

Key intuition and motivation:

the generation of api calls can be explicitly represented using states. By eliminating tokens outside the predefined states vocabulary, we can greatly reduce hallucinations. Core contribution: beyond a extensible method in augmented LLM, it provides a general method of incorporating domain-knowledge to eliminate hallucinations.

[PaperReading] BioMANIA: a Pipeline for Bioinformatics

less than 1 minute read

Published: October 14, 2023

Using LLM to properly choose and precisely use biomedical apis.

Brief Summary

This paper provide a pipeline for choosing and using biomedical functions/apis. The pipeline combines

• a BERT-based API retriever. It will take a task instruction and return a short list of API candidates.
• a GPT-4-based API predictor. It will (with in-context learning) choose the most relevant API.
• a GPT-4-based generation pipeline of api calling that takes several steps to give the final script and show the result.

[PaperReading] ToolDec: Using Finite-State-Machine to Eliminate Hallucinations in LLM Decoding

less than 1 minute read

Published: December 06, 2023

Using FSM to enhance tool-augmented LLM.

Key intuition and motivation:

the generation of api calls can be explicitly represented using states. By eliminating tokens outside the predefined states vocabulary, we can greatly reduce hallucinations. Core contribution: beyond a extensible method in augmented LLM, it provides a general method of incorporating domain-knowledge to eliminate hallucinations.

[PaperReading] BioMANIA: a Pipeline for Bioinformatics

less than 1 minute read

Published: October 14, 2023

Using LLM to properly choose and precisely use biomedical apis.

Brief Summary

This paper provide a pipeline for choosing and using biomedical functions/apis. The pipeline combines

• a BERT-based API retriever. It will take a task instruction and return a short list of API candidates.
• a GPT-4-based API predictor. It will (with in-context learning) choose the most relevant API.
• a GPT-4-based generation pipeline of api calling that takes several steps to give the final script and show the result.