TL:DR: LLM training is highly susceptible to GIGO.
(GIGO is what one gets when feeding LLM with "G"arbage "I"n, "G"arbage "O"ut.)
This is the current problem about making a vaccine signature fitting like a glove ... as tight as possible ... when populating the anti-malware (i.e. IDS/IPS/NDS/XNS) search pattern engine for use by Aho-Corasick-variant algorithms (such as Parallel-Failureless Aho Corasick).
However, LLM as a binary code-based detector for malware detection has a very limited benefit (it is there but only as a backend topical add-on after all other conditionals have been identified).
LLM lacks qualifying conditionals surrounding a premise data, and I have my doubts of using LLM for medical diagnosis as well: until we start having LLM denote the much-needed weighted combo-conditionals by "percentages".
(GIGO is what one gets when feeding LLM with "G"arbage "I"n, "G"arbage "O"ut.)
This is the current problem about making a vaccine signature fitting like a glove ... as tight as possible ... when populating the anti-malware (i.e. IDS/IPS/NDS/XNS) search pattern engine for use by Aho-Corasick-variant algorithms (such as Parallel-Failureless Aho Corasick).
However, LLM as a binary code-based detector for malware detection has a very limited benefit (it is there but only as a backend topical add-on after all other conditionals have been identified).
LLM lacks qualifying conditionals surrounding a premise data, and I have my doubts of using LLM for medical diagnosis as well: until we start having LLM denote the much-needed weighted combo-conditionals by "percentages".