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Large Signal Models

Large Signal Models (LSM) vs. Large Language Models (LLM)

Key Takeaways:

  • LSMs solve for inherent LLM hallucination problems, consequently usable in regulatory rich environments
  • LSM technology is additive & can optimize previous enterprise LLM investment (no 'rip and replace’)
  • LSMs can ingest LLM outputs  e.g. generated test data & large document summaries. Helpful for data enrichment, video/image to text, & image/test search
  • DeepDecision’s patented LSM technology proven with Google, Intel Community, and Financial Services customers

Incorporating Additive LSM technology corrects for critical LLM limitations and enables step change in capability

Using both LLM + LSM brings additive capabilities enabled by synergistic use.

LLM only
ChatGPT, Gemini, and Meta Llama 3 		LSM only
Deepdecision logo 		LLM + LSM
Deepdecision with ChaptGPT, Gemini, and/or Meta Llama 3
Uses unstructured text data   ✔ ✔ ✔
Unsupervised, General use AI ✔ ✔ ✔
Generates human-centric response to user queries ✔ ✔ ✔
Leverages deep-learning ✔ ✔ ✔
Native chatbot capability ✔ ❌ ✔
Generates creative content ✔ ❌ ✔
Can summarize unstructured content ✔ ✔ ✔
Makes use of data and signals other thn text (signal agnostic) ❌ ✔ ✔
User doesn't require explicit prompts ❌ ✔ ✔
Reduces false positives with statistically significant output (defendable in court) ❌ ✔ ✔
Avoids non-factual responses (e.g. creative hallucinations) ❌ ✔ ✔
Accommodates regulatory requirements ❌ ✔ ✔
Creates deployable models to be reused downstream ❌ ✔ ✔
Incorporates other LLM outputs (e.g. exports as JSON) ❌ ✔ ✔
Able to identify & counter adversarial LLM enabled tactics ❌ ❌ ✔