Solution

To address the challenges associated with hosting and training the resource-intensive GPT-4 model (Refer: https://platform.openai.com/docs/models/gpt-4-and-gpt-4-turbo), Quantiphi developed a lightweight LLM model suitable for commercial GPUs. This solution enables efficient and high-performing model execution, matching or surpassing GPT-4 capabilities for specific tasks.

Models are selected based on their suitability for fine-tuning and evaluation on a commercial-grade GPU, capacity for swift inference, and alignment with anticipated task outcomes.
Pinpointed and forecasted instances where changes in treatment were prompted by disease diagnoses. Isolated examples of asset pairs that exhibited transitions from one treatment type to another.
Identified factors affecting patients’ treatment continuance, such as cost, time, travel, job retention, family responsibilities, or specific barriers, no hesitancy.
Determined and predicted treatment or asset efficacy; categorized outcomes as still exists, recovered, deteriorated, slightly improved, no medical sentiment.
Identified post-diagnosis psychosocial impact for varying cancer types and labeled them such as anxiety, stress, suicidal tendencies, depression, panic, and no psychosocial impact.

Results

Using fine-tuned models – we were able to improve performance by 25% and reduce the infrastructure cost by 40%.
Finetuned models performed better on most NLP tasks than GPT4.

“It was a great experience collaborating with Quantiphi team which showcased their expertise in the area of NLP, machine learning, LLM and prompt engineering for problem statement in the engagement. The team was quite thorough in the experimentation as well as documenting the outcomes throughout the engagement. The team also has decent expertise in the area of managing the deliverables and provide consulting on potential ways forward. Thanks to entire team of Quantiphi for an insightful engagement.”
Text Analytics team at Merck Group