LLM Case Studies and Applications: Real-World Examples of Enhanced AI Accuracy

LLMs are transforming industries such as finance and healthcare by automating complex tasks and improving decision-making capabilities. Some key applications include:

LLMs are advancing healthcare by supporting diagnostics, patient communication, and biomedical research. Here are some examples:

• BioGPT
  BioGPT, introduced by Microsoft, is a transformer language model trained on biomedical literature, such as PubMed abstracts, to support various biomedical NLP tasks. These include end-to-end relation extraction, question answering, and document classification. BioGPT has shown superior performance over baseline methods in biomedical text generation and mining, streamlining tasks that require precise understanding of complex biomedical data.

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• Radiology-Llama2

Radiology-Llama2, a specialized version of Meta’s LLaMA 2, is fine-tuned for radiology tasks. It assists radiologists by interpreting radiological images and generating concise, clinically relevant reports, improving both the efficiency and accuracy of medical reporting in radiology.

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• Hippocratic AI
  Hippocratic AI is creating LLMs specifically designed for healthcare, focusing on safe and accurate patient-provider communication. By tailoring its LLM for clinical applications, Hippocratic AI enhances decision support, helping providers deliver precise and effective care.
• Health Acoustic Representations (HeAR)

Google's research team developed a bioacoustic foundation model that analyzes cough sounds to detect respiratory diseases, aiding in early diagnosis and screening, especially in underserved areas. This model provides a non-invasive way to identify respiratory conditions, offering a scalable solution for disease detection.

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As the finance sector grows, with projected values of $40.8 billion by 2029, LLMs are playing a transformative role. From supporting risk assessment to sentiment analysis, these models bring precision and insight to complex financial processes. Here are some of the ways LLMs are being used to address the intricate demands of the finance industry.

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• TradingGPT
  TradingGPT is a multi-agent system that emulates human traders' cognitive processes through a layered memory framework. This LLM-based system is designed for the stock and fund trading markets, where it processes complex financial data and engages in inter-agent communication to make informed trading decisions. Its layered memory, inspired by human cognition, allows it to retain and prioritize critical market information for strategic trading, making it highly adaptive to dynamic market conditions.

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• FLANG
  FLANG, a financial language model, is trained on datasets with specific financial terminology and objectives to perform NLP tasks such as sentiment analysis of managerial statements and classification of financial news. Its focused training on financial keywords allows it to understand sector-specific language, making it a valuable asset for financial sentiment analysis and risk assessment.

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• DISC-FinLLM
  DISC-FinLLM uses a multi-expert fine-tuning framework to enhance general LLM capabilities through multi-turn question answering, financial text processing, and retrieval-augmented generation. Fine-tuned on a dataset named DISC-FIN-SFT, it excels at consulting, NLP tasks, financial computations, and knowledge retrieval, providing a comprehensive toolkit for finance professionals.

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Even the most advanced LLMs, like GPT-4 and Claude, require fine-tuning to meet the specific demands of different industries. While these models are trained on vast datasets, their general-purpose nature can limit performance in highly specialized tasks. Post-training is essential to bridge this gap, ensuring the model aligns with unique industry requirements, such as precision, contextual understanding, and ethical alignment. Some of the common LLM post-training challenges include:

• Domain-specific knowledge:

General-purpose LLMs often struggle with industry-specific terminology and context, such as medical jargon in healthcare or complex financial regulations in finance. Without targeted fine-tuning, the model may produce generic or inaccurate responses.

• Performance reliability:

In high-stakes industries like healthcare or finance, even small errors can have significant consequences. Ensuring reliability in outputs—whether coding suggestions or patient communication—is a major challenge during post-training.

• Handling diverse use cases:

Many enterprises need their LLMs to handle diverse tasks, from customer service and data analysis to regulatory compliance. Fine-tuning models for multiple functions without introducing errors or inefficiencies requires meticulous data curation and iterative testing.

• Scalability and integration:

Scaling LLMs to meet enterprise-level demands often requires integrating APIs, plugins, and multimodal capabilities like image processing. Ensuring seamless integration while maintaining performance is a complex post-training hurdle.

• Bias and ethical alignment:

Pre-trained models can inherit biases from their training data, making post-training crucial for ensuring ethical alignment, reducing bias, and improving fairness in outputs.

• Data privacy and compliance:

Industries like finance and healthcare must address stringent privacy and compliance requirements. Fine-tuning models is essential to ensure sensitive data is handled securely and that outputs align with regulatory standards.

Deploying an off-the-shelf LLM is just the beginning. To unlock its full potential, businesses must invest in post-training with continuous optimization and partner with experts to ensure their model meets industry-specific needs, scales efficiently, and remains accurate, ethical, and reliable.

At Turing, we partner with clients across industries to optimize LLMs for real-world applications. Through customized fine-tuning, multimodality integration, and model evaluation, our team drives accuracy and efficiency to meet each client’s unique needs.

Here’s how Turing addressed some of the key challenges and delivered measurable results:

Offering: LLM evaluation

Overview: 

A leading U.S.-based technology company specializing in social media and AI needed a comprehensive evaluation of their custom-built LLM. The model showed strong performance in tasks like sentiment analysis but struggled with complex coding accuracy. Turing was brought in to develop a detailed understanding of the model’s strengths and weaknesses to drive accuracy improvements.

Solution: 

Over a two-week sprint, Turing implemented six targeted evaluation projects, including guided API evaluation, prompt breaking, LLM and human benchmark analysis, and community feedback aggregation. These evaluations provided actionable insights to guide specific improvements.

Result:

• Enhanced prompt reliability: Reduced failure rates for complex logical prompts and coding scenarios, leading to improved prompt handling and response accuracy.
• Benchmarking insights: Highlighted challenges in highly contextual tasks and uncovered errors in logic and syntax, setting a baseline for future training improvements.
• Actionable insights: Developed a detailed performance map, categorizing 20% of scenarios as high-priority, 40% as general weaknesses, and 40% as baseline tasks for ongoing optimization.

Read more about the case study here.

Offering: LLM training and enhancement

Overview: A U.S.-based global technology leader needed to improve reasoning and coding capabilities in one of its largest AI models. The complexity of their datasets required high-quality proprietary data and advanced techniques like supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF). The client needed a flexible approach to accommodate frequent guideline updates while maintaining data quality.

Solution:
In collaboration with Turing, the client initiated a targeted model enhancement strategy with a focus on implicit code execution (ICE) and code reasoning. Turing deployed a dedicated team of LLM advisors and trainers for tasks like code-related RLHF, data cleaning, and SFT prompt engineering. Key steps included:

• Notebook process: Developed a process for SFT data analysis, generating single-turn (ST) notebooks for isolated inputs and multi-turn (MT) notebooks for context-based inputs.
• Notebook rectification: Iteratively corrected and updated pre-generated notebooks based on new training insights.
• Feedback and data curation: Curated and labeled data from PDFs and .xlsx files, and provided feedback on the client’s RLHF tool.

Result:

• 50,000+ high-quality notebooks: Generated over 50,000 ST and MT notebooks, significantly enhancing the model’s training data volume and diversity.
• Improved data accuracy: Rectified existing notebooks, increasing data reliability and enhancing the model’s coding and debugging capabilities.
• Curated evaluation datasets: Created single-turn evaluation datasets from complex files (e.g., PDFs, .xlsx), ensuring thorough model assessment and improved scientific problem-solving.

Read more about the case study here.

Offering: Multimodal data integration

Overview: A leading AI research organization needed to evolve its LLM beyond basic text generation, aiming to handle complex tasks such as coding, data analysis, and real-time information retrieval. The model needed to seamlessly integrate APIs, plugins, and third-party tools to enhance its functionality and maintain high performance.

Solution:
In collaboration with Turing, the client undertook a multimodal transformation, integrating diverse tools like programming language interpreters, web browsers, image interpreters, and file systems. Key solution stages included:

• Technology selection: Identified a flexible tech stack to support multimodal interactions, ensuring compatibility and scalability with future advancements.
• Tool integration: Developed custom interfaces to connect the LLM with external tools, enabling efficient data exchange while preserving each tool’s functionality.
• Training process: Trained the LLM with real-world examples and phased complexity, leveraging RLHF techniques to refine tool usage across various tasks. Compatibility testing was conducted to ensure seamless performance.

Result:

• Automated programming: The LLM now writes, debugs, and optimizes code across multiple languages, accelerating software development and reducing time-to-market.
• Creative design: With access to image generators, the model assists in creating visuals and design drafts, enhancing productivity for creative teams.
• User adoption increase: User adoption surged by 80% within two quarters, driven by increased usage in coding (66%), summarization (52%), and research (45%).
• Team scaling and model advancement: Turing helped the client scale its team by 3x in three months to support evolving project needs, and over 1,000 test cases enhanced API calls, processing, and tool utilization, strengthening the model’s overall performance.

Read more about the case study here.

We’ve helped top foundation LLM companies optimize the way they approach LLM model evaluation, factuality and data analysis, multimodal reasoning, LLM training, and more. 

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