Real World Applications Of LLM

Heard about a robot mimicking a person?

Heard about conversational AI creating bots that can understand and respond to human language?

Yes, those are some of the LLM applications.

Their many uses range from virtual assistants to data augmentation, sentiment analysis, comprehending natural language, answering questions, creating content, translating, summarizing, and personalizing. Their adaptability makes them useful in a wide range of industries.

One type of machine learning model that can handle a wide range of natural language processing (NLP) tasks is the large language model (LLM). These tasks include language translation, conversational question answering, text classification, and text synthesis. What we mean by “large” is the huge amount of values (parameters) that the language model can learn to change on its own. With billions of parameters, some of the best LLMs claim to be.

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Real-World Applications of LLM for Success

• GPT-3 (and ChatGPT), LaMDA, Character.ai, Megatron-Turing NLG – Text generation useful especially for dialogue with humans, as well as copywriting, translation, and other tasks
• PaLM – LLM from Google Research that provides several other natural language tasks
• Anthropic.ai – Product focused on optimizing the sales process, via chatbots and other LLM-powered tools
• BLOOM – General purpose language model used for generation and other text-based tasks, and focused specifically on multi-language support
• Codex (and Copilot), CodeGen – Code generation tools that provide auto-complete suggestions as well as creation of entire code blocks
• DALL-E, Stable Diffusion, MidJourney – Generation of images based on text descriptions
• Imagen Video – Generation of videos based on text descriptions
• Whisper – Transcription of audio files into text

LLM Applications

1. Computational Biology

Similar difficulties in sequence modeling and prediction arise when dealing with non-textual data in computational biology. Producing protein embeddings from genomic or amino acid sequences is a notable use of LLM-like models in the biological sciences. The xTrimoPGLM model, developed by Chen et al., can generate and embed proteins at the same time. Across a variety of activities, this model achieved better results than previous methods. The functional sequences were generated by training ProGen on control-tagged amino acid sequences of proteins by Madani et al. To generate antibody sequences, Shuai et al. created the Immunoglobulin Language Model (IgLM). The model showed that antibody sequences can be controlled and generated.

2. Using LLMs for Code Generation

The generation and completion of computer programs in multiple programming languages is one of the most advanced and extensively used applications of Large Language Models (LLMs). While this section mostly addresses LLMs designed for programming jobs, it is worth mentioning that general chatbots, which are partially trained on code datasets such as ChatGPT, are also finding more and more use in programming. Frameworks such as ViperGPT, RLPG, and RepoCoder have been suggested to overcome the long-range dependence issue by retrieving relevant information or abstracting it into an API specification. To fill in or change existing code snippets according to the given context and instructions, LLMs are employed in the code infilling and generation domain. LLMs designed for code infilling and generating jobs include InCoder and SantaCoder. Also, initiatives like DIDACT are working to better understand the software development process and anticipate code changes by utilizing intermediate phases.

3. Creative Work

Story and script generation has been the primary application of Large Language Models (LLMs) for creative jobs. Mirowski and colleagues present a novel method for producing long-form stories using a specialized LLM called Dramatron. Using methods such as prompting, prompt chaining, and hierarchical generation, this LLM uses a capacity of 70 billion parameters to generate full scripts and screenplays on its own. Co-writing and expert interviews helped qualitatively evaluate Dramatron’s efficacy. Additionally, Yang and colleagues present the Recursive Reprompting and Revision (Re3) framework, which makes use of GPT-3 to produce long stories exceeding 2,000 words in length.

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4. Medicine and Healthcare

Similar to their legal domain counterparts, LLMs have found several uses in the medical industry, including answering medical questions, extracting clinical information, indexing, triaging, and managing health records. Understanding and Responding to Medical Questions. Medical question answering entails coming up with answers to medical questions, whether they are free-form or multiple-choice. To tailor the general-purpose PaLM LLM to address medical questions, Singhal et al. developed a specific method using few-shot, CoT, and self-consistency prompting. They combined the three prompting tactics into their Flan-PaLM model, and it outperformed the competition on multiple medical datasets.

5. LLMs in Robotics

The incorporation of LLMs has brought improvements in the use of contextual knowledge and high-level planning in the field of embodied agents and robotics. Coding hierarchies, code-based work planning, and written state maintenance have all made use of models such as GPT-3 and Codex. Both human-robot interaction and robotic task automation can benefit from this method. Exploration, skill acquisition, and task completion are all accomplished by the agent on its own. GPT-4 suggests problems, writes code to solve them, and then checks if the code works. Both Minecraft and VirtualHome have used very similar methods.

6. Utilizing LLMs for Synthetic Datasets

One of the many exciting new avenues opened up by LLMs’ extraordinary in-context learning capabilities is the creation of synthetic datasets to train more targeted, smaller models. Based on ChatGPT (GPT-3.5), AugGPT (Dai et al., 2017) adds rephrased synthetic instances to base datasets. These enhanced datasets go above and beyond traditional augmentation methods by helping to fine-tune specialist BERT models. Using LLM-generated synthetic data, Shridhar et al. present Decompositional Distillation, a method for simulating multi-step reasoning abilities. To improve the training of smaller models to handle specific sub-tasks, GPT-3 breaks problems into sub-question and sub-solution pairs.

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Conclusion

Exciting new possibilities may arise in the future thanks to the introduction of huge language models that can answer questions and generate text, such as ChatGPT, Claude 2, and Llama 2. Achieving human-level performance is a gradual but steady process for LLMs. These LLMs’ rapid success shows how much people are interested in robotic-type LLMs that can mimic and even surpass human intelligence.