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Test your LLM's math skills with our benchmark for complex problems and step-by-step reasoning

Test your LLM's math skills with our benchmark for complex problems and step-by-step reasoning

Test your LLM's math skills with our benchmark for complex problems and step-by-step reasoning

Toloka Team

Jun 28, 2023

Jun 28, 2023

Essential ML Guide

Essential ML Guide

How do LLMs work?

Lately, large language models (LLMs) have become one of the most popular fields of artificial intelligence. All across the world, these models are headed for design, evaluation, and implementation. Organizations are increasingly pursuing the goal of developing language-understanding software.

New avenues for studying human language and cognition are made possible by LLMs. As a result of its growth, neural networks are now widely used for natural language processing tasks across a wide range of business domains. Let's take a closer look at what LLMs are and how they work.

What is a large language model (LLM)?

Large Language Models are machine learning models that employ Artificial Neural Networks and large data repositories to power Natural Language Processing (NLP) applications. An LLM serves as a type of AI model designed to be able to grasp, create, and manipulate natural language.

These models rely on deep learning technologies, like neural networks, to perform word processing and analyze textual information. Large language models are taught on immense amounts of text training data to learn statistical relationships and representations of language through training, enabling generalization beyond simple memorization.

Language models are called large because they represent large-scale systems in addition to the massive training dataset. In fact, they are so large that they cannot be launched on a single machine. That's why they are usually accessible through a web interface or API.

Large language models are suitable for solving multi-faceted tasks practically for any textual query of the user, as opposed to being trained for one specific task. By studying data such as books, documents, and web pages, they can understand the intricacies of any language. Thus, they can devise content and generate text indistinguishable from human writing in multiple languages.

One of the prevalent large language models at the moment is OpenAI's GPT (Generative Pre-trained Transformer). GPT-3 is one of the world's major artificial intelligence models, and it can perform tasks such as qualitatively generating lengthy articles, replying to various inquiries, and even writing software code.

How large language models work

Large language models consist of a neural network with tens of millions to hundreds of billions of parameters. Usually, the more parameters a neural network has, the better it consolidates its skills and knowledge. 

Such neural networks exist within advanced AI assistants, allowing us to communicate with the machine. So, how does a large language model work? The LLMs are introduced to available textual data in the preparation phase to explore the overall structure and rules of the language. The massive datasets are then submitted to a model referred to as a transformer during a training process. Transformer is a type of deep-learning algorithm.

The core notion of a language model consists of its capacity to predict the next word, called a token, based on the pre-existing text. A family of machine learning architectures called transformer architecture has turned this process into a more human-like one.

The transformer model mixes the option of model pre-training, parallel data processing, and extensive application of the attention mechanism. Pre-training implies that a machine learning model is first trained on a large set of text data, and then a process of fine-tuning may be applied to it to address a specific problem. 

Fine-tuning helps enhance a pre-trained model that already has some knowledge with minor adjustments without having to teach it from the beginning.

The attention mechanism assigns weights to all input tokens based on their relevance to a specific position in the sequence being processed. The neural network analyzes which position of the incoming sequence is essential for the specific position of the sequence in the output.

The transformer model, among other things, allows for processing all input text simultaneously or in parallel rather than sequence. This allows transformers to learn from vast amounts of data and significantly reduce training time compared to other methods.

Transformers can handle several excerpts of long texts at the same time. The model does not neglect the beginning of the text but rather uses what has already been studied, builds better connections between words, and can make sense by understanding the context of a considerable amount of data.

How an LLM is built

Building a large language model (LLM) involves several key stages, each contributing to the creation of a robust and efficient model capable of understanding and generating human-like text. Here’s an overview of the process:

  1. Data Collection and Preparation
    The first step in building an LLM is gathering a large and diverse dataset. These datasets typically contain vast amounts of text from numerous sources, including books, articles, websites, and other forms of written communication. The data must be pre-processed to remove noise, handle incomplete or erroneous information, and structure it in a way that is useful for training the model. The more diverse the dataset, the better the LLM can generalize across different topics and languages.

  1. Model Architecture Selection
    Once the data is prepared, the next step is to choose the appropriate architecture for the LLM. The transformer model, which is based on attention mechanisms, has become the standard for most modern LLMs. This architecture allows the model to handle large amounts of data in parallel and learn long-range dependencies between words, making it ideal for processing natural language.

  1. Pre-training
    During pre-training, the LLM learns to predict the next word in a sequence based on the context provided by the previous words. This phase typically involves massive computational power and extensive time, often using GPUs or specialized hardware like TPUs. The goal of pre-training is for the model to develop a general understanding of language patterns, grammar, facts, and relationships between words.

  1. Fine-tuning
    After pre-training, the LLM undergoes fine-tuning, where it is tailored for specific tasks or industries. Fine-tuning involves providing the model with labeled data and refining its capabilities for particular use cases, such as medical text analysis, legal document processing, or customer support queries. This phase ensures that the model is optimized for its intended purpose and can provide high-quality, relevant results.

  1. Model evaluation
    Once fine-tuned, the LLM is evaluated using various benchmarks to assess its performance. These evaluations typically measure the model’s accuracy, relevance, and ability to handle edge cases. Based on feedback, further adjustments can be made to the model, including additional fine-tuning or adjustments to the architecture. This iterative process continues until the model meets the required standards for real-world applications.

General knowledge vs domain-specific LLMs

Recent advancements in AI have been unprecedented, with general-purpose large language models (LLMs) like GPT-3 making a significant impact across various fields. 

These models can handle a wide range of tasks, from creating marketing copy to writing emails, and they have been trained on a large amount of data. 

Although one of their most significant advantages is their wide range of applications, they aren't always appropriate for highly specialized ones where accuracy and domain-specific knowledge are essential.

This is where domain-specific LLMs come into play. Unlike general-purpose models, domain-specific models are trained on data from a particular field – whether it’s healthcare, law, finance, or any other industry – enabling them to offer deeper insights and more reliable performance in those areas. 

While the versatility of general-purpose LLMs is valuable, specialized models' ability to take on complex, field-specific challenges sets them apart.

Limitations of general LLM models

General-purpose LLMs are trained on highly diverse data, which gives them a broad understanding of human language. They can generate text on almost any topic and answer various questions. 

Still, their broad knowledge can also be a limitation when it comes to industries that require specialized knowledge. For instance, in fields like healthcare, legal work, or finance, the complexity and nuances of the data require models that go beyond general understanding and can handle the intricacies of these industries.

In medical fields, a general-purpose LLM might be able to summarize an academic research paper, but it would lack the ability to diagnose conditions based on patient history or suggest treatments tailored to a person's unique genetic profile. 

With legal matters, a general model could help with general legal questions, but it wouldn't have the expertise to analyze complex case law, for example.

The role of domain-specific LLMs

The domain-specific LLMs aim to get around these restrictions by concentrating on a single area and training on pertinent, superior domain-specific data. 

These models can obtain more accurate results because they comprehend the terminology, context, and main difficulties of the field. 

For example, a healthcare-specific LLM trained in medical research, patient records, and clinical guidelines can help physicians diagnose uncommon conditions, find possible treatments, or even forecast patient outcomes.

A domain-specific LLM in finance would typically provide far more accurate insights than those offered by a general-purpose model by analyzing market trends, assessing financial reports, and forecasting stock movements. 

This is also true in the legal field, where qualified LLMs can help draft contracts and perform legal research.

The importance of specialization

In all probability, domain-specific LLMs will become the norm in industries like healthcare, law, and finance, especially as AI becomes more widely used for tasks that require in-depth knowledge. 

The combination of highly specialized, field-specific models and robust, broad-based models will fuel ongoing innovation and efficiency across sectors.

The primary benefit of domain-specific LLMs is their capacity to reliably and accurately manage challenging and high-stakes tasks. 

Decisions in fields like healthcare, law, and finance must be made using specialized knowledge. The reason is that errors or discrepancies can have serious repercussions. For instance, a poor financial analysis could result in losses, while in healthcare, an incorrect recommendation could result in inappropriate treatments.

Specialized LLMs reduce these risks. These models can analyze large volumes of domain-specific data, producing more accurate and well-informed insights than general-purpose models. 

Specialized models will become a crucial component of workflows as industries continue to adopt AI, providing not only greater efficiency but also more accurate and dependable decision-making in expert settings.

Imagining a hybrid future

Without a doubt, general-purpose models still have a place in the mix of things – even though domain-specific models are necessary for some applications. 

The two are likely to coexist in the future of artificial intelligence, becoming more integrated as technology develops. 

Without too much imaginative faculty, it is entirely possible to picture a hybrid future in which specialized models manage intricate industry-specific problems, and general models support routine, everyday tasks. 

Training data for large language models

A large language model demands large amounts of text data for model training, thus ensuring that contextually relevant responses are provided. 

The large language model training process may involve any kind of text data, and there is no need for any sort of labeling of this data beforehand. 

However, human input is needed at this stage to collect and clean datasets.

The outcome of training is the language model successfully predicting the next word based on information about the words that come before it. 

Any book, commentary, or essay is already a ready-made training piece of data, as it already consists of a tremendous array of "word-next word" type sequences. However, not any text written by a human will do. Diverse text, high-quality, and well-curated datasets are necessary for training. 

Importantly, it's not just about collecting data—expert human involvement is crucial to verifying that the data is clean, relevant, and balanced. 

AI Tutors and data experts play an indispensable role in building the datasets that feed into the model. Trainers are specialists who are responsible for creating data and evaluating output, ensuring that irrelevant, biased, or harmful content is removed or isn’t used in training. 

Their expertise ensures the model learns in a way that improves its ability to provide more accurate and meaningful responses.

AI Tutors, in particular, are essential when the model is being trained for specific industries. For instance, if an LLM is being fine-tuned for the healthcare or legal sectors, AI Tutors ensure the data includes industry-specific language, terms, and nuances. 

Focused curation helps the model respond more effectively to specialized queries and real-world scenarios.

Additionally, data experts are responsible for balancing the datasets, making sure the model is trained on a diverse range of examples. It's a vital stage of the process that helps prevent overfitting to particular biases or narrow perspectives, ensuring the model can provide equitable responses across different demographics, languages, and contexts.

Limitations of large language models and how to bridge them

While LLMs have made great strides, they are not without limitations. 

Understanding these shortcomings is crucial to improving their performance and ensuring that they are used effectively across industries.

  1. Context and long-term coherence issues
    LLMs struggle to maintain coherence over more extended conversations or documents. This happens because they process data in chunks, often losing track of earlier parts of the conversation as the context expands. For instance, a model may provide an accurate response to the immediate query but fail to integrate earlier information, leading to disjointed or repetitive answers.

Solution: One promising fix lies in refining model architectures. Researchers are exploring "memory-augmented neural networks" and "retrieval-augmented generation" techniques, which allow LLMs to "remember" past interactions and incorporate that context into their responses. These methods are designed to extend the model's ability to recall relevant information over long sequences, improving its overall coherence.

  1. Bias and fairness concerns
    Another significant limitation is the potential for bias in LLMs. Since these models learn from vast amounts of text data, they may inadvertently replicate harmful stereotypes or reinforce social biases present in the training material. For example, a model might generate biased or discriminatory content based on the demographic skew of its data.


Solution: One approach to address this issue is through improved data curation. By diversifying the data used to train models, we can reduce the risk of bias. Additionally, techniques like "bias correction algorithms" are being developed to detect and mitigate biases during both the training and inference stages. Another potential solution is the involvement of human moderators or experts who review and adjust model outputs for fairness, ensuring that the responses meet ethical standards.

  1. Lack of common sense and reasoning
    While LLMs are adept at generating fluent text, they often lack common sense reasoning. They can produce logically sound sentences without necessarily reflecting a deep understanding of the world. This can result in responses that are technically accurate but contextually flawed, such as answering questions based on patterns rather than real-world knowledge.

Solution: To address this, researchers are incorporating "commonsense reasoning" into LLMs. These models are trained on specialized datasets designed to teach them more nuanced, real-world reasoning. One approach is "knowledge-grounded generation," where LLMs are supplemented with external knowledge sources like structured databases or real-time information to provide more contextually appropriate answers.

  1. Data privacy and security
    As LLMs are trained on vast datasets, often scraped from the web, concerns about privacy and the security of sensitive data arise. LLMs might inadvertently generate responses based on private or confidential information that was included in the training data.

Solution: Data privacy measures can be integrated by adopting techniques like federated learning, where data is processed locally and only model updates are shared, ensuring that private data does not leave the user's device. Additionally, "differential privacy" techniques can be applied to ensure that individual data points are obscured during the model’s training process, preventing the model from memorizing and reproducing sensitive information. 

While they have not been perfected yet, the ongoing efforts to refine and improve these LLMs hold the potential for groundbreaking advancements in AI. By addressing limitations, we can ensure that LLMs are more accurate, fair, and valuable tools across various applications.

To better understand how LLMs work in practice, it's helpful to explore real-world applications. The following examples will give you a clearer view of how these models are utilized across different domains.

Applications for enhancing productivity

Large language models can boost a business's productivity in many ways. Their power to interpret human queries and resolve relatively complex problems helps us assign time-consuming routine tasks to chatbots and then simply verify the outcomes.

Pre-trained transformer models may be designed to quickly fulfill the appropriate goals of your business. They already possess the knowledge required and deeply comprehend the target language, enabling you to focus selectively on fine-tuning the model for any specific tasks you have in mind. Here are some examples:

Sales and customer service

Chatbots and AI assistants custom-trained for your business deliver high-quality, prompt feedback and support to your customers. The sales bot can interact with your customers, present the range of products, inform them about discounts and promotions, and motivate them to make a purchase.

Content generation 

Human-like text generation is one of the key features of language models. AI creates texts that help your products stand out among competitors. Additionally, they can acquire the ability to generate documentation based on your knowledge base, which can significantly speed up your company's document workflow.

Content generalization

Quite often, especially in large companies, a considerable amount of internal documents and various text materials are piled up in random order. It would be more convenient for employees to interact with them if they were sorted into categories. LMMs are very capable at helping to categorize such documents.

Content moderation

LLMs assist in identifying spam, profanity, and toxic content on your resources according to the set guidelines. Large models shield users from any type of controversial content that might be considered unsafe or inappropriate, potentially tainting the platform's online reputation.

Niche applications for specialized fields

Large language models (LLMs) are powerful tools for streamlining business processes, but their true potential lies in specialized, expert-driven fields. When fine-tuned to meet specific needs, LLMs provide tailored solutions that enhance efficiency, support decision-making, and drive innovation.

In this section, we’ll explore how LLMs are being applied in established industries—such as healthcare, law, engineering, and education—and in more unexpected fields like archaeology, aerophysics, and computer design. While the first set of industries are well-established expert domains, the latter highlight the growing potential of LLMs in cutting-edge sectors.

In these specialized fields, LLMs can handle complex tasks, from interpreting medical records to analyzing legal documents and processing technical data. 

Healthcare

Artificial intelligence is opening up revolutionary possibilities in the healthcare industry by facilitating a deeper understanding of intricate biological data and improving the precision of diagnoses and treatments. Here’s what’s happening in the exciting field of medicine:

Personalised medication

Healthcare professionals can use AI to analyze genomic sequences to identify genetic variations affecting a patient's treatment response. With personalized medicine, treatment plans are customized based on each patient's distinct genetic profile. This method matches therapies to patients' unique biological needs, ensuring more effective treatments and reducing side effects.

Bloodwork diagnostics

AI improves the interpretation of blood test results by rapidly examining biomarkers to find early indicators of inflammatory and chronic diseases. By quickly processing vast amounts of data, AI enables medical practitioners to detect possible health hazards before they become more serious. This opens the door to earlier interventions and better patient outcomes.

Drug development

AI is accelerating the sometimes expensive and drawn-out drug development process. AI helps researchers find promising drug candidates more quickly by forecasting which molecular compounds are likely to be effective in treating particular diseases. This helps shorten the time it takes to introduce new treatments to the market and speeds up the development of targeted therapies.

Epigenetics advancements

The study of epigenetics, which examines how outside influences can affect gene expression, heavily relies on artificial intelligence. This research is revealing important new information about diseases like cancer, neurodegenerative diseases, and mental health issues. AI assists scientists in understanding the interactions between genetic makeup and environmental factors, opening the door to more effective treatments that address the different facets of disease.

Legal

Although the legal industry is often perceived as being somewhat resistant to change, LLMs are beginning to change the way legal teams work. By automating the prosaic and time-consuming parts of legal practice, they are enhancing the skills of current practitioners rather than replacing them. This allows them to concentrate on the work that really needs human insight. Here are some ways in which AI is making its mark in the field of law:

Draughting and reviewing contracts

Contract creation and review frequently require navigating complex legal jargon and identifying possible problems. By swiftly reading through vast amounts of text, highlighting any inconsistencies, and spotting possible dangers, LLMs can relieve legal teams of some of their workload. They can recommend changes or revisions or even produce boilerplate text according to particular requirements, which makes them especially helpful for standard contracts.

Litigation support

Lawyers deal with a mountain of data when managing cases. LLMs can assist by organizing case files, identifying key points of law, and even summarizing long-winded depositions or trial transcripts. Large language models help ensure that no critical information is overlooked, freeing up attorneys to focus on strategy and client engagement while AI handles the bulk of the details.

Regulatory and compliance checks

The world of law is littered with ever-evolving regulations, from financial compliance to environmental law. Keeping track of these is a full-time job in itself. LLMs step in by scanning regulatory documents and keeping legal teams up-to-date on changes that affect their practice. Whether it’s checking for compliance with new privacy laws or anticipating potential legal risks, these models provide a faster, more reliable way to stay compliant.

Managing intellectual property

In industries such as tech and entertainment, a company's intellectual property (IP) is frequently its most valuable asset. By expediting patent and trademark searches, comparing new filings to existing IP, and spotting possible conflicts or infringements, LLMs play a critical role. 

When AI handles the preliminary steps, legal teams can lead the strategy and litigation components of IP protection. In other words, rather than replacing legal professionals, LLMs are augmenting the capabilities of those already specialists in their independent fields, helping them focus on the work that genuinely requires human insight while automating the repetitive and time-consuming aspects of legal practice.

Performing legal research

Despite being absolutely necessary, legal research can be a daunting undertaking. It could take weeks to manually gather pertinent precedents and important arguments from millions of legal documents, statutes, and case law, but LLMs have the ability to sift through them. Because of its speed and efficiency, legal research is not only completed more quickly but also more thoroughly, giving attorneys access to the most current and reliable information possible.

Finance

The financial sector is leveraging cutting-edge applications of large language models (LLMs) to reimagine traditional workflows and unlock new opportunities. These innovations extend far beyond efficiency, driving insights and strategies previously unattainable with conventional tools.

  1. Synthetic data generation for financial modeling
    LLMs create synthetic datasets that mimic actual financial behaviors without compromising privacy in areas where real-world data is limited or sensitive. These datasets enable institutions to test new models, develop innovative products, and assess market reactions in a controlled yet realistic environment.

  1. Real-time macroeconomic forecasting
    LLMs are revolutionizing how financial institutions predict macroeconomic trends. These models synthesize insights into actionable forecasts by ingesting unstructured data from global news, regulatory announcements, and market commentary. Their ability to process multilingual inputs also ensures comprehensive coverage of international markets.

  1. Behavioral finance insights
    Beyond traditional numerical analysis, LLMs analyze behavioral patterns in text-based data, such as investor sentiment from social media or psychological cues in earnings calls. By correlating these patterns with market movements, financial institutions gain a deeper understanding of how emotions and biases drive market behavior.

  1. Dynamic portfolio optimization
    Traditional portfolio management relies heavily on static models, but LLMs are enabling real-time optimization. These models integrate real-world events like geopolitical shifts or environmental disasters into risk and return assessments, helping investors pivot strategies in rapidly changing markets.

  1. RegTech and compliance transformation
    Financial regulations have a way of changing constantly – making compliance a monumental task. LLMs are transforming RegTech by reading and interpreting new regulations, automatically mapping them to internal compliance frameworks, and flagging potential areas of risk. This reduces the lag between regulation updates and corporate adaptation, enhancing compliance agility.

  1. Quantum finance and predictive analytics
    In cutting-edge financial research, LLMs are being integrated with quantum computing experiments to explore complex financial systems that exceed classical computing capabilities. These systems aim to refine predictive analytics, uncover hidden correlations, and simulate high-dimensional market behaviors with unprecedented accuracy.

  1. Tokenized asset markets
    As digital assets and tokenization grow, LLMs are central to understanding and navigating these emerging markets. They analyze white papers, smart contract codes, and market trends to assess the viability and risks of tokenized assets, supporting informed decision-making in decentralized finance (DeFi).

  1. Hyper-personalized financial ecosystems
    LLMs are powering platforms that create bespoke financial ecosystems for users, going beyond simple recommendations. These systems dynamically adjust to an individual’s evolving financial landscape, incorporating real-time market data, spending habits, and even life events to deliver hyper-personalized advice and product offerings.

  1. Climate risk assessment in investments
    LLMs are helping investors address one of the most pressing modern challenges: climate change. By analyzing environmental data, carbon disclosures, and sustainability reports, these models identify climate-related risks and opportunities, allowing for more resilient and responsible investment strategies.

These advancements are literally pushing the boundaries of what’s possible in finance, moving the sector from static, rule-based systems to dynamic, intelligent ecosystems that learn, adapt, and innovate in real time. The integration of LLMs is not just about staying competitive – it’s about redefining the very fabric of financial strategy.

Education

AI is reshaping education in ways that focus on real, tangible benefits for learners and educators alike. Instead of reimagining the classroom entirely, it is addressing long-standing gaps and enhancing how knowledge is delivered and absorbed.

Customized learning for every student

AI-powered tools tailor educational experiences to individual needs, adapting lessons based on how students respond. Whether it’s accelerating learning for gifted students or providing extra support for those who struggle, this ensures no one is left behind.

Overcoming resource gaps

With AI translating and localizing content into multiple languages, quality education is no longer confined to specific regions or demographics. This democratization of learning resources bridges gaps in access, particularly in underserved communities.

Smarter evaluations

AI doesn’t just grade – it interprets. By analyzing trends and patterns in a student’s answers, educators can gain deeper insights into learning behaviors, enabling interventions that address the root of challenges rather than just their symptoms.

Better teacher support

Virtual assistants now handle administrative tasks and provide real-time feedback during lessons, freeing teachers to focus on creativity and student interaction. This partnership between AI and educators enhances the human side of teaching, rather than replacing it.

Building future-ready skills

AI literacy is becoming a core part of education. From understanding how algorithms shape the digital world to hands-on experience with machine learning tools, students are being prepared for careers in AI-driven industries.

Coding

Using LLMs, developers can create code generation models. By classifying code parts in popular programming languages, they can train such a model according to a custom dataset and have it perform specialist tasks or functions in dev work.

E-commerce

It probably doesn’t come as a complete surprise that e-commerce businesses are increasingly leaning into AI. As the digital marketplace grows more crowded and consumer expectations rise, businesses are turning to advanced AI tools to keep pace. 

AI is not just about automation—it’s about delivering deeper insights and optimizing key areas of the shopping experience. Let’s examine how AI is transforming e-commerce through hyper-personalized shopping, dynamic pricing, and enhanced customer support.

Hyper-personalized shopping

AI has unlocked a new era of personalized experiences for online shoppers. By gathering and analyzing data from various sources—such as browsing habits, previous purchases, and even interactions with customer support—AI can suggest products that are specifically aligned with each customer’s preferences. 

This level of personalization isn’t just a nice-to-have feature anymore. Consumers want to feel like the shopping experience was made just for them, and AI makes that possible. Businesses that use AI to tailor product recommendations often see improved conversion rates, as shoppers are presented with items that genuinely interest them. This makes the shopping journey more seamless and customer-centric, driving both sales and brand loyalty.

Dynamic pricing

Staying competitive in e-commerce terms often means adjusting prices in response to market trends, competitor pricing, and demand shifts. AI is helping businesses do this in real time, enabling them to implement dynamic pricing strategies that automatically adjust prices based on various factors. This capability allows businesses to stay flexible, adjust pricing for maximum profitability, and make data-driven decisions. 

Whether responding to seasonal fluctuations or taking competitor moves into account, AI-powered pricing systems ensure that businesses remain responsive and efficient, without the need for constant manual intervention. For e-commerce businesses, this can mean the difference between being competitive or losing out to a stronger competitor.

Enhanced customer support

AI-powered customer service tools, such as chatbots and virtual assistants, are increasingly being used to handle everyday customer interactions. By automating common inquiries – such as order tracking, product details, or return policies – AI helps businesses deliver faster and more consistent support. This not only reduces wait times but also frees up human agents to focus on more complex issues. 

Additionally, AI systems learn from each interaction, continuously improving their ability to resolve customer inquiries with minimal input. As a result, the customer experience becomes more efficient, allowing businesses to scale their support without sacrificing quality. This improvement in customer service not only enhances customer satisfaction but also increases retention rates.

AI in e-commerce is about creating experiences that resonate with customers – an expectation that is growing by the day. By focusing on hyper-personalization, flexible pricing, and efficient support, AI is helping businesses meet the high expectations of today’s online shoppers while driving growth in the process.

Engineering

LLMs are making it easier to handle complex technical documentation and design processes in engineering. By training on vast datasets, LLMs can examine engineering specifications, product manuals, and blueprints with incredible precision. They help engineers to automatically identify discrepancies, suggesting improvements, and even generating technical solutions based on historical data. 

For example, in the design phase, LLMs can process vast amounts of data, suggesting optimization strategies for materials, cost-saving techniques, or energy-efficient systems. When looking at maintenance, LLMs can be used to predict potential equipment failures by analyzing manuals and sensor data, helping companies address issues before they escalate into costly breakdowns.

Robotics

In robotics, LLMs are transforming the way robots understand and interact with their environment. By processing vast amounts of text and sensor data, LLMs help robots improve their decision-making, adapt to new situations, and enhance their learning capabilities. In industrial settings, LLMs assist in automating tasks, such as parts identification or assembly line optimization, by interpreting both structured and unstructured data. These models also help in human-robot collaboration by interpreting and responding to verbal commands in natural language, making the interaction seamless. For autonomous robots, LLMs support mission planning by analyzing real-time data and generating responses that guide robots in navigation or task execution.

Architecture

Architects use LLMs to process complex legal documents and regulations, ensuring compliance with local and international standards. LLMs also support sustainable architecture by analyzing energy usage patterns and suggesting design modifications that reduce the building’s carbon footprint. By integrating these models, architectural firms can streamline the planning process, ensure greater accuracy, and deliver innovative solutions more efficiently.

Archaeology

LLMs are making waves in the field of archaeology by helping researchers process and interpret vast amounts of historical and cultural data. By training on archaeological records, LLMs help archaeologists quickly locate relevant information from excavation reports, artifacts, and historical texts. These models can even predict the potential location of undiscovered sites by analyzing geographical and historical patterns, assisting archaeologists in their exploration. Furthermore, LLMs aid in the cataloging of ancient languages, translating and interpreting texts that were previously difficult to decipher. This opens new doors for understanding lost civilizations and enhancing research efficiency.

Astrophysics

Yes – even astrophysics. LLMs are being used to decode the massive datasets generated by telescopes and space missions. These models are trained to recognize patterns in astronomical data, identifying new celestial bodies, classifying galaxies, and even predicting the behavior of cosmic phenomena. LLMs also assist in research by summarizing vast amounts of scientific literature and enabling astrophysicists to keep up with the rapidly expanding body of knowledge. For instance, LLMs are used to analyze light curves from distant stars to detect exoplanets or to process gravitational wave data, facilitating deeper insights into the universe’s most profound mysteries.

Climate and environmental modeling

LLMs play an increasingly important role in climate science by analyzing large datasets to predict weather patterns, track climate change, and simulate environmental impacts. These models are used to process data from satellites, weather stations, and climate sensors, helping scientists understand long-term trends and short-term fluctuations in the environment. In the context of policy and mitigation, LLMs help model the potential effects of various environmental regulations, offering governments and organizations data-driven insights into the best courses of action. LLMs also assist in modeling natural disasters, helping to predict events like wildfires, hurricanes, and floods, thus aiding in disaster management and response efforts.

Quantum computing design

Quantum computing is an inherently complex field, but LLMs are making it easier to design quantum algorithms and systems. By processing large datasets and understanding intricate quantum mechanics terminology, LLMs assist in the creation of new quantum computing protocols and error-correction techniques. 

They can analyze research papers, technical documents, and simulation results, providing scientists with insights that speed up the development of quantum software. LLMs are also helping to bridge the gap between quantum hardware engineers and software developers by automatically translating complex quantum programming tasks into human-readable language, streamlining collaboration, and accelerating progress in quantum computing.

How to tailor a large language model for your business

Even though data labeling is not required for pre-training, to handle a specific task well, language models need fine-tuning. If you want to develop your LLM, you will require high-quality labeled data. This calls for a certain amount of labeled data, which is most likely considerably smaller than the data originally employed to train the initial LLM.

Large language models perform well on generic tasks due to the fact that they are pre-trained on immense quantities of unlabeled text data, such as books, online commentaries, or massive document databases. To create successful applications that perform specific tasks, you require human-labeled data.

Aside from providing human-labeled data for the language model development process, crowdsourcing platforms such as Toloka enable their users to automate the fine-tuning of the models. This allows them to launch their AI application without the need to hire a team of experts, but rather outsourcing it. The LLMs are laborious in their development and upkeep, putting them out of reach for most companies. A perfect solution that makes LLMs more accessible for any enterprise is addressing a crowd of domain experts through partners like Toloka.

The role of people in LLM development

Humans aren’t quite out of the loop just yet. As previously mentioned, the development of large language models for specific uses requires accurately labelled data in order for the language model to generate accurate estimates. Computers cannot produce labels as fine and sophisticated as people can.

In order to align the model with expectations, human evaluation of the model's performance is crucial. The input data used for human labeling is consistent and derived from actual situations. 

From gathering and labeling the required data to ‘humanly’ assessing the model's quality and moderating its output, the Toloka platform allows human input into every step of the development of your LLM.

Article written by:

Toloka Team

Updated:

Jun 28, 2023

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What types of projects or tasks does Toloka typically handle?

What industries and use cases does Toloka focus on?

What is Toloka’s mission?

Where is Toloka located?

What is Toloka’s key area of expertise?

How long has Toloka been in the AI market?

How does Toloka ensure the quality and accuracy of the data collected?

How does Toloka source and manage its experts and AI tutors?

What types of projects or tasks does Toloka typically handle?

What industries and use cases does Toloka focus on?

What is Toloka’s mission?

Where is Toloka located?

What is Toloka’s key area of expertise?

How long has Toloka been in the AI market?

How does Toloka ensure the quality and accuracy of the data collected?

How does Toloka source and manage its experts and AI tutors?

What types of projects or tasks does Toloka typically handle?

What industries and use cases does Toloka focus on?