The research “Survey of different Large Language Model Architectures: Trends, Benchmarks, and Challenges,” conducted by Minghao Shao, Abdul Basit, and Ramesh Karri at New York University and New York University Abu Dhabi, analyzes the architectures of Large Language Models (LLMs) and their multimodal variations. It highlights the evolution from simple textual comprehension models to systems capable of managing heterogeneous inputs. The study underscores progress, structural complexity, and numerical results, providing critical insights into current techniques and upcoming developments in LLMs.
Large Language Models: From Architectural Foundations to Initial Experiments
The early phase of natural language processing (NLP) on computers was dominated by models that captured only a limited portion of textual complexity. Before the advent of Large Language Models, NLP systems relied on approaches such as Markov chains, bag-of-words models, or recurrent neural networks (RNNs, LSTMs), all sharing intrinsic limitations in maintaining long-term context. Although these solutions marked an important first step, they proved relatively inflexible in interpreting articulated, hypertextual texts with extensive cross-references. Their ability to generate coherent large-scale responses was modest, and the effectiveness of contextual understanding dwindled over longer textual sequences.
The qualitative leap occurred in 2017 with the publication of the Transformer model. The introduction of the attention mechanism broke the sequential constraint of recurrent networks: instead of processing words one by one, the Transformer could analyze all words in a sentence simultaneously, identifying which terms were most important for interpreting each individual token. This allowed the model to overcome the barrier of long-range dependencies in texts: a word at the beginning of a sequence could be related to a term much farther ahead, without relying on a slow, incremental update of an internal state, as in RNNs. The parallelization of attention, combined with large-scale training techniques, facilitated the exponential increase in model size.
The first Large Language Models that followed—such as the early versions of BERT and GPT—adopted the Transformer architecture and showed how adding new neural layers and increasing the number of parameters could lead to significant improvements on various NLP tasks. More accurate translations, more coherent summaries, increasingly precise question-answering, and more robust contextual comprehension emerged thanks to models with millions, then billions of parameters. LLMs came to encompass distributed knowledge extracted from entire encyclopedias, books, web pages, and technical documents.
It should be noted that this transition was not without obstacles. Training models with billions of parameters required enormous computational resources: highly performant GPUs, distributed computing clusters, parallelization techniques such as data parallelism and pipeline parallelism, as well as custom optimization strategies. Computational costs and investments in research and development became substantial. Moreover, as models grew, concerns about access to suitable datasets, data quality, bias, and the ability to evaluate performance using reliable benchmarks increased.
Despite these costs and complexities, the shift to Transformer architectures represented a turning point: on the one hand, it made possible the processing of much longer textual contexts; on the other, it rendered models more adaptable to different domains, from literary text to technical documentation, from natural languages to programming languages. The ability to understand the semantic structure of text was no longer an insurmountable theoretical limit. During this historical phase, NLP research underwent a drastic acceleration: the advent of LLMs marked the transition from simple neural networks designed for specific tasks to highly versatile base models capable of addressing a wide array of linguistic challenges with simple fine-tuning or suitable prompt engineering techniques.
Parameters, Data, and Ever-Growing Dimensions
The rapid growth in the number of parameters contained in a Large Language Model has profound implications for capability, performance, and operational complexity. In the early years of applying neural networks to language, models ranged from a few million parameters to a few hundred million—already ambitious results at the time. Today, the bar has risen to models boasting tens or hundreds of billions, and in some cases even trillions of parameters are hypothesized. This quantitative escalation is not merely an engineering feat; it directly affects the quality and variety of the linguistic competencies acquired. Such a vast model can capture subtle semantic nuances, narrative structures, and even styles or idiolects, drawing upon the knowledge distributed across immense textual collections.
However, increasing the number of parameters also raises the requirements for computational resources and optimization strategies. Training a large-scale LLM is not comparable to running a few epochs on a single GPU: it involves orchestrating complex computational pipelines on clusters of hundreds of processing units, synchronizing gradients, and managing efficient workload distribution. Traditional training techniques are no longer sufficient: methods like activation checkpointing, which stores only certain intermediate information to reduce memory usage, or quantization, which converts model weights from 32-bit formats into more compact representations (for example, 8 or 16 bits), aim to contain computational costs and enable the convergence of massive models.
In parallel, the availability of massive-scale datasets has been crucial. During training, the model draws on corpora made up of billions of tokens from heterogeneous sources: books, scientific articles, social media posts, web pages, and code repositories. This enormous variety of materials enhances the model’s robustness and versatility, allowing it to fluidly switch from a literary translation to analyzing a specialized text, from answering a general knowledge question to solving a logic problem. The semantic richness embedded in the model is such that, given the right prompts, it can generate coherent content in multiple languages, tackle complex problems, and demonstrate basic deductive abilities.
Despite the power of these giants of computational linguistics, researchers face a delicate balance. Every additional increase in parameters leads to exponential growth in training time, hardware costs, and energy consumption. Questions arise about environmental impact, economic sustainability, and the responsibility of those developing these technologies. For this reason, new research lines have emerged focused on model compression, the search for more efficient architectures, or strategies to reuse acquired representations. Extraordinary linguistic skills are not enough: an LLM must be trained and managed sustainably.
Applications of Large Language Models and Concrete Examples
The ability of LLMs to handle a multitude of operational scenarios, fluidly shifting from one domain to another, is already transforming various industrial and professional sectors. The most immediate example is text generation, where a model trained on a wide range of sources can compose formal business emails, draft informative articles, propose effective headlines for marketing content, or create original narratives, adjusting style and tone to user preferences. But beyond writing, applications have extended well beyond simple text composition.
In customer support, a suitably configured LLM can serve as a virtual agent capable of conducting a structured conversation with the user, recognizing the problem and proposing targeted solutions. Once limited to predefined responses, this ability now extends to personalizing the interaction: the assistant can understand the user’s specific situation, provide technical details, recall the entire context of a previous chat, and handle increasingly complex questions. From a business perspective, this means reducing training times for support staff and guaranteeing a 24/7 service with consistently high-quality responses.
Another tangible case is specialized consulting. Imagine an executive who needs a concise and up-to-date market analysis. By providing the model with a series of internal reports and external sources, the LLM can extract key insights, identify emerging trends, compare competitors’ strategies, and point out potential risks, all presented in a clear and coherent manner. In financial contexts, the LLM can read balance sheets, extract salient data, and answer questions like “What were the main growth drivers in the last six months?” or “How does operating margin vary by sector?” It is not merely generating text, but performing a semantic bridge between raw data, specialized reports, and the user’s specific queries.
In R&D departments, the LLM can serve as an assistant in code design or technical documentation writing. A team of software engineers can receive suggestions on how to optimize an algorithm, which libraries to use for a particular function, or even how to migrate part of the code to a new framework. These capabilities are useful not only for experts seeking faster implementation ideas but also for novices who want to learn through examples and explanations provided by the model itself.
In the creative realm, linguistic models can help draft scripts, create briefs for advertising campaigns, write scripts for podcasts or online videos. Their ability to understand context allows them to generate drafts already coherent with the topic, desired style, and set communication goals. This enables creative professionals to focus more on the concept and less on execution.
The integration with analytics and Business Intelligence tools should not be underestimated. An LLM can act as a “conversational interface” to data: instead of querying a database with complex SQL, a manager can pose questions in natural language—“Show me the sales distribution by geographic area in the last quarter”—and receive immediate answers, possibly with on-the-fly generated tables and charts.
Finally, in education and corporate learning, an LLM can serve as a virtual tutor, clarifying difficult concepts, suggesting additional exercises, evaluating the correctness of student responses, or offering tips to improve understanding of a topic. Learning thus becomes interactive, personalized, and continuous, without the need for constant human intervention.
Beyond Text: Multimodal Models
The evolution toward Multimodal Large Language Models (MLLMs) represents a crucial turning point in the history of automated content processing. While early LLMs were confined to the textual dimension, MLLMs combine heterogeneous inputs—images, videos, audio, and potentially even sensory data—to offer an integrated understanding of a scene or context. This capability is not a simple quantitative extension: it moves from interpreting sequences of tokens to comprehending a richer, more complex narrative in which words, sounds, and images merge into a unified semantic fabric.
From a technical perspective, integrating different modalities requires specialized architectures. It is not enough to train a visual model (such as a CNN or a Vision Transformer) and a textual model (like an LLM) separately: alignment and fusion mechanisms of signals must be planned. Some approaches use common latent spaces, where text, images, and audio are mapped into comparable numerical representations, enabling the model to “reason” about the content. Others adopt two-stage architectures, in which a visual or audio backbone extracts semantic features, and a linguistic module, informed by these features, produces coherent textual descriptions or generates contextual responses.
The results obtained by pioneering models indicate that MLLMs can accurately describe complex scenes, identifying objects, recognizing actions, extracting situational contexts, and formulating sensible narratives. For example, a multimodal system could interpret a drone video flying over farmland: not only identifying the presence of crops, buildings, and roads, but also explaining the ongoing action (an inspection of the fields) and providing a coherent summary. Similarly, it could listen to an audio clip containing voices and background noise, detecting people conversing, a moving vehicle, or music, and integrate this information into a textual description that explains the scene comprehensibly.
The commercial and industrial applications of MLLMs are potentially immense. In e-commerce, an MLLM can analyze product images, technical data sheets, and customers’ voice reviews, then synthesize detailed descriptions or suggest targeted marketing strategies for different user segments. In market analysis, integrating images (like graphs and infographics), text (reports, news articles), and audio/video (interviews, conferences) allows the identification of trends, emerging patterns, and hidden correlations among heterogeneous information sources. In the creative field, an MLLM can support multimedia production: an author can provide an initial storyboard, reference images, and an oral description of a scene, obtaining from the model ideas for dialogue, settings, and coherent narrative dynamics.
Robotics also benefits from this multimodal convergence. A robot equipped with cameras and microphones can transmit raw data (images and ambient sounds) to an MLLM which, interpreting them, provides the robot with textual and logical instructions on how to proceed: for example, when faced with a partially unknown environment, the robot might receive suggestions on which object to manipulate, which direction to follow, or how to react to an acoustic signal. This synergy between the physical world and multimodal interpretive power lays the foundation for more flexible and “intelligent” autonomous systems in the colloquial sense.
It must be emphasized that we are still at the dawn of MLLMs’ full maturity. The promises are significant, but so are the technical and conceptual challenges: from the need for balanced multimodal datasets and careful annotation, to reducing cultural and perceptual biases, and up to scaling the computational power required to train and maintain these systems. Nevertheless, the progress already made indicates a clear direction: future models will not be limited to “reading” texts, but will perceive images, sounds, videos, and potentially other signals, becoming universal assistants capable of understanding the world in its full sensory and cognitive complexity.
Current Challenges, Future Perspectives, and Evolutions
One of the major challenges in the field of LLMs concerns the ability to process increasingly large and complex textual contexts. Today, if a model can maintain a context of a few thousand tokens, the future goal is to handle extended documents, entire books, or even knowledge databases. Achieving this is not as simple as blindly increasing model size: more efficient architectures and attention to aspects such as long-term memory, dynamic text segmentation, and internal indexing mechanisms inspired by advanced data structures are needed. The difficulty lies in making these solutions scalable and computationally sustainable.
Data quality remains crucial. If the LLM is fed inaccurate, outdated, or poor-quality information, the results and recommendations it provides will be equally unreliable. Hence the need for ongoing dataset curation, with data cleaning, deduplication, and advanced filtering practices to remove toxic content, propaganda, or misinformation. Moreover, linguistic bias inevitably reflects the prejudices present in the training data. Aligning models with ethical and inclusive principles requires interdisciplinary efforts: linguists, sociologists, ethicists, and AI engineers must collaborate to define criteria and metrics capable of measuring the model’s fairness and neutrality, preventing discriminatory tendencies.
On the efficiency front, energy costs and the ecological footprint of training gigantic models cannot be ignored. With increasingly powerful GPUs, energy-intensive computing centers, and the need to perform multiple training iterations, the environmental impact is not negligible. The search for more sustainable training methods, the reuse of pre-trained models, the use of pruning techniques (selective removal of less relevant parameters) and quantization (reducing numerical weight precision) are some approaches to contain costs without sacrificing performance. In parallel, the emergence of sparse or hybrid architectures that activate only certain parts of the model based on input promises to reduce computational load.
The variety of competing approaches—from enhancing individual models to specialized solutions, from multimodal LLMs to hybrid ones integrating symbols and logic—reflects an increasingly diversified scenario. This competition is not only technical but also industrial: large companies, startups, and research consortia are racing to develop more powerful, faster, and cheaper models. In this context, the lack of shared standards and uniform evaluation metrics is an obstacle. To compare performance and reliability, credible, up-to-date benchmarks recognized by the scientific community and the business world are needed. In this sense, joint efforts such as defining new test sets, ethical standards, and safety protocols become fundamental.
Looking to the future, the goal is no longer just the raw power of LLMs, but their ability to integrate into more complex ecosystems: systems that combine linguistic models with structured knowledge databases, software agents that leverage the LLM to interact with the real world, and conversational interfaces that make data accessible to non-technical users. The coming years will see more elastic LLMs, able to dynamically adapt to available resources and learn new tasks without starting from scratch. A future emerges in which the balance between power, efficiency, reliability, and sustainability becomes the true measure of success, paving the way for linguistic models fully integrated into everyday practices—in education, scientific research, business, political analysis—in an ethical, responsible, and lasting manner.
Conclusions
The current results of Large Language Models and their multimodal variants underscore a phase in which computational power and data availability have enabled capabilities once unimaginable. However, reflecting on the overall picture and comparing what has emerged with technologies already on the market, it becomes clear that one cannot rely solely on scaling parameters and dataset size. Alternative strategies—such as using more specialized models, compression formats, or techniques tailored to specific tasks—may prove more sustainable and scalable for businesses and organizations. Performance stability, the ability to adapt to specific domains, and the management of hybrid contexts will be key elements for those looking to harness these technologies strategically, avoiding the limits of overly generic approaches. This suggests a scenario in which new standards, shared evaluation metrics, and integrated approaches become essential, outlining a future in which model power is accompanied by a more reasoned and sustainable vision.
Source: https://arxiv.org/abs/2412.03220
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