Future Trends in Large Language Models¶
By training massive neural networks on vast datasets, LLMs have acquired remarkable capabilities to understand and generate human-like text across an expansive range of domains and applications.
Yet even as LLMs revolutionize fields like question answering, writing assistance, conversational AI and content creation, we are just scratching the surface of their full potential. Driven by continued research into more capable architectures and training paradigms, improved computational scale, and novel application frontiers, the scope and impact of LLMs will continue to grow rapidly in the coming years. This chapter explores some of the most promising and consequential future directions shaping the next evolution of large language model capabilities. We cover emerging areas like multimodal integration, model compression techniques for efficient deployment, streamlined fine-tuning lifecycles, the rise of intelligent agent-based systems enabled by LLM function calling abilities, and the challenges around robustly benchmarking model performance in real production environments.
As LLMs transition from research novelties into critical pillars of our increasingly AI-driven world, responsibly developing and harnessing their future potential will be crucial for driving technological progress while mitigating risks. This chapter aims to provide a roadmap for the key research areas, engineering hurdles and paradigm shifts on the horizon for this powerful AI technology. Note that contrary to previous chapter, the topics covered here are open to evolution. To try to cover more ground we will recommend further readings in each of the section and we invite those interested to deepen their understanding to take these references as entry points.
Multimodal LLMs¶
One of the most exciting frontiers in large language models is their expansion into multimodal domains, integrating understanding and generation across different data modalities like text, images, speech, and video. While current state-of-the-art LLMs operate primarily on text input and output, multimodal models aim to combine linguistic knowledge with perception of other modalities like vision and audio.
The potential applications of multimodal LLMs are vast, spanning content creation, information extraction, knowledge synthesis, and creative support tools. Imagine an AI assistant that can analyze images and documents to gather insights, then generate a multimedia report combining written analysis with data visualizations and graphics. Or an AI creative collaborator that can understand a text story premise, visualize characters and scenes, and storyboard potential shots seamlessly bridging language and visuals.
In content understanding applications, multimodal LLMs could enable major advances in interpreting websites, document archives, videos, and other multimedia data sources in a unified way. The learned multimodal representations could power more comprehensive search, data mining, knowledge extraction and synthesis capabilities far beyond what is possible with language models alone.
On the content generation side, LLMs have already shown impressive language capabilities in areas like creative writing, but true compositional multimodal generation could make entirely new types of AI-supported creative workflows possible. From automatic storyboarding to AI-assisted graphic design, multimodal LLMs could become potent co-creative tools, enhancing and augmenting human creatives rather than replacing them.
However, despite this immense potential, there are significant challenges in developing robust multimodal LLM systems. The variability and complexity of visual and audio data modalities is astronomically higher than text, driving extreme computational requirements for modeling at scale. Integrating vastly different data representations like language tokens and image pixels into unified dense representations is a major unsolved problem.
There are also significant data challenges around collecting and annotating high-quality aligned multimodal datasets with sufficient scale, coverage and fidelity for representation learning. Moreover, responsible development of multimodal models must carefully navigate concerns around disinformation, ethical issues like consent and bias, IP protection, and many other thorny challenges at the intersection of AI and multimedia data.
Despite these hurdles, the frontiers of multimodal AI represent an enormously promising, high-impact and societally beneficial domain as we work to develop more general artificial intelligence systems that can engage with the full richness and complexity of human experience and expression. As LLMs continue to rapidly evolve, their transition into robust multimodal perceiving and generating systems may catalyze revolutionary new applications and workflows for enhancing information access, knowledge work, content creation and more.
Model Compression for Production Deployment¶
While the capabilities of large language models continue to grow, so do their computational requirements and model sizes, which can easily reach into the billions of parameters. Deploying such massive models in production environments like web services, mobile apps or edge devices raises major engineering challenges around model size, latency, cost and hardware utilization.
Model compression techniques aim to reduce the memory footprint and accelerate inference of large models like LLMs, enabling more efficient deployment. Classic approaches to compressing large neural models like LLMs involve techniques for sparsifying the dense parameter matrices and reducing the precision of numerical representations. One widely used method is pruning, which involves identifying and removing weights in the model that have relatively small magnitudes and thus contribute less to the overall output. By zeroing out these smaller weights, the model becomes more sparse, requiring less memory for storage. However, pruning too aggressively can degrade the model's performance, so there is a delicate balance to strike.
Another compression approach is quantization, which aims to represent the high-precision floating-point weights and activations in the model using lower-precision numeric formats like 8-bit or 4-bit integers. By storing parameters with fewer bits, the model's memory footprint can be drastically reduced with reasonable losses in accuracy. Quantization is particularly effective for deployment to resource-constrained hardware like mobile devices. That said, supporting efficient computation on low-precision data often requires specialized software and hardware acceleration.
The specific quantization approach, pruning schedule, or distillation process must be tailored to the particular model architecture, training data, and target deployment environment to properly navigate the trade-offs between compression ratio, accuracy, computational efficiency, and other factors. Developing compressed models often requires expertise in model compression research as well as deep understanding of prospective downstream use cases.
A third major technique is knowledge distillation, inspired by the teacher-student framework in machine learning. Here, a large pre-trained model (the "teacher") is used to supervise the training of a smaller "student" model which learns to mimic the teacher's outputs or model representations. If done carefully, the student can achieve comparable performance to the teacher despite having far fewer parameters. This compression approach is especially relevant for large language models, where the base "teacher" may have billions of parameters encoding broad general knowledge.
A particularly promising technique for compressing large language models is LoRA (Low-Rank Adaptation), inspired by knowledge distillation. LoRA trains a small number of rank-decomposed weight matrices to quickly adapt a pre-trained "teacher" LLM to a new task or dataset. This allows fine-tuning the model with far fewer trainable parameters while still benefiting from the general knowledge encoded in the teacher.
Evaluating compression techniques involves analyzing multiple trade-offs. While quantization and pruning reduce model size, they can also degrade the model's general performance. LoRA trades off task-specific performance to retain the base model's breadth of knowledge. There are also engineering challenges around optimizing compressed models for efficient tensor operations on target hardware like GPUs and TPUs.
Additionally, smaller models may need to operate at higher inference batch sizes to saturate accelerators and maximize utilization, introducing latency concerns. There are emerging techniques like model parallelism to mitigate this by distributing the workload for a single request across multiple devices.
The choice of compression approach depends heavily on the production use case. An on-device mobile app prioritizes ultra-low latency, favoring heavy quantization even if it sacrifices some accuracy. A web API may opt for higher throughput over latency by batching many requests together on efficient data center infrastructure.
Finally, model compression is tightly coupled with the production deployment strategy. Many cloud providers now offer optimized hosting services for accelerated LLM inference, leveraging custom silicon and seamless containerization. Open source tools like HuggingFace allow easily loading quantized PyTorch or TensorFlow models into serverless APIs.
For on-premises deployment, options span installing optimized inference engines like NVIDIA Triton on GPU clusters to lower-cost CPU-only setups. Multi-node model parallelism further distributes the inference workload across machines, though this introduces greater serving complexity.
In summary, while capable, large language models also have an immense appetite for compute resources. Engineering compressed models and efficient systems for serving them at scale will be crucial to democratizing LLM applications and fully realizing their potential across a wide range of production environments.
Model Fine-Tuning Lifecycle¶
While large language models trained on broad data exhibit remarkable general capabilities, customizing and optimizing them for specific domains and tasks through fine-tuning is crucial for unlocking their full potential in real-world applications.
Fine-tuning approaches¶
There are several predominant approaches to fine-tuning LLMs. One common method is continued pre-training, which involves further training the model's full weights on domain-specific data to specialize its knowledge. For example, an LLM could be fine-tuned on a large corpus of enterprise texts to enhance its performance on corporate applications. Another popular technique is prompt-based fine-tuning, where only a small subset of model weights corresponding to newly initialized prompts or prefixes are trained, while the base LLM weights remain frozen. This parameter-efficient approach allows quickly adapting the model to new data distributions or tasks with far fewer training examples.
Few-shot learning methods that expose the LLM to just a few examples of the target task have also shown great promise. For instance, the model can be prompted with a few question-answer pairs to demonstrate the desired question-answering behavior. Combined with clever prompt engineering, these approaches can achieve strong performance with minimal task-specific data.
One of the key challenges in fine-tuning very large pre-trained language models is the risk of catastrophic forgetting - a phenomenon where the model severely loses its previously learned general knowledge as it is updated on task-specific data during fine-tuning. This can happen because the extremely high-dimensional weight space makes it difficult for the model to preserve and consolidate its broad pre-training signal while adapting to new inputs and distributions. As the model's weights get tuned towards a particular task or domain, they can become over-specialized and overwrite important general information encoded across the model during the initial self-supervised pre-training phase on diverse data. This can lead to degraded performance on other tasks the model was originally competent at, diminishing its general reasoning and language understanding capabilities.
Fine tuned models in production¶
Managing production fine-tuning workflows adds another layer of complexity. LLMs may need to be continuously updated and fine-tuned on evolving real-world data to maintain high performance over time. This requires robust processes for data collection, validation, model retraining and deployment with mechanisms to monitor for drift or degradation.
Some production systems may need to dynamically multi-task by simultaneously fine-tuning on evolving data from multiple domains. Determining optimal retraining schedules, data prioritization strategies, and allocating finite compute budgets while mitigating interference between tasks requires careful systems design.
Additionally, there are open research questions around better leveraging cheaper unlabeled data through semi-supervised approaches, rapidly adapting to new tasks via meta-learning, and the promise of unified models that can flexibly apply broad general knowledge to any task or domain with lightweight tuning.
Overall, the fine-tuning lifecycle poses both interesting research challenges and substantial engineering efforts as the community works to develop best practices for responsibly and efficiently customizing these powerful but ravenous models for diverse real-world use cases.
Function Calling for Conversational Agent¶
One of the most exciting frontiers in expanding the capabilities of large language models is providing mechanisms for them to initiate function calls - allowing the LLM to invoke external programs, APIs and services or trigger customized internal functionality beyond just generating text output.
Function call¶
At its core, the function calling paradigm equips an LLM with a simple interface to execute arbitrary code by generating a text string with a special syntax representing the desired function call. For example, the model could output:
result = fetch_weather_data("Toulouse")
Which would trigger a corresponding API call to retrieve weather information for Toulouse, with the results streamed back into the LLM's context to condition its subsequent outputs.
While straightforward in principle, this simple interface unlocks powerful new capabilities. LLMs could automate workflows by autonomously calling cloud services, databases, analytic engines or knowledge retrieval APIs based on the context of a dialog or instruction. They could even trigger real-world actions like sending notifications or operating cyber-physical systems. The ability of large language models to initiate function calls is a direct consequence of how they are trained on vast datasets that intermix natural language text with programming languages and code samples. During pre-training, the model learns dense representations that can map between the distributional semantics of human-readable text and the structured syntax of programming languages.
This enables LLMs to not just generate fluent text, but also synthesize executable code snippets or structured representations that can interface with programs, APIs, and services. By combining natural language understanding with code generation capabilities in a single unified model, LLMs gain the potential to serve as agents that can interpret conversational instructions, reason about them, and trigger appropriate computational actions or function calls to carry out those instructions.
Conversational agent with diverse capabilities¶
On a deeper level, function calling enables an "agent" paradigm wherein the LLM functions as a rational decision-making engine that can take actions and receive observations about the world in a recursive, cyclic fashion. This opens the door to more general AI systems that can engage in multi-step reasoning, long-term planning, and even learn sophisticated strategies by exploring the environment and experiencing action consequences.
For example, an LLM agent could be embedded in a virtual or robotic environment and use function calls to move, sense, and manipulate objects with the goal of learning complex skills like tool use, spatial reasoning or game-playing in a grounded end-to-end fashion. In workplace environments, an agent LLM assistant could execute a multi-step series of productivity application operations to complete complex workflows as easily as following instructions.
This decision-making aspect of selectively invoking functions based on dialog context is what truly unlocks language models as functional agents rather than just elaborate text generators. Given a prompt like "What's the weather forecast for this weekend in San Francisco?", the LLM can recognize the implicit intent, determine that an API call is needed to retrieve the relevant data, synthesize the correct code representation (e.g. fetch_weather_data("Toulouse", start_date="2024-04-03", end_date="2024-04-10")) and execute that function call.
The model can then parse the returned data, condition its response generation on that information, and provide a contextually relevant answer like "The forecast for this weekend in Toulouse shows sunny skies on Friday with a high of 16°C, and a 30% chance of rain on Saturday with a high of 18°C." All within the seamless flow of a conversational interaction. This tight integration of language understanding, reasoning, dynamic decision-making about how to act (e.g. retrieve knowledge, execute a query, trigger an API call or other service), and generating an enriched response informed by those actions is what characterizes LLMs as emergent task-agnostic agents. Their multi-modal training allows them to fluidly translate between modalities like text and code.
As language models' function calling capabilities increase in flexibility and get integrated with more robust execution environments, we'll see them rapidly evolve into more capable general-purpose AI assistants able to orchestrate complex sequences of actions and queries to accomplish multi-step tasks based on open-ended natural language instructions. This agnostic agent-like behavior, grounded in both language and code understanding, has transformative potential when applied to domains like workflow automation, open-ended question-answering, data analysis, tutoring systems, and beyond.
Risks and limits of agent paradigm¶
While tremendously powerful, the function calling paradigm also raises significant security and safety considerations that must be carefully navigated. A vulnerability or bug in the LLM could potentially cause it to issue unexpected sequences of function calls bypassing normal security checks. There are also open questions around how to constrain LLM outputs to only represent valid function calls, avoid infinite loops, and respect resource utilization limits.
Additionally, safety measures like guiding AI principles and debate protocols must be integrated to ensure the LLM cannot abuse its capabilities in unintended ways that cause harm. There could also be challenging transparency issues if the model's reasoning behind function call decisions is not properly interpretable.
The potential limitations of adapting language models designed for next-token prediction into capable sequential decision-making agents via the function calling paradigm is still a crucial research question today.
At their core, language models are optimized for distributional language modeling - learning to predict the most likely next token given the previous sequence of tokens. This training objective does not inherently instill the type of multi-step reasoning, long-term planning, or explicit decision-making capabilities required for an agent to execute complex sequences of actions and function calls over the course of a lengthy conversation. While large language models do exhibit interesting emergent capabilities in coherently tracking and updating context over long sequences, it's unclear if this behavior reflects true recursive reasoning or a more shallow pattern matching based on the sequential examples present in the training data. There are legitimate concerns that their performance could degrade when extended to decision trajectories far outside the distributions they were trained on.
While LLMs show exciting emergent potential as conversational agents, realizing their full promise in deployable sequential decision-making systems will likely require architectural innovations. Potential directions include incorporating explicit memory modules, enforcing decision consistency through structured reasoning components, adversarial training to reinforce robust behavior, or jointly modeling language and decision trajectories during pre-training.
Alternatively, the function calling paradigm with LLMs could serve as a strong base component that gets integrated into larger modular architectures designed specifically for multi-step decision-making - leveraging the LLM's multi-modal capabilities while orchestrating its outputs through more specialized decision, memory, and planning modules. Rigorously evaluating LLMs' performance as conversational agents across metrics like decision consistency, commitment retention, query strategy, and others will be crucial for understanding their current limitations. This can help define the path towards safely deploying them in environments where sequential decision-making is truly critical. The practical usability gap between their strong apparent capabilities and the complexities of real-world robust decision workflows remains an important area for research and responsible development.
That said, the function calling paradigm represents a pivotal shift towards grounded, embodied AI systems capable of flexibly composing advanced multi-step behaviors in the open-ended real world environment. As this research direction advances, the scope of LLM agents is likely to rapidly expand across application domains from productivity automation to conversational AI assistants to robotics and beyond.
Benchmarking LLMs in Real Production Settings¶
Academic evaluation approach: a solid foundation¶
While academic benchmarks on curated datasets have indeed been instrumental in pushing the boundaries of what large language models can achieve, it's important to recognize their primary role as research tools aimed at advancing our fundamental understanding of these models' capabilities, strengths and limitations.
In an academic setting, narrowly scoped benchmarks that isolate specific skills like question-answering or summarization serve as crucial probing grounds. They allow researchers to rigorously evaluate new modeling approaches, training strategies, architectural innovations and other novel techniques across well-defined tasks in controlled environments. The idealized nature of these benchmarks enables careful measurement and analysis that yields insights into the core reasoning abilities of LLMs. For example, benchmarks probing logical reasoning, multi-step arithmetic processing, temporal understanding and other cognitive capabilities have been invaluable in exposing innate strengths but also critical failure modes of existing language models. This guides future research into architectural additions like explicit memory modules, neuro-symbolic reasoning components and other inductive biases aimed at addressing these shortcomings.
Moreover, many academic benchmarks incorporate adversarial test cases specifically designed to assess robustness and failure modes, enable rich analysis of errors, and quantify uncertainty - all critical for responsible development of safe and reliable AI systems. The idealized nature facilitates this kind of rigorous probing.
However, the very characteristics that make academic benchmarks such powerful research tools can also become limitations when they are stretched beyond their intended use cases and treated as catchall evaluation suites for assessing production-readiness. Their narrowly operationalized tasks and idealized data distributions often fail to capture the messiness and multi-modal heterogeneity inherent in real-world applications.
For instance, while an LLM may achieve human parity on a question-answering benchmark, production question-answering systems need to handle ambiguous queries, multi-turn dialog contexts, extracting information across diverse data modalities, resolving inconsistencies through external knowledge grounding, and generating rich responses tailored to the user's persona and intent. These complexities are often abstracted away in benchmarks. There is also a risk of Goodhart's law where optimizing purely for narrow benchmark metrics like ROUGE or accuracy scores can lead to degenerate behavior and shortcut solutions that don't generalize to true open-ended environments. Model updates that improve benchmark performance could potentially degrade capabilities critical for production experiences.
While invaluable research tools, academic benchmarks must be understood within their limited scope and not treated as representative proxies for evaluating overall production-readiness. Supplementing them with living test suites derived from real application data, production telemetry, and rigorous human evaluation remains essential for holistic assessment as LLMs get deployed in safety-critical, user-facing systems. Benchmarking itself is a key area for future innovation.
Evaluating model performance in real-world¶
In contrast, production environments for LLM applications involve operating on highly heterogeneous data distributions across diverse and open-ended use cases. An LLM virtual assistant may need to handle everything from creative writing tasks to analyzing legalese to engaging in multi-turn dialogs incorporating external knowledge retrieval - all within the same conversational session.
There are also major distinctions between clean, well-formatted benchmark data versus the noisy, messy inputs encountered from real users including typos, embedded images/links, code snippets, and domain-specific jargon. Benchmark settings make implicit simplifying assumptions that get violated in production like coherence of input, adherence to instructions, or requiring only text-based responses. Many prominent LLM benchmarks evaluate just one static snapshot without accounting for the model lifecycle and update processes critical in real deployments. An assistant constantly being fine-tuned on new data may exhibit drastically different performance characteristics over time compared to inference on a single frozen model. Managing drift, catastrophic forgetting, and maintaining stable performance in evolving systems is an immense challenge.
Furthermore, many benchmarks focus narrowly on maximizing metrics like accuracy or ROUGE scores which don't always align with user experience and satisfaction in production. Conversational assistants need to balance factors like personality, empathy, semantic coherence, response specificity, factual consistency and more. Techniques like HBLFU may help but qualitative human evaluation remains indispensable.
That said, benchmarking in simulated production-like environments is an area of intense research focus. Techniques like RealityPrompts help create more naturalistic evaluation environments accounting for openendedness, multi-modality, and compounding task complexity. As LLMs get deployed more widely, continuous monitoring of user feedback signals, careful A/B testing, and adaptive model updating strategies will be crucial to maintaining high-quality user experiences.
Benchmarks have proven immensely useful. Yet, we are still in the relatively early stages of developing comprehensive and representative suites for holistically evaluating the true production-readiness and real-world safety of LLMs across the vast scope of potential applications. Closing this gap is vital to responsibly realizing their full transformative potential.
Closing words¶
The future trajectory of LLMs points towards increasingly capable, generalized and robust AI systems that can understand, reason, and take actions to assist humans across an open-ended range of real-world domains and modalities. From creative virtual assistants to multimodal knowledge synthesizers to agent-based workflow automation, LLMs have the potential to profoundly enhance information access, productivity, content creation and so much more.
However, realizing this immense potential will require overcoming substantial research obstacles around robustness, safety and scalable deployment. There are also broader societal challenges in governing the development and uses of LLMs to align with human ethics and mitigate risks like disinformation or existential threats from advanced AI systems. Ultimately, large language models represent just one facet, albeit among the most advanced and visible, of the rapidly evolving field of artificial intelligence. Their next evolution is likely to involve deeper integration with other AI technologies spanning robotics, multimodal perception, automated reasoning and domains like biotechnology and material science. As various strands of AI progress from narrow applications towards more general intelligence, the impacts on fields as diverse as science, technology, healthcare, governance and creative arts could be truly transformative.
The path ahead is uncharted and carries great responsibilities, but also incredible opportunities to push the boundaries of knowledge and intelligent problem-solving capabilities in service of human flourishing. With focused research, responsible development practices and sustained innovation, the future of AI embodied by large language models stands to fundamentally enhance our understanding of intelligence itself.