Notes:
Text generation is a broad term that refers to the automated creation of any sequence of text, often driven by statistical models, neural networks, or other algorithms, without necessarily considering linguistic structure, communicative intent, or coherence beyond the immediate sequence of words. It can include outputs like random word strings, autocomplete suggestions, or machine-written paragraphs, and is not always designed to mimic the complexity of human language. Natural language generation (NLG), on the other hand, is a specific subfield of artificial intelligence and computational linguistics concerned with generating coherent, contextually appropriate, and human-like language from structured or non-linguistic data. Unlike generic text generation, NLG emphasizes communicative goals, linguistic planning, and readability, often involving stages such as content determination, structuring, aggregation, lexical choice, and realization. In short, all NLG is text generation, but not all text generation qualifies as NLG, since NLG is purposefully designed to produce meaningful, natural, and context-aware human language.
See also:
LLM Evolution Timeline | Sequence-to-Sequence (seq2seq) & Chatbots | Text Classification & Chatbots | Text Graphs & Natural Language
[Aug 2025]
Text generation powered the shift from scripted chatbots to adaptive pretrained models
Before the breakthroughs of 2018–2019, text generation and chatbots were largely defined by sequence-to-sequence architectures, statistical models, and rule-based systems. These approaches could generate replies but often produced shallow, repetitive, or incoherent responses, struggling to maintain context across multiple turns. Chatbots in this earlier period were mainly deployed in constrained domains such as customer service or FAQ-style interactions, where scripted flows or retrieval-based methods could keep conversations manageable. Research was focused on improving diversity through techniques like reinforcement learning and attention mechanisms, but the absence of large-scale pretrained models meant chatbots lacked the fluency, adaptability, and generalization that came later. In essence, this stage set the stage for the revolution to come, as the limitations of handcrafted and narrowly trained systems made clear the need for more powerful, generalizable methods.
Between 2018 and 2019, the story of text generation and chatbots reads as a moment of transition where the field began moving decisively away from simple sequence-to-sequence responses and into the era of pretrained language models. Researchers, energized by breakthroughs such as GPT, BERT, and ELMo, saw a new path forward for conversation systems. They began experimenting with ways to harness these general-purpose models for dialogue, aiming to create more fluent, varied, and contextually aware responses. The research of this time captures a period of enthusiasm mixed with frustration, as fluent text generation became achievable but still fell short of producing genuinely useful conversational partners.
In these years, chatbots became a central proving ground for transfer learning. Studies show a kind of “gold rush” to adapt pretrained models for conversational purposes, with researchers seeking to control style, inject emotion, and ground responses in context or external knowledge. While GPT-2 dazzled with its ability to generate long, coherent passages, many papers revealed how fragile chatbots remained: they repeated themselves, lost track of context, and struggled with personality or emotional nuance. The research community was simultaneously captivated by the generative possibilities and aware that bridging the gap to real, sustained dialogue remained unresolved.
What emerges is a picture of infrastructure building. Researchers were not yet deploying polished conversational agents at scale but were laying the technical and methodological groundwork. They were testing new objectives to encourage diversity without sacrificing coherence, introducing adversarial and variational training methods, and exploring how pretrained encoders could strengthen understanding in dialogue pipelines. There was also an emerging awareness of governance: discussions of bias, safety, and evaluation began to appear more prominently, influenced by OpenAI’s decision to withhold the full GPT-2 release. This early ethical caution signaled that text generation for chatbots was not only a technical challenge but also a social and cultural one.
In retrospect, this period can be seen as a prelude to the large-scale chatbot systems of the 2020s. The research captured a field that suddenly had powerful new tools but was still working out how to make them reliable and safe for open-domain conversation. The experiments with emotion, context, and control foreshadowed the refinements that would later underpin systems like ChatGPT. Thus, the story of 2018–2019 is one of anticipation and groundwork: a time when chatbots stopped being confined to rigid, narrow roles and began to be imagined as general conversational partners, even if the technology had not yet matured to make that vision a reality.
After 2018–2019, the field of text generation and chatbots entered a phase of rapid scaling and mainstream deployment, driven by increasingly large pretrained language models. GPT-3, released in 2020, demonstrated that sheer scale—175 billion parameters—could deliver striking improvements in fluency, contextual consistency, and task transfer, pushing conversational AI much closer to practical utility. This period also saw the rise of fine-tuning strategies and prompt engineering, which allowed systems to adapt to specific tasks and personalities without retraining from scratch. Commercial chatbots and assistants began incorporating these advances, moving beyond scripted flows to more open-domain, adaptive conversations. At the same time, ethical debates intensified around misinformation, bias, and safety, as researchers and companies grappled with the risks of deploying generative systems at scale. By the early 2020s, this combination of technical breakthroughs and societal concerns laid the foundation for applications like ChatGPT, where the focus shifted from research prototypes to widely accessible conversational platforms, marking a new era in the integration of text generation into everyday human-computer interaction.