{"slug": "sequence-transduction-the-forgotten-problem-that-led-to-modern-llms", "title": "Sequence Transduction: The Forgotten Problem That Led to Modern LLMs", "summary": "Shrijith Venkatramana, building git-lrc, explains that sequence transduction—transforming one sequence into another—was the original problem that led to modern large language models. Early neural networks like RNNs and LSTMs struggled with long-range dependencies and sequential processing, but the Transformer architecture solved these issues, enabling today's LLMs.", "body_md": "*Hello, I'm Shrijith Venkatramana. I'm building git-lrc, an AI code reviewer that runs on every commit. Star Us to help devs discover the project. Do give it a try and share your feedback for improving the product.*\n\n*Most developers think large language models were built to predict the next word. They weren't—not at first.*\n\nIf you travel back to the early 2010s, the hardest problems in AI weren't writing poems or generating code. They were translating English into French, converting speech into text, and summarizing documents. These were all instances of the same challenge: **sequence transduction**.\n\nThe term appears almost casually in the opening paragraph of the Transformer paper:\n\n\"...sequence modeling and transduction problems such as language modeling and machine translation.\"\n\nToday, almost everyone knows the Transformer. Very few remember the problem it was invented to solve.\n\nIronically, solving sequence transduction turned out to create the foundation upon which modern LLMs would later emerge.\n\nLet's explore why.\n\nImagine you own a factory.\n\nA sequence modeling problem asks:\n\n\"Given everything that has happened so far, what comes next?\"\n\nLike predicting the next product coming off the conveyor belt.\n\n```\nThe cat sat on the _____\n                ↓\n               mat\n```\n\nThis is language modeling.\n\nA sequence transduction problem is larger idea.\n\nInstead of predicting one missing piece, you transform an entire sequence into another.\n\n```\nEnglish\n↓\n\n\"The weather is nice.\"\n\n↓\n\nFrench\n\n\"Il fait beau.\"\n```\n\nOr\n\n```\nAudio\n↓\n\nWaveform\n\n↓\n\nText\n\n\"Welcome everyone.\"\n```\n\nOr\n\n```\nBuggy code\n\n↓\n\nCorrect code\n```\n\nDifferent input.\n\nDifferent output.\n\nOften different lengths.\n\nThe model must understand the entire source or at least large parts of it before generating the target.\n\nIn hindsight, modern AI assistants spend almost all of their time doing sequence transduction:\n\nThey all reduce to:\n\nInput sequence → Output sequence\n\nHumans underestimate how much memory translation requires.\n\nConsider translating:\n\n\"The committee, after reviewing several proposals over three months, finally approved the budget.\"\n\nSuppose you're translating into German.\n\nThe verb may not appear until the end.\n\nTo translate correctly, the model must remember:\n\nEarly neural networks processed text one word at a time.\n\n```\nWord₁ → hidden state\n              ↓\nWord₂ → hidden state\n              ↓\nWord₃ → hidden state\n```\n\nEverything had to be compressed into one hidden vector.\n\nIt was like asking someone to summarize an entire novel using only one sticky note.\n\nEventually information disappears.\n\nThis became known as the **long-range dependency problem**.\n\nDuring the late 1980s and 1990s, researchers developed **Recurrent Neural Networks (RNNs)** to process sequential data.\n\nUnlike ordinary neural networks, RNNs reused the same parameters at every time step.\n\nInstead of building a different network for every word, one network repeatedly updated an internal memory.\n\nMathematically:\n\n```\nhidden_state = f(previous_hidden_state, current_input)\n```\n\nThe same computation runs repeatedly.\n\nThis parameter sharing was elegant.\n\nSuppose an RNN contains one million parameters.\n\nA thousand-word paragraph still uses one million parameters—not a billion.\n\nThe network simply reuses them.\n\nEconomically, this was attractive. But computationally, it was painful.\n\nEverything had to happen sequentially.\n\nWord 500 could not begin until word 499 finished.\n\nNo parallelism. No GPUs in picture. Training was slow.\n\nIn 1997, Sepp Hochreiter and Jürgen Schmidhuber introduced one of the most influential ideas in deep learning:\n\n**Long Short-Term Memory (LSTM).**\n\nInstead of blindly overwriting memory every step, the network learned gates.\n\nThink of memory like a whiteboard.\n\nEach word asks three questions:\n\nThose questions became three learned gates.\n\nForget gate.\n\nInput gate.\n\nOutput gate.\n\nInstead of forcing every piece of information through the same bottleneck, the model learned what deserved long-term storage.\n\nA surprisingly intuitive analogy is human note-taking.\n\nMost conversations are forgotten.\n\nA few facts are written into your notebook.\n\nLSTMs learned which facts deserved the notebook.\n\nFor over a decade, LSTMs dominated speech recognition, handwriting recognition, language translation, and time-series forecasting.\n\nGoogle, Apple, Microsoft, and Baidu all deployed enormous production systems powered by them.\n\nAround 2014, another breakthrough appeared.\n\nInstead of using one RNN/LSTMs for everything, researchers separated the task into two parts.\n\n```\nInput sentence\n      ↓\nEncoder\n      ↓\nMeaning vector\n      ↓\nDecoder\n      ↓\nOutput sentence\n```\n\nThis architecture became known as the **sequence-to-sequence (Seq2Seq)** model.\n\nFor the first time, neural networks learned translation end-to-end.\n\nNo phrase tables or handcrafted grammar or brittle rules. It was just millions of examples.\n\nOne famous anecdote came from Google.\n\nTraditional statistical machine translation systems consisted of dozens of independently engineered components accumulated over years.\n\nNeural machine translation replaced much of that complexity with a single differentiable model trained from data. In 2016, Google reported that its neural system substantially reduced translation errors across multiple language pairs while simplifying the overall pipeline.\n\nThis represented an engineering improvement for sure, but more importantly it was a philosophical shift.\n\nInstead of programming language knowledge -- we trained it.\n\nThe Seq2Seq model still had one weakness.\n\nEverything had to fit inside one vector.\n\nInformation gets lost.\n\nIn 2014, Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio proposed **attention**.\n\nInstead of remembering everything, just look back whenever necessary.\n\nWhile generating each output word, the decoder asks:\n\nWhich input words matter right now?\n\nNot every word.\n\nOnly the relevant ones.\n\nTranslation suddenly became much easier.\n\nLong sentences improved dramatically.\n\nThe Transformer paper in 2017 -- instead of improving recurrent networks, removed recurrence entirely.\n\nEvery word could attend directly to every other word.\n\nParallel computation became possible.\n\nTraining speed increased enormously.\n\nGPUs became dramatically more efficient because every token in a sequence could be processed simultaneously rather than one after another.\n\nEven more interesting was the economics.\n\nSuppose translating a sentence of 100 words with an RNN requires roughly 100 sequential computation steps.\n\nA Transformer still performs similar amounts of arithmetic overall, but many of those operations can execute in parallel on modern accelerators.\n\nThe wall-clock training time drops dramatically because GPUs are optimized for large batches of matrix multiplications rather than long chains of sequential dependencies.\n\nThat operational advantage—not merely higher accuracy—made scaling practical.\n\nThe remarkable twist is that the architecture built to solve translation generalized astonishingly well.\n\nReplace:\n\n```\nEnglish → French\n```\n\nwith\n\n```\nQuestion → Answer\n```\n\nor\n\n```\nCode → Documentation\n```\n\nor\n\n```\nPrompt → Python program\n```\n\nThe underlying problem barely changes.\n\nIt remains sequence transduction.\n\nModern LLMs still perform next-token prediction during training.\n\nBut from a developer's perspective, they are universal transduction engines.\n\nEvery prompt is transformed into another sequence.\n\nThe interface changed.\n\nThe underlying abstraction survived.\n\nThe history of AI is often told as a story about predicting the next word.\n\nThat story is incomplete.\n\nFor decades, researchers wrestled with a harder question:\n\nHow do we transform one complex sequence into another while preserving meaning?\n\nThat single question drove the invention of encoder–decoder architectures, LSTMs, attention mechanisms, and ultimately the Transformer itself.\n\nThe next time you ask an LLM to refactor code, summarize a meeting, or generate a SQL query, remember what it's really doing.\n\nNot merely predicting words.\n\nPerforming sequence transduction at an extraordinary scale.\n\n**What surprised you most about this history?** Did you always think LLMs grew out of language modeling, or is it more useful to think of them as the latest—and perhaps most powerful—generation of sequence transduction systems?\n\n*AI agents write code fast. They also silently remove logic, change behavior, and introduce bugs -- without telling you. You often find out in production.\n\ngit-lrc fixes this. It hooks into git commit and reviews every diff before it lands. 60-second setup. Completely free.*\n\nAny feedback or contributors are welcome! It's online, source-available, and ready for anyone to use.\n\n| [🇩🇰 Dansk](https://github.com/HexmosTech/git-lrc/readme/README.da.md) | [🇪🇸 Español](https://github.com/HexmosTech/git-lrc/readme/README.es.md) | [🇮🇷 Farsi](https://github.com/HexmosTech/git-lrc/readme/README.fa.md) | [🇫🇮 Suomi](https://github.com/HexmosTech/git-lrc/readme/README.fi.md) | [🇯🇵 日本語](https://github.com/HexmosTech/git-lrc/readme/README.ja.md) | [🇳🇴 Norsk](https://github.com/HexmosTech/git-lrc/readme/README.nn.md) | [🇵🇹 Português](https://github.com/HexmosTech/git-lrc/readme/README.pt.md) | [🇷🇺 Русский](https://github.com/HexmosTech/git-lrc/readme/README.ru.md) | [🇦🇱 Shqip](https://github.com/HexmosTech/git-lrc/readme/README.sq.md) | [🇨🇳 中文](https://github.com/HexmosTech/git-lrc/readme/README.zh.md) | [🇮🇳 हिन्दी](https://github.com/HexmosTech/git-lrc/readme/README.hi.md) |\n\nGenAI today is a **race car without brakes**. It accelerates fast -- you describe something, and large blocks of code appear instantly. But AI agents *silently break things*: they remove logic, relax constraints, introduce expensive cloud calls, leak credentials, and change behavior -- without telling you. You often find out in production.\n\n** git-lrc is your braking system.** It hooks into\n\n`git commit`\n\nand runs an AI review on every diff In short, git-lrc helps **Prevent Outages, Breaches, and Technical Debt Before They Happen**\n\n**At a glance:** [10 risk categories](https://github.com/HexmosTech/git-lrc#what-git-lrc-checks-for) · [100+ failure patterns tracked](https://github.com/HexmosTech/git-lrc#what-git-lrc-checks-for) · every commit…", "url": "https://wpnews.pro/news/sequence-transduction-the-forgotten-problem-that-led-to-modern-llms", "canonical_source": "https://dev.to/shrsv/sequence-transduction-the-forgotten-problem-that-led-to-modern-llms-439e", "published_at": "2026-06-27 17:15:41+00:00", "updated_at": "2026-06-27 17:33:37.102092+00:00", "lang": "en", "topics": ["large-language-models", "machine-learning", "natural-language-processing", "ai-research", "neural-networks"], "entities": ["Shrijith Venkatramana", "git-lrc", "Transformer", "LSTM", "Sepp Hochreiter", "Jürgen Schmidhuber"], "alternates": {"html": "https://wpnews.pro/news/sequence-transduction-the-forgotten-problem-that-led-to-modern-llms", "markdown": "https://wpnews.pro/news/sequence-transduction-the-forgotten-problem-that-led-to-modern-llms.md", "text": "https://wpnews.pro/news/sequence-transduction-the-forgotten-problem-that-led-to-modern-llms.txt", "jsonld": "https://wpnews.pro/news/sequence-transduction-the-forgotten-problem-that-led-to-modern-llms.jsonld"}}