ELIZA: The Origin of Conversational Agents (1960s)​

The lineage of conversational AI begins with ELIZA, created by Joseph Weizenbaum at MIT in 1966.

ELIZA was a rule-based chatbot that used simple pattern matching and substitution rules to simulate conversation. Weizenbaum’s most famous script for ELIZA, called “DOCTOR,” parroted a Rogerian psychotherapist: it would reflect the user’s inputs back as questions or prompts. For example, if a user said “I feel stressed about work,” ELIZA might reply, “Why do you feel stressed about work?” This gave an illusion of understanding without any real comprehension of meaning.

ELIZA was one of the first programs to attempt the Turing Test (engaging in dialogue indistinguishable from a human). While it was a very simple system, ELIZA proved that humans could be momentarily convinced they were chatting with an understanding entity – a phenomenon later dubbed the “Eliza effect.” This early success sparked widespread interest and laid the foundation for chatbot development, even though ELIZA’s capabilities were rudimentary and entirely scripted.

Scripted Chatbots: Menu-Driven Systems and AIML (1980s–1990s)​

After ELIZA, conversational systems remained largely rule-based but grew more sophisticated.

Many early customer service bots and phone IVR systems in the 1980s and 1990s were essentially menu-driven – they guided users through predefined options (e.g. “Press 1 for account info, 2 for support”) rather than truly “understanding” free text.

Around the same time, more advanced text-based bots used bigger rule sets and pattern libraries to appear conversational. A landmark was A.L.I.C.E. (Artificial Linguistic Internet Computer Entity), introduced in 1995 by Richard Wallace. ALICE employed a specialized scripting language called AIML (Artificial Intelligence Markup Language) to manage conversation rules. Instead of hard-coding every response, AIML let developers define patterns and template replies. As a result, ALICE had an enormous base of about 41,000 predefined templates and pattern-response pairs. This allowed it to engage in more varied, natural-sounding chats than ELIZA’s simple keyword tricks. ALICE was even awarded the Loebner Prize (a conversational AI contest) multiple times in the early 2000s.

Despite these improvements, bots like ALICE and its contemporaries still relied on static scripts. They lacked true understanding and could be easily led off-track by inputs outside their scripted patterns. In practice, developers often had to anticipate countless phrasings or guide users to stay within expected inputs (hence the popularity of menu-driven designs for reliability). By the late 1990s, the paradigm in industry was that chatbots were essentially expert systems: large collections of if-then rules or decision trees. These systems worked for narrowly defined tasks (like tech support FAQs or simple dialog games) but were brittle and labor-intensive to expand. Still, this era demonstrated that with enough rules, a chatbot could handle surprisingly complex dialogues – a stepping stone toward more data-driven approaches.

The Rise of ML and Hybrid NLU Frameworks (2010s)​

The 2010s saw a shift toward machine learning (ML) in conversational AI, aiming to make chatbots less brittle and easier to build. Instead of manually writing thousands of rules, developers began using statistical Natural Language Understanding (NLU) techniques to interpret user input.

Frameworks like Google’s Dialogflow and the open-source Rasa platform (open-sourced in 2017) exemplified this hybrid approach. They let developers define intents (user’s goals) and entities (key information), and then train ML models on example phrases. The ML model generalizes from those examples, so the bot can recognize a user request even if it’s phrased in an unforeseen way. For instance, whether a user says “Book me a flight for tomorrow” or “I need to fly out tomorrow,” an intent classification model can learn to map both to the same “BookFlight” intent. This significantly reduced the need to hand-craft every possible pattern.

Over time, these NLU models incorporated Transformer-based innovations to boost accuracy. For example, Rasa introduced the DIET (Dual Intent and Entity Transformer) architecture, a lightweight transformer network for intent classification and entity extraction. Such models approach the language-understanding performance of large pre-trained transformers like BERT, but are tailored to the specific intents/entities of the chatbot. Meanwhile, the dialogue management in these frameworks was still often rule-based or followed story graphs defined by developers. In Dialogflow, one would design conversational flows with contexts and transitions. In Rasa, one could write stories or rules that specify how the bot should respond or which action to take next given the recognized intent and dialogue state.

This combination of ML + rules was a major step up. It allowed chatbots to handle more natural language variation while maintaining controlled flows for business logic. Many virtual assistants and customer support bots deployed in the late 2010s (on platforms like Facebook Messenger, Slack, or bank websites) were built this way. However, challenges remained. Designing and maintaining the conversation flows could become complex as an assistant’s scope grew. Every new feature or edge case might require adding new intents, more training data, and more dialogue branches – which risked turning into a tangle of states (a “graph-based” framework that can become overwhelmingly complex as the agent grows).

Moreover, while these systems were more flexible than pure rules, they still could fail if users went truly off-script or asked something outside the trained data.

The LLM Era: Prompt-Based Conversations and RAG (2020s)​

A watershed moment came with the advent of Large Language Models (LLMs) in the early 2020s. Models like OpenAI’s GPT-3 (2020) and later ChatGPT (2022) demonstrated that a single, massive neural network trained on internet-scale data could engage in remarkably fluent open-ended conversations.

ChatGPT, for instance, can generate responses that are often difficult to distinguish from human-written text, and it can carry on a dialogue spanning many turns without explicit rules scripted by a developer. Instead of defining intents or writing dialogue trees, developers could now provide a prompt (e.g. a starting instruction like “You are a helpful customer service agent…”) and let the LLM generate the conversation. This approach flips the old paradigm: rather than the developer explicitly mapping out the conversation, the model itself learned conversational patterns from its training data and can dynamically produce answers.

However, using LLMs for reliable conversational agents brought new challenges. Firstly, large models have a fixed knowledge cutoff (ChatGPT’s base knowledge, for example, only went up to 2021 data in its initial release). And they are prone to “hallucinations” – confidently generating incorrect or fabricated information when asked something outside their knowledge.

To tackle this, a technique called Retrieval-Augmented Generation (RAG) became popular. RAG pairs the LLM with an external knowledge source: when a user asks a question, the system first retrieves relevant documents (from a database or search index) and then feeds those into the model’s context so it can base its answer on up-to-date, factual information. This method helps address the knowledge gap and reduces hallucinations by grounding the LLM’s responses in real data. Many modern QA bots and enterprise assistants use RAG – for example, a customer support chatbot might retrieve policy documents or user account info so that the LLM’s answer is accurate and personalized.

Another tool in this era is the use of system prompts and few-shot examples to steer LLM behavior. By providing instructions like “Always respond in a formal tone,” or giving examples of desired Q&A pairs, developers attempt to guide the model’s style and compliance with rules. This is powerful but not foolproof: LLMs often ignore instructions if a conversation is long or if the prompt is complex, as parts fall out of its attention.

Essentially, pure prompting lacks guarantees – it’s still the model’s learned behavior that decides the outcome. And while RAG can inject facts, it “can’t guide behavior” or enforce complex dialogue flows. For instance, RAG will help a bot cite the correct price from a database, but it won’t ensure the bot follows a company’s escalation protocol or keeps a consistent persona beyond what the prompt suggests.

By late 2024, developers had a mix of approaches for conversational AI:

• Fine-tuning an LLM on custom data to specialize it (which can be expensive and inflexible, often requiring re-training the whole model for small changes).
• Prompt engineering and RAG to leverage pre-trained LLMs without full retraining (quick to prototype, but needing careful tweaking and still lacking strong runtime control and consistency).
• Traditional frameworks (intents/flows or graphical dialog builders) which offer deterministic behavior but at the cost of flexibility and significant manual work, especially as complexity grows.

Each approach had trade-offs. Many teams found themselves combining methods and still encountering issues with consistency and maintainability. This set the stage for a new paradigm aiming to capture the best of both worlds – the knowledge and linguistic fluency of LLMs with the control and predictability of rule-based systems. This emerging paradigm is what we refer to as Conversation Modeling.

Conversation Modeling with Parlant.io: A New Paradigm​

The latest development in conversational AI is the rise of Conversation Modeling platforms, with Parlant as a prime example. Parlant is an open-source Conversation Modeling Engine designed to build user-facing agents that are adaptive, yet predictable and accurate. In essence, it provides a structured way to shape an LLM-driven conversation without reverting to rigid workflows or expensive model retraining. Instead of coding up dialogue flows or endlessly tweaking prompts, a developer using Parlant focuses on writing guidelines that direct the AI’s behavior.

Guideline-Driven Conversations​

Guidelines in Parlant are like contextual rules or principles that the AI agent should follow. Each guideline has a condition (when it applies) and an action (what it should make the agent do).

For example, a guideline might be: When the user is asking to book a hotel room and they haven’t specified the number of guests, then ask for the number of guests. This “when X, then Y” format encapsulates business logic or conversation policy in a flexible, declarative way. The crucial difference from old-school rules is that guidelines don’t script out the exact wording of the bot’s response or a fixed path – they simply set expectations that the generative model must adhere to.

Parlant’s engine takes care of enforcing these guidelines during the conversation. It does so by dynamically injecting the relevant guidelines into the LLM’s context at the right time.

In our hotel booking example, if the user says, “I need a hotel in New York this weekend,” Parlant would recognize that the “ask about number of guests” guideline’s condition is met. It would then load that guideline into the prompt for the LLM, so the AI’s response would be guided to, say, “Certainly! I can help with that. How many guests will be staying?” instead of the model’s default response, which might have omitted the guest count question. If another guideline says the agent should always respond enthusiastically, that guideline would also be activated, ensuring the tone is upbeat. This way, multiple guidelines can shape each response.

Importantly, Parlant keeps the model’s “cognitive load” light by only including guidelines that are contextually relevant, given the current conversation state. An agent could have dozens of guidelines defined, but the user doesn’t get bombarded with irrelevant behavior – the system is smart about which rules apply when.

This dynamic approach allows richer interactions than a static flowchart: the conversation can go in many directions, but whenever a situation arises that has a guideline, the model will consistently follow that instruction. In effect, the LLM becomes more grounded and consistent in its behavior, without losing its natural language flexibility.

Reliability, Enforcement, and Explainability​

A standout feature of Parlant’s conversation modeling is how it checks and explains the agent’s decisions.

Traditional chatbots might log which intent was matched or which rule fired, but Parlant goes further. It actually supervises the AI’s output before it reaches the user to ensure that the guidelines were followed. One novel technique the Parlant team developed is called Attentive Reasoning Queries (ARQs).

In simplified terms, ARQs are an internal query the system poses (via the LLM’s reasoning capabilities) to double-check that the response satisfies the active guidelines. If something is off – say the model produced an answer that violates a guideline or contradicts a prior instruction – Parlant can catch that and correct course. This might involve instructing the model to try again or adjusting the context. The result is an extra layer of assurance that the agent’s answers are on-policy and safe before the user sees them.

From a developer’s perspective, this yields a high degree of predictability and makes it easier to debug conversations. Parlant provides extensive feedback on the agent’s decisions and interpretations. One can trace which guideline triggered at a given turn, what the model “thought” the user meant, and why it chose a certain reply.

This level of transparency is rarely available in pure LLM solutions (which can feel like a black box) and even in many ML-based frameworks. If a conversation went wrong, you can quickly see if a guideline was missing or mis-specified, or if the AI misunderstood because no guideline covered a scenario, and then adjust accordingly.

Faster Iteration and Scalable Testing​

Conversation modeling also dramatically improves the development lifecycle for AI agents. In older approaches, if a business stakeholder said “Our chatbot should change its behavior in X scenario,” implementing that could mean re-writing parts of a flow, collecting new training data, or even fine-tuning a model – and then testing extensively to ensure nothing else broke. With Parlant, that request usually translates to simply adding or editing a guideline.

For instance, if the sales team decides that during holidays the bot should offer a 10% discount, a developer can implement a guideline: When it is a holiday, then the agent should offer a discount. There’s no need to retrain the language model or overhaul the dialog tree; the guideline is a modular addition.

Parlant was built so that developers can iterate quickly in response to business needs, updating the conversational behavior at the pace of changing requirements. This agility is akin to how a human manager might update a customer service script or policies, and immediately all agents follow the new policy – here, the “policies” are guidelines, and the AI agent follows them immediately once updated.

Because guidelines are discrete and declarative, it’s also easier to test and scale conversational agents built this way. Each guideline can be seen as a testable unit: one can devise example dialogues to verify that the guideline triggers properly and that the agent’s response meets expectations. Parlant’s deterministic injection of guidelines means the agent will behave consistently for a given scenario, which makes automated testing feasible (you won’t get a completely random response every time, as raw LLMs might give).

The platform’s emphasis on explainability also means you can catch regressions or unintended effects early – you’ll see if a new guideline conflicts with an existing one, for example. This approach lends itself to more robust, enterprise-grade deployments where reliability and compliance are crucial.

Integration with Business Logic and Tools​

Another way Parlant stands apart is in how it separates conversational behavior from back-end logic.

Earlier chatbot frameworks sometimes entangled the two – for example, a dialog flow node might both decide what to say and invoke an API call. Parlant encourages a clean separation: use guidelines for conversation design, and use tool functions (external APIs or code) for any business logic or data retrieval.

Guidelines can trigger those tools, but they don’t contain the logic themselves. This means you can have a guideline like “When the customer asks to track an order, then retrieve the order status and communicate it.”

The actual act of looking up the order status is done by a deterministic function (so no uncertainty there), and the guideline ensures the AI knows when to call it and how to incorporate the result into the conversation. By not embedding complex computations or database queries into the AI’s prompt, Parlant avoids the pitfalls of LLMs struggling with multi-step reasoning or math.

The division of labor leads to more maintainable and reliable systems: developers can update business logic in code without touching the conversation scripts, and vice versa. It’s a design paradigm that scales well as projects grow.

Real-World Impact and Use Cases​

All these capabilities make conversation modeling suitable for applications that were previously very challenging for conversational AI.

Parlant emphasizes use cases like regulated industries and high-stakes customer interactions. For example, in financial services or legal assistance, an AI agent must strictly follow compliance guidelines and wording protocols – a single off-script response can have serious consequences. Parlant’s approach ensures the agent reliably follows prescribed protocols in such domains.

In healthcare communications, accuracy and consistency are paramount; an agent should stick to approved responses and escalate when unsure. Guidelines can encode those requirements (e.g. “if user mentions a medical symptom, always provide the disclaimer and suggest scheduling an appointment”).

Brand-sensitive customer service is another area: companies want AI that reflects their brand voice and policies exactly. With conversation modeling, the brand team can literally read the guidelines as if they are a policy document for the AI. This is a big improvement over hoping an ML model “learned” the desired style from training examples.

Teams using Parlant have noted that it enables richer interactions without sacrificing control. Users aren’t forced down rigid conversational menus; instead, they can ask things naturally and the AI can handle it, because the generative model is free to respond creatively as long as it follows the playbook defined by guidelines.

At the same time, the development overhead is lower – you manage a library of guidelines (which are human-readable and modular) instead of a spaghetti of code. And when the AI does something unexpected, you have the tools to diagnose why and fix it systematically.

In short, Parlant’s conversation modeling represents a convergence of the two historical threads in chatbot evolution: the free-form flexibility of advanced AI language models with the governed reliability of rule-based systems. This paradigm is poised to define the next generation of conversational agents that are both intelligent and trustworthy, from virtual customer assistants to automated advisors across industries.

Disclaimer: The views and opinions expressed in this guest article are those of the author and do not necessarily reflect the official policy or position of Marktechpost.

The post From ELIZA to Conversation Modeling: Evolution of Conversational AI Systems and Paradigms appeared first on MarkTechPost.

This article explores key innovations, including granular atomic guidelines, dynamic evaluation and filtering of instructions, and Attentive Reasoning Queries (ARQs), while acknowledging implementation limitations and trade-offs.

The Challenge: Inconsistent AI Performance in Customer Service

LLMs are already providing tangible business value when used as assistants to human representatives in customer service scenarios. However, their reliability as autonomous customer-facing agents remains a challenge.

Traditional approaches to developing conversational LLM applications often fail in real-world use cases. The two most common approaches are:

1. Iterative prompt engineering, which leads to inconsistent, unpredictable behavior.
2. Flowchart-based processing, which sacrifices the real magic of LLM-powered interactions: dynamic, free-flowing, human-like interactions.

In high-stakes customer-facing applications, such as banking, even minor errors can have serious consequences. For instance, an incorrectly executed API call (like transferring money) can lead to lawsuits and reputational damage. Conversely, mechanical interactions that lack naturalness and rapport hurt customer trust and engagement, limiting containment rates (cases resolved without human intervention).

For LLMs to reach their full potential as dynamic, autonomous agents in real-world cases, we must make them follow business-specific instructions consistently and at scale, while maintaining the flexibility of natural, free-flowing interactions.

How to Create a Reliable, Autonomous Customer Service Agent with LLMs

To address these gaps in LLMs and current approaches, and achieve a level of reliability and control that works well in real-world cases, we must question the approaches that failed.

One of the first questions I had when I started working on Parlant (an open-source framework for customer-facing AI agents) was, “If an AI agent is found to mishandle a particular customer scenario, what would be the optimal process for fixing it?” Adding additional demands to an already-lengthy prompt, like “Here’s how you should approach scenario X…” would quickly become complicated to manage, and the results weren’t consistent anyhow. Besides that, adding those instructions unconditionally posed an alignment risk since LLMs are inherently biased by their input. It was therefore important that instructions for scenario X did not leak into other scenarios which potentially required a different approach.

We thus realized that instructions needed to apply only in their intended context. This made sense because, in real-life, when we catch unsatisfactory behavior in real-time in a customer-service interaction, we usually know how to correct it: We’re able to specify both what needs to improve as well as the context in which our feedback should apply. For example, “Be concise and to the point when discussing premium-plan benefits,” but “Be willing to explain our offering at length when comparing it to other solutions.”

In addition to this contextualization of instructions, in training a highly capable agent that can handle many use cases, we’d clearly need to tweak many instructions over time as we shaped our agent’s behavior to business needs and preferences. We needed a systematic approach.

Stepping back and rethinking, from first principles, our ideal expectations from modern AI-based interactions and how to develop them, this is what we understood about how such interactions should feel to customers:

1. Empathetic and coherent: Customers should feel in good hands when using AI.
2. Fluid, like Instant Messaging (IM): Allowing customers to switch topics back and forth, express themselves using multiple messages, and ask about multiple topics at a time.
3. Personalized: You should feel that the AI agent knows it’s speaking to you and understands your context.

From a developer perspective, we also realized that:

1. Crafting the right conversational UX is an evolutionary process. We should be able to confidently modify agent behavior in different contexts, quickly and easily, without worrying about breaking existing behavior.
2. Instructions should be respected consistently. This is hard to do with LLMs, which are inherently unpredictable creatures. An innovative solution was required.
3. Agent decisions should be transparent. The spectrum of possible issues related to natural language and behavior is too wide. Resolving issues in instruction-following without clear indications of how an agent interpreted our instructions in a given scenario would be highly impractical in production environments with deadlines.

Implementing Parlant’s Design Goals

Our main challenge was how to control and adjust an AI agent’s behavior while ensuring that instructions are not spoken in vain—that the AI agent implements them accurately and consistently. This led to a strategic design decision: granular, atomic guidelines.

1. Granular Atomic Guidelines

Complex prompts often overwhelm LLMs, leading to incomplete or inconsistent outputs with respect to the instructions they specify. We solved this in Parlant by dropping broad prompts for self-contained, atomic guidelines. Each guideline consists of:

• Condition: A natural-language query that determines when the instruction should apply (e.g., “The customer inquires about a refund…”)
• Action: The specific instruction the LLM should follow (e.g., “Confirm order details and offer an overview of the refund process.”)

By segmenting instructions into manageable units and systematically focusing their attention on each one at a time, we could get the LLM to evaluate and enforce them with higher accuracy.

2. Filtering and Supervision Mechanism

LLMs are highly influenced by the content of their prompts, even if parts of the prompt are not directly relevant to the conversation at hand.

Instead of presenting all guidelines at once, we made Parlant dynamically match and apply only the relevant set of instructions at each step of the conversation. This real-time matching can then be leveraged for:

• Reduced cognitive overload for the LLM: We’d avoid prompt leaks and increase the model’s focus on the right instructions, leading to higher consistency. 
• Supervision: We added a mechanism to highlight each guideline’s impact and enforce its application, increasing conformance across the board.
• Explainability: Every evaluation and decision generated by the system includes a rationale detailing how guidelines were interpreted and the reasoning behind skipping or activating them at each point in the conversation.
• Continuous improvement: By monitoring guideline effectiveness and agent interpretation, developers could easily refine their AI’s behavior over time. Because guidelines are atomic and supervised, you could easily make structured changes without breaking fragile prompts. 

3. Attentive Reasoning Queries (ARQs)

While “Chain of Thought” (CoT) prompting improves reasoning, it remains limited in its ability to maintain consistent, context-sensitive responses over time. Parlant introduces Attentive Reasoning Queries (ARQs)—a technique we’ve devised to ensure that multi-step reasoning stays effective, accurate, and predictable, even across thousands of runs. You can find our research paper on ARQs vs. CoT on parlant.io and arxiv.org.

ARQs work by directing the LLM’s attention back to high-priority instructions at key points in the response generation process, getting the LLM to attend to those instructions and reason about them right before it needs to apply them. We found that “localizing” the reasoning around the part of the response where a specific instruction needs to be applied provided significantly greater accuracy and consistency than a preliminary, nonspecific reasoning process like CoT.

Acknowledging Limitations

While these innovations improve instruction-following, there are challenges to consider:

• Computational overhead: Implementing filtering and reasoning mechanisms increases processing time. However, with hardware and LLMs improving by the day, we saw this as a possibly controversial, yet strategic design choice.
• Alternative approaches: In some low-risk applications, such as assistive AI co-pilots, simpler methods like prompt-tuning or workflow-based approaches often suffice.

Why Consistency Is Crucial for Enterprise-Grade Conversational AI

In regulated industries like finance, healthcare, and legal services, even 99% accuracy poses significant risk. A bank handling millions of monthly conversations cannot afford thousands of potentially critical errors. Beyond accuracy, AI systems must be constrained such that errors, even when they occur, remain within strict, acceptable bounds.

In response to the demand for greater accuracy in such applications, AI solution vendors often argue that humans also make mistakes. While this is true, the difference is that, with human employees, correcting them is usually straightforward. You can ask them why they handled a situation the way they did. You can provide direct feedback and monitor their results. But relying on “best-effort” prompt-engineering, while being blind to why an AI agent even made some decision in the first place, is an approach that simply doesn’t scale beyond basic demos.

This is why a structured feedback mechanism is so important. It allows you to pinpoint what changes need to be made, and how to make them while keeping existing functionality intact. It’s this realization that put us on the right track with Parlant early on.

Handling Millions of Customer Interactions with Autonomous AI Agents

For enterprises to deploy AI at scale, consistency and transparency are non-negotiable. A financial chatbot providing unauthorized advice, a healthcare assistant misguiding patients, or an e-commerce agent misrepresenting products can all have severe consequences.

Parlant redefines AI alignment by enabling:

• Enhanced operational efficiency: Reducing human intervention while ensuring high-quality AI interactions.
• Consistent brand alignment: Maintaining coherence with business values.
• Regulatory compliance: Adhering to industry standards and legal requirements.

This methodology represents a shift in how AI alignment is approached in the first place. Using modular guidelines with intelligent filtering instead of long, complex prompts; adding explicit supervision and validation mechanisms to ensure things go as planned—these innovations mark a new standard for achieving reliability with LLMs. As AI-driven automation continues to expand in adoption, ensuring consistent instruction-following will become an accepted necessity, not an innovative luxury.

If your company is looking to deploy robust AI-powered customer service or any other customer-facing application, you should look into Parlant, an agent framework for controlled, explainable, and enterprise-ready AI interactions.

The post Achieving Critical Reliability in Instruction-Following with LLMs: How to Achieve AI Customer Service That’s 100% Reliable appeared first on MarkTechPost.

While I deeply respect LeCun’s work and approach to AI development and resonate with many of his insights, I believe this particular claim overlooks some key aspects of how LLMs function in practice. In this post, I’ll explain why autoregressive models are not inherently divergent and doomed, and how techniques like Chain-of-Thought (CoT) and Attentive Reasoning Queries (ARQs)—a method we’ve developed to achieve high-accuracy customer interactions with Parlant—effectively prove otherwise.

What is Autoregression?

At its core, an LLM is a probabilistic model trained to generate text one token at a time. Given an input context, the model predicts the most likely next token, feeds it back into the original sequence, and repeats the process iteratively until a stop condition is met. This allows the model to generate anything from short responses to entire articles.

For a deeper dive into autoregression, check out our recent technical blog post.

Do Generation Errors Compound Exponentially?  

LeCun’s argument can be unpacked as follows:

1. Define C as the set of all possible completions of length N.
2. Define A ⊂ C as the subset of acceptable completions, where U = C – A represents the unacceptable ones.
3. Let Ci[K] be an in-progress completion of length K, which at K is still acceptable (Ci[N] ∈ A may still ultimately apply).
4. Assume a constant E as the error probability of generating the next token, such that it pushes Ci into U.
5. The probability of generating the remaining tokens while keeping Ci in A is then (1 – E)^(N – K).

This leads to LeCun’s conclusion that for sufficiently long responses, the likelihood of maintaining coherence exponentially approaches zero, suggesting that autoregressive LLMs are inherently flawed.

But here’s the problem: E is not constant.

To put it simply, LeCun’s argument assumes that the probability of making a mistake in each new token is independent. However, LLMs don’t work that way.

As an analogy to what allows LLMs to overcome this problem, imagine you’re telling a story: if you make a mistake in one sentence, you can still correct it in the next one to keep the narrative coherent. The same applies to LLMs, especially when techniques like Chain-of-Thought (CoT) prompting guide them toward better reasoning by helping them reassess their own outputs along the way.

Why This Assumption is Flawed

LLMs exhibit self-correction properties that prevent them from spiraling into incoherence.

Take Chain-of-Thought (CoT) prompting, which encourages the model to generate intermediate reasoning steps. CoT allows the model to consider multiple perspectives, improving its ability to converge to an acceptable answer. Similarly, Chain-of-Verification (CoV) and structured feedback mechanisms like ARQs guide the model in reinforcing valid outputs and discarding erroneous ones.

A small mistake early on in the generation process doesn’t necessarily doom the final answer. Figuratively speaking, an LLM can double-check its work, backtrack, and correct errors on the go.

Attentive Reasoning Queries (ARQs) are a Game-Changer

At Parlant, we’ve taken this principle further in our work on Attentive Reasoning Queries (a research paper describing our results is currently in the works, but the implementation pattern can be explored in our open-source codebase). ARQs introduce reasoning blueprints that help the model maintain coherence throughout long completions by dynamically refocusing attention on key instructions at strategic points in the completion process, continuously preventing LLMs from diverging into incoherence. Using them, we’ve been able to maintain a large test suite that exhibits close to 100% consistency in generating correct completions for complex tasks.

This technique allows us to achieve much higher accuracy in AI-driven reasoning and instruction-following, which has been critical for us in enabling reliable and aligned customer-facing applications.

Autoregressive Models Are Here to Stay

We think autoregressive LLMs are far from doomed. While long-form coherence is a challenge, assuming an exponentially compounding error rate ignores key mechanisms that mitigate divergence—from Chain-of-Thought reasoning to structured reasoning like ARQs.

If you’re interested in AI alignment and increasing the accuracy of chat agents using LLMs, feel free to explore Parlant’s open-source effort. Let’s continue refining how LLMs generate and structure knowledge.

Disclaimer: The views and opinions expressed in this guest article are those of the author and do not necessarily reflect the official policy or position of Marktechpost.

The post Are Autoregressive LLMs Really Doomed? A Commentary on Yann LeCun’s Recent Keynote at AI Action Summit appeared first on MarkTechPost.