[AI Papers] Why Language Models Cant Say I Dont Know and ChainShifts Answer

Beyond Correct Answers, to Trust: A New Paradigm Proposed by ChainShift in the Era of AI Hallucination 

"If we could tell AI it could be wrong, the world would be much more trustworthy." 

OpenAI's research findings, published in September 2025, exposed the deep shadow of AI, which we had blindly trusted. It was a shocking analysis that the phenomenon of "hallucination" in language models was not simply a technical error, but fundamentally a problem with the "systemic design structure." OpenAI pointed out that current benchmark tests encourage AI to guess, and that the evaluation system, which awards higher scores for guessing correctly than for answering "I don't know," is the main cause of hallucinations. [ Why Language Models Hallucinate ] This calls for a fundamental rethinking of how language models are designed and evaluated, and suggests that we must move beyond the paradigm of chasing the "right answer" and move toward an era of building "trust." In this rapidly changing AI paradigm, search engine optimization services like Chainshift will play a key role in overcoming the AI ​​trust crisis and setting new standards. 

Why Do Language Models Lie? 

The phenomenon of language models occasionally generating plausible, but inaccurate, information is called "hallucination." What is the root cause of this phenomenon? According to OpenAI's analysis, the core reason lies in the AI ​​training method for language models. 

Current AI training methods are structured like exams, with a "correct" or "incorrect" answer. A key issue here is that "I don't know" (IDK) is either absent or receives the lowest score. In other words, models are encouraged to generate plausible, even incorrect, answers rather than simply saying "I don't know" even in uncertain situations. This is because answering something is more advantageous than admitting "I don't know" when it comes to scoring. 

 OpenAI analyzed that current benchmark tests encourage AI to guess, and pointed out that the evaluation system, which gives higher scores for guessing correctly than for saying "I don't know," is the primary cause of hallucinations. Currently, most language model evaluations focus on accuracy, with "correct" answers receiving points and "I don't know" (IDK) receiving zero points. This evaluation method encourages AI to guess answers even in uncertain situations. Answering incorrectly is more rewarding than admitting "I don't know." 

To understand this, consider the hypothetical example of "Adam Kalai's birthday." If the AI ​​hasn't learned enough about Adam Kalai, it wouldn't know his exact birthday. However, because answering "I don't know" earns zero points, the AI ​​can combine birthday information from other people in the training data or general patterns to come up with a plausible date. Even if this answer is incorrect, it doesn't lose points compared to saying "I don't know," and there's a chance it might even be considered "correct" (if it's a coincidence), so it's forced to lie. 

The Problem Isn't the Data, It's the "Test" 

Language model training can be broadly divided into two stages: pre-training and post-training. In the pre-training stage, a vast amount of text data is learned to understand the patterns and structure of language. In the post-training stage, the model is fine-tuned to perform a specific task, and its performance is evaluated during this process. This evaluation method during the post-training stage is identified as a key cause of hallucination. 

Most evaluation systems award a point if the model provides the "correct" answer to a question, and a point of zero if the answer is "incorrect" or "I don't know." This "test" structure forces the model to answer even if it's uncertain. If honestly admitting "I don't know" earns a point of zero, then providing a plausible incorrect answer is psychologically preferable (from the model's training goals perspective). Of course, lack of data or errors can be a problem, but the current evaluation method, which prevents AI from saying "I don't know" when it doesn't know, is the root cause of hallucination. 

Solution: Designing an Environment Where AI Doesn't Have to Lie 

As a practical solution to hallucination, a persistent problem in AI, OpenAI proposed creating an environment where AI can "don't have to lie." The core of this concept is that the model should choose whether to answer or not, and that choice should be based on its level of confidence. This is called "Confidence Threshold Prompting." This technology allows AI to assess its own confidence level when generating answers, thereby filtering out unreliable answers. In other words, AI assigns its own confidence score to each element of the answer, and if the score falls below a preset confidence threshold, it determines that the answer may be inaccurate. In this case, the AI ​​expresses honest uncertainty instead of confident errors by honestly saying, "I don't know," restructuring the answer, or asking the user for additional information. 

This concept has evolved beyond a simple technical solution into a business strategy. This is the emergence of "Generative Engine Optimization (GEO)." GEO refers to all activities that optimize generative AI engines like ChatGPT, Perplexity, and Google AI Overview to cite and leverage a specific brand's content as a trusted source when generating answers. This fundamentally differs from traditional SEO (search engine optimization) in its goals and strategies. While SEO aims to attract users to a website, GEO focuses on securing new visibility and trustworthiness in the AI ​​era by incorporating brand content into AI-generated responses. This presents a new paradigm, enabling brands to deliver their information to users in a reliable manner through AI in an era where AI-generated responses themselves become the primary information acquisition channel for users. 

Chainshift's Application Strategy: Designing GEO for "Honest AI" 

Chainshift is a GEO/AEO (AI Engine Optimization) company that innovatively redesigns traditional SEO concepts for the era of generative AI. Chainshift is establishing itself as a leading expert group in the GEO field. Recently, Chainshift has developed a solution that induces "hallucination-free responses" in sentiment analysis, focusing on increasing the accuracy and reliability of AI-generated information. This unrivaled expertise has been proven through successful collaborations with leading domestic and international companies. Furthermore, with investment from global accelerator Antler, its expertise and technological prowess are recognized globally. ChainShift, a trusted partner in designing "honest AI," helps companies gain a competitive edge in the search environment of the AI ​​era. (Source: ChainShift Official Website ) 

Trust-based Prompt Design 

ChainShift meticulously designs prompts and content to prioritize reliable sources, such as objective data and official statistics, when generating AI answers. This is a key strategy that ensures AI answers are factually accurate, not mere guesses. SEO SEARCH JOURNAL As mentioned in , this design encourages AI to actively utilize externally verified information rather than rely solely on its own training data, resulting in improved factual accuracy and reliability of AI answers. 

New Evaluation Metrics Design 

Going beyond the traditional simple accuracy measurement method, ChainShift develops and applies new metrics that comprehensively evaluate AI answers for factors such as ▲factual basis ▲source reliability ▲logical flow. As highlighted in the Deep Sales Blog , these multifaceted evaluation metrics serve as a powerful mechanism that encourages AI to go beyond mere information listing and generate high-quality, insightful answers. This in turn motivates AI systems to provide higher-quality, ethical, and factual answers. 

✅ Result: Significantly improves the overall quality and deep trustworthiness of AI answers. 

Feedback-driven GEO Loop 

ChainShift builds a loop that continuously improves its Generative Engine Optimization (GEO) strategy by analyzing user feedback and AI response data in real time. This, as described in The Digital MKT , maximizes the ability of AI systems to learn and evolve from user experiences, enabling them to proactively respond to changing information environments and user expectations. It is a dynamic system that continuously optimizes AI performance and reliability through user participation. 

Conclusion: Now is the era of "trust," not "correct answers." 

AI hallucination is not simply a technical flaw; it is the result of a system design that constantly demands the "correct answer" and forces users to guess even in uncertain situations. We now face a new paradigm: moving beyond the narrow focus of improving accuracy and designing a "trust structure" that allows AI to honestly say "I don't know" when uncertain. 

AI's hallucinations weren't simply a technical flaw; they were the result of a system design that constantly demanded the "right answer" and encouraged guesswork even in uncertain situations. We now face a new paradigm: moving beyond the narrow focus of improving accuracy and designing a "trust structure" that allows AI to honestly say "I don't know" when uncertain. 

Trustworthy AEO and GEO diagnosis and analysis cannot be achieved with subjective metrics that cannot be normalized, such as intent and other brand metrics, or with AI trained on data of unknown origin or brand-centric personas. 

At a time when the most important value in the AI ​​era has shifted from "correct answers" to "trust," Chainshift is leading the way in building true trust by fundamentally redesigning the AI ​​response structure itself, going beyond simple generative engine optimization (GEO) consulting and SaaS (Software as a Service). 

With proprietary technologies such as "proprietary real-time AI visibility monitoring technology" and "trust threshold-based prompting," and innovative approaches, Chainshift maximizes the reliability of AI-provided information and is a true "trusted partner" ushering in a new era of AI. 

Chainshift Anna © 2025 ChainShift. All rights reserved. Unauthorized reproduction and redistribution prohibited.