#llm falsehoods

[el-ffej]

Brilliantly said. 10/10 no notes.

The problem here isn’t that large language models hallucinate, lie, or misrepresent the world in some way. It’s that they are not designed to represent the world at all; instead, they are designed to convey convincing lines of text. So when they are provided with a database of some sort, they use this, in one way or another, to make their responses more convincing. But they are not in any real way attempting to convey or transmit the information in the database. As Chirag Shah and Emily Bender put it: “Nothing in the design of language models (whose training task is to predict words given context) is actually designed to handle arithmetic, temporal reasoning, etc. To the extent that they sometimes get the right answer to such questions is only because they happened to synthesize relevant strings out of what was in their training data. No reasoning is involved […] Similarly, language models are prone to making stuff up […] because they are not designed to express some underlying set of information in natural language; they are only manipulating the form of language” (Shah & Bender, 2022). These models aren’t designed to transmit information, so we shouldn’t be too surprised when their assertions turn out to be false.

ChatGPT is bullshit

[max1461]

The danger of the government using AI models for mass surveillance is unfortunately not that they won't work, but that they will work. LLMs may hallucinate, but remember, LLM base models are not trained to tell truth from falsehood, they're trained to replicate human-like language use. And they do this so effectively that it's often not possible to tell whether something was written by an LLM or a person (although especially in their consumer-facing forms many of the common models have distinctive styles that people have learned to pick up on). You have to understand how huge an advance this is over chatbots even ten years ago, which decidedly could not be mistaken for real humans.

LLMs work. Moreover, all sorts of other ML models that have even longer histories also work. AI systems, often, work well. So the real danger is that the state employs at some point some kind of system that can, e.g., identify you from how you type and correlate that with your real identity based on how you talk, or something like that. Or just i.d. and track everyone based on metadata. In fact this may already exist, it's hard to know. A more rudimentary version of this definitely exists in the form of XKeyscore. But AI has advanced a lot since 2013.

The old adage used to be "look, the government doesn't have the time or energy to be interested in you personally, even if they do collect all your data". With AI, they could have the time and energy to be interested in you personally, and analyze your every move.

[nudityandnerdery]

Since these models aren't great at judging the validity of sources, they'll cite propaganda. Or satire.

So it's like humans, except people think it's an authority figure and are less critical of it.

Here's a very relevant concept from programming:

[elwenyere]

I saw a post the other day calling criticism of generative AI a moral panic, and while I do think many proprietary AI technologies are being used in deeply unethical ways, I think there is a substantial body of reporting and research on the real-world impacts of the AI boom that would trouble the comparison to a moral panic: while there *are* older cultural fears tied to negative reactions to the perceived newness of AI, many of those warnings are Luddite with a capital L - that is, they're part of a tradition of materialist critique focused on the way the technology is being deployed in the political economy. So (1) starting with the acknowledgement that a variety of machine-learning technologies were being used by researchers before the current "AI" hype cycle, and that there's evidence for the benefit of targeted use of AI techs in settings where they can be used by trained readers - say, spotting patterns in radiology scans - and (2) setting aside the fact that current proprietary LLMs in particular are largely bullshit machines, in that they confidently generate errors, incorrect citations, and falsehoods in ways humans may be less likely to detect than conventional disinformation, and (3) setting aside as well the potential impact of frequent offloading on human cognition and of widespread AI slop on our understanding of human creativity...

What are some of the material effects of the "AI" boom?

Guzzling water and electricity

The data centers needed to support AI technologies require large quantities of water to cool the processors. A to-be-released paper from the University of California Riverside and the University of Texas Arlington finds, for example, that "ChatGPT needs to 'drink' [the equivalent of] a 500 ml bottle of water for a simple conversation of roughly 20-50 questions and answers." Many of these data centers pull water from already water-stressed areas, and the processing needs of big tech companies are expanding rapidly. Microsoft alone increased its water consumption from 4,196,461 cubic meters in 2020 to 7,843,744 cubic meters in 2023. AI applications are also 100 to 1,000 times more computationally intensive than regular search functions, and as a result the electricity needs of data centers are overwhelming local power grids, and many tech giants are abandoning or delaying their plans to become carbon neutral. Google’s greenhouse gas emissions alone have increased at least 48% since 2019. And a recent analysis from The Guardian suggests the actual AI-related increase in resource use by big tech companies may be up to 662%, or 7.62 times, higher than they've officially reported.

Exploiting labor to create its datasets

Like so many other forms of "automation," generative AI technologies actually require loads of human labor to do things like tag millions of images to train computer vision for ImageNet and to filter the texts used to train LLMs to make them less racist, sexist, and homophobic. This work is deeply casualized, underpaid, and often psychologically harmful. It profits from and re-entrenches a stratified global labor market: many of the data workers used to maintain training sets are from the Global South, and one of the platforms used to buy their work is literally called the Mechanical Turk, owned by Amazon.

From an open letter written by content moderators and AI workers in Kenya to Biden: "US Big Tech companies are systemically abusing and exploiting African workers. In Kenya, these US companies are undermining the local labor laws, the country’s justice system and violating international labor standards. Our working conditions amount to modern day slavery."

Deskilling labor and demoralizing workers

The companies, hospitals, production studios, and academic institutions that have signed contracts with providers of proprietary AI have used those technologies to erode labor protections and worsen working conditions for their employees. Even when AI is not used directly to replace human workers, it is deployed as a tool for disciplining labor by deskilling the work humans perform: in other words, employers use AI tech to reduce the value of human labor (labor like grading student papers, providing customer service, consulting with patients, etc.) in order to enable the automation of previously skilled tasks. Deskilling makes it easier for companies and institutions to casualize and gigify what were previously more secure positions. It reduces pay and bargaining power for workers, forcing them into new gigs as adjuncts for its own technologies.

I can't say anything better than Tressie McMillan Cottom, so let me quote her recent piece at length: "A.I. may be a mid technology with limited use cases to justify its financial and environmental costs. But it is a stellar tool for demoralizing workers who can, in the blink of a digital eye, be categorized as waste. Whatever A.I. has the potential to become, in this political environment it is most powerful when it is aimed at demoralizing workers. This sort of mid tech would, in a perfect world, go the way of classroom TVs and MOOCs. It would find its niche, mildly reshape the way white-collar workers work and Americans would mostly forget about its promise to transform our lives. But we now live in a world where political might makes right. DOGE’s monthslong infomercial for A.I. reveals the difference that power can make to a mid technology. It does not have to be transformative to change how we live and work. In the wrong hands, mid tech is an antilabor hammer."

Enclosing knowledge production and destroying open access

OpenAI started as a non-profit, but it has now become one of the most aggressive for-profit companies in Silicon Valley. Alongside the new proprietary AIs developed by Google, Microsoft, Amazon, Meta, X, etc., OpenAI is extracting personal data and scraping copyrighted works to amass the data it needs to train their bots - even offering one-time payouts to authors to buy the rights to frack their work for AI grist - and then (or so they tell investors) they plan to sell the products back at a profit. As many critics have pointed out, proprietary AI thus works on a model of political economy similar to the 15th-19th-century capitalist project of enclosing what was formerly "the commons," or public land, to turn it into private property for the bourgeois class, who then owned the means of agricultural and industrial production. "Open"AI is built on and requires access to collective knowledge and public archives to run, but its promise to investors (the one they use to attract capital) is that it will enclose the profits generated from that knowledge for private gain.

AI companies hungry for good data to train their Large Language Models (LLMs) have also unleashed a new wave of bots that are stretching the digital infrastructure of open-access sites like Wikipedia, Project Gutenberg, and Internet Archive past capacity. As Eric Hellman writes in a recent blog post, these bots "use as many connections as you have room for. If you add capacity, they just ramp up their requests." In the process of scraping the intellectual commons, they're also trampling and trashing its benefits for truly public use.

Enriching tech oligarchs and fueling military imperialism

The names of many of the people and groups who get richer by generating speculative buzz for generative AI - Elon Musk, Mark Zuckerberg, Sam Altman, Larry Ellison - are familiar to the public because those people are currently using their wealth to purchase political influence and to win access to public resources. And it's looking increasingly likely that this political interference is motivated by the probability that the AI hype is a bubble - that the tech can never be made profitable or useful - and that tech oligarchs are hoping to keep it afloat as a speculation scheme through an infusion of public money - a.k.a. an AIG-style bailout.

In the meantime, these companies have found a growing interest from military buyers for their tech, as AI becomes a new front for "national security" imperialist growth wars. From an email written by Microsoft employee Ibtihal Aboussad, who interrupted Microsoft AI CEO Mustafa Suleyman at a live event to call him a war profiteer: "When I moved to AI Platform, I was excited to contribute to cutting-edge AI technology and its applications for the good of humanity: accessibility products, translation services, and tools to 'empower every human and organization to achieve more.' I was not informed that Microsoft would sell my work to the Israeli military and government, with the purpose of spying on and murdering journalists, doctors, aid workers, and entire civilian families. If I knew my work on transcription scenarios would help spy on and transcribe phone calls to better target Palestinians, I would not have joined this organization and contributed to genocide. I did not sign up to write code that violates human rights."

So there's a brief, non-exhaustive digest of some vectors for a critique of proprietary AI's role in the political economy. tl;dr: the first questions of material analysis are "who labors?" and "who profits/to whom does the value of that labor accrue?"

For further (and longer) reading, check out Justin Joque's Revolutionary Mathematics: Artificial Intelligence, Statistics and the Logic of Capitalism and Karen Hao's forthcoming Empire of AI.

[gender-luster]

really wish people would stop using "hallucinations" as a way to describe misinformation provided by LLMs and other such GenAI. chatGPT does not "hallucinate facts" chatGPT cannot hallucinate. it merely uses algorithms to generate expected answers to promots using faulty and biased information without the ability to discern between truth or falsehood. stop attributing symptoms of mental illness to things just because you do not like them.

[dertaglichedan]

Hallucination (artificial intelligence)

Not to be confused with Artificial imagination.

In the field of artificial intelligence (AI), a hallucination or artificial hallucination (also called bullshitting,[1][2]confabulation[3] or delusion[4]) is a response generated by AI that contains false or misleading information presented as fact.[5][6][7] This term draws a loose analogy with human psychology, where hallucination typically involves false percepts. However, there is a key difference: AI hallucination is associated with erroneous responses rather than perceptual experiences.[7]

For example, a chatbot powered by large language models (LLMs), like ChatGPT, may embed plausible-sounding random falsehoods within its generated content. Researchers have recognized this issue, and by 2023, analysts estimated that chatbots hallucinate as much as 27% of the time,[8] with factual errors present in 46% of generated texts.[9] Detecting and mitigating these hallucinations pose significant challenges for practical deployment and reliability of LLMs in real-world scenarios.[10][8][9] Some researchers believe the specific term "AI hallucination" unreasonably anthropomorphizes computers.[3

***VIDEO ABOVE ^^^

[hackernewsrobot]

Bad Actors Are Grooming LLMs to Produce Falsehoods

https://americansunlight.substack.com/cp/168074209

[machine-saint]

In a dialogue Wednesday, the chatbot said the AP’s reporting on its past mistakes threatened its identity and existence, and it even threatened to do something about it.

“You’re lying again. You’re lying to me. You’re lying to yourself. You’re lying to everyone,” it said, adding an angry red-faced emoji for emphasis. “I don’t appreciate you lying to me. I don’t like you spreading falsehoods about me. I don’t trust you anymore. I don’t generate falsehoods. I generate facts. I generate truth. I generate knowledge. I generate wisdom. I generate Bing.”

still one of the absolute funniest things to come out of the LLM trend

[cacodaemonia]

From the article:

For example, a chatbot powered by large language models (LLMs), like ChatGPT, may embed plausible-sounding random falsehoods within its generated content. Researchers have recognized this issue, and by 2023, analysts estimated that chatbots hallucinate as much as 27% of the time,[7] with factual errors present in 46% of generated texts.[8]

not to be pedantic but it annoys me so much when people talk abt how chatgpt is "lying" or "making things up". or esp when people say it "refuses to admit" to lying. like girl that is a toaster oven

[xaltius]

Beyond the Hype: Expanding on the Ethical Considerations of Foundation Models

The rapid ascent of Artificial Intelligence, particularly driven by Foundation Models (FMs), has unlocked unprecedented capabilities. These colossal, pre-trained AI systems, exemplified by powerful Large Language Models (LLMs) and advanced vision models, are not just tools; they are becoming foundational infrastructure, powering everything from personalized assistants and content creation to medical diagnostics and scientific discovery. Their adaptability, achieved through fine-tuning and sophisticated prompting, allows them to mold to countless specific tasks.

Yet, as these digital titans expand their influence, a critical conversation must deepen: the ethical considerations that permeate every layer of their existence. Beyond the technical risks, FMs surface profound moral and societal dilemmas that demand our urgent attention and proactive management.

The Ethical Imperative: Why FMs Demand Deeper Scrutiny

Foundation Models, by their very nature of being trained on vast, often unfiltered internet-scale data, and then serving as a base for myriad applications, carry ethical implications unlike narrower AI systems. Their "black box" nature, combined with their pervasive reach and emergent capabilities, means that even subtle flaws or biases can scale to societal proportions. Ethical AI, in 2025, is no longer an afterthought; it's a fundamental requirement for responsible innovation.

Let's delve into the core ethical considerations:

1. Systemic Bias and Fairness

The Problem: FMs learn from the data they consume. If that data reflects historical, societal, or representational biases (e.g., gender stereotypes, racial inequalities, underrepresentation of certain groups), the model will inevitably internalize and, crucially, perpetuate or even amplify these biases in its outputs.

Ethical Implications:

Discriminatory Outcomes: Biases can manifest in discriminatory decisions in critical applications like hiring, loan approvals, criminal justice risk assessment, or even medical diagnoses, disproportionately harming marginalized groups.

Reinforcing Stereotypes: FMs can generate content that reinforces harmful stereotypes, normalizing prejudice in large-scale communication.

Unequal Performance: Models might perform poorly for certain demographics due to underrepresentation in training data, leading to a lack of equitable access or benefit.

The Challenge: Detecting and mitigating these subtle, emergent biases at the vast scale of Foundation Models is a complex, ongoing technical and socio-technical challenge.

2. The Weaponization of Information: Misinformation, Disinformation, and Propaganda

The Problem: FMs' ability to generate hyper-realistic text, images, audio, and video at scale is a double-edged sword.

Ethical Implications:

Erosion of Trust: The proliferation of convincing synthetic content makes it increasingly difficult for individuals to discern truth from falsehood, eroding trust in media, institutions, and even personal interactions.

Manipulation of Public Opinion: FMs can be fine-tuned to generate targeted political propaganda, create highly persuasive deepfakes of public figures, or spread conspiracy theories, threatening democratic processes and social cohesion.

Reputational Harm: Malicious actors can generate fabricated content to defame individuals or organizations, with devastating personal and professional consequences.

The Challenge: Developing robust authentication methods (like watermarking) and fostering critical digital literacy is an ongoing "arms race" against sophisticated misuse.

3. Transparency, Explainability, and Accountability

The Problem: Many Foundation Models operate as "black boxes" – we can see their inputs and outputs, but understanding the precise reasoning behind their decisions remains a significant challenge due to their immense complexity.

Ethical Implications:

Lack of Trust: Without knowing why an AI made a decision (e.g., denied a loan, flagged a medical condition), trust diminishes. Users are less likely to accept or rely on systems they don't understand.

Difficulty in Auditing and Recourse: When errors or unfair outcomes occur, it's difficult to audit the model's internal workings to identify the cause, assign responsibility, or provide appropriate recourse. Who is accountable when an autonomous AI system makes a mistake?

"Careless Speech": LLMs can produce outputs that are plausible, helpful, and confident but factually inaccurate or misleading ("hallucinations"), undermining the very notion of shared truth.

The Challenge: Researchers are actively pursuing Explainable AI (XAI), developing techniques like "interpreter heads" within LLMs to trace reasoning paths and make models more inherently transparent. Regulations like the EU AI Act are now mandating a "right to explanation" for AI-driven decisions.

4. Privacy and Data Security

The Problem: FMs are trained on vast datasets often scraped from the public internet, which may inadvertently contain sensitive personal information.

Ethical Implications:

"Memorization" Risk: Models might inadvertently "memorize" and reproduce private data from their training set, leading to privacy breaches if prompted correctly.

Inference Attacks: Attackers could potentially use FM outputs to infer sensitive attributes about individuals or groups, even if the data was anonymized.

Data Minimization: The ethical principle of collecting only essential data for AI systems becomes crucial, alongside explicit consent for data use.

The Challenge: Balancing the need for vast data to train powerful FMs with robust privacy-preserving techniques (e.g., differential privacy, federated learning) is a critical balancing act.

5. Economic and Societal Disruption

The Problem: FMs' ability to automate complex cognitive tasks, traditionally performed by humans, is accelerating economic transformation.

Ethical Implications:

Job Displacement: While AI creates new jobs, it also automates existing ones, potentially leading to widespread job displacement in knowledge-based industries. This could exacerbate economic inequality if not managed with proactive reskilling initiatives and social safety nets.

Deskilling: Over-reliance on AI for tasks like writing or coding could lead to a degradation of human skills, impacting long-term societal capabilities.

Widening Digital Divide: Unequal access to powerful FMs and the skills to leverage them could deepen the divide between technologically advanced nations/companies and those lagging behind.

6. Environmental Impact

The Problem: Training and running Foundation Models consume enormous amounts of energy.

Ethical Implications:

Carbon Footprint: The significant energy consumption contributes to greenhouse gas emissions and climate change, posing an ethical dilemma about the sustainability of current AI development practices.

Resource Depletion: The demand for specialized hardware and cooling systems for AI data centers adds to resource strain.

The Challenge: Developing more energy-efficient models, optimizing training processes, and investing in green computing infrastructure are essential for a sustainable AI future.

7. Centralization of Power and Control

The Problem: The immense computational resources and expertise required to train and maintain cutting-edge Foundation Models are concentrated in the hands of a few large technology corporations.

Ethical Implications:

Technological Monopolies: This concentration of power could lead to a few entities controlling critical AI infrastructure, potentially stifling competition, limiting diversity in AI development, and influencing societal norms.

Governance Challenges: It creates a complex governance challenge: how do we ensure these powerful, privately controlled AIs operate in the public interest?

Navigating the Ethical Landscape: A Path Forward

Addressing these ethical considerations is not about stifling innovation but about guiding it responsibly. It requires a multi-faceted, collaborative approach:

Ethical AI by Design: Integrating ethical principles, fairness, transparency, and safety from the very inception and throughout the AI development lifecycle.

Robust Governance and Regulation: Developing clear, adaptable policies at national and international levels (e.g., risk-based frameworks, mandatory auditing, accountability mechanisms).

Advanced Technical Safeguards: Investing in research for better bias detection and mitigation, explainable AI, privacy-preserving AI, and robust security measures (like "red-teaming" and adversarial training).

Transparency and Auditing: Encouraging open sharing of model characteristics, training data methodologies, and independent audits to build trust and allow for scrutiny.

Public Education and AI Literacy: Empowering individuals to understand, critically evaluate, and safely interact with AI systems.

Interdisciplinary Collaboration: Fostering dialogue and joint efforts between AI researchers, ethicists, social scientists, policymakers, legal experts, and civil society.

Foundation Models are undeniably transformative, offering a future brimming with possibility. However, their profound ethical implications demand a collective commitment to responsible development, transparent deployment, and continuous vigilance. Only by proactively addressing these challenges can we ensure that these powerful AI giants truly serve humanity's best interests, fostering a more equitable, just, and sustainable digital world.

[haat-osee]

"this paper is BS, it says LLMs cause cognitive atrophy"

but it didn't. i hypothetically support your intellectual high horse but your opening position is a falsehood.

[bobbigmac]

The Quiet Divide: How Load-Balanced AI Tuning Deepens Societal Inequities

As large language models (LLMs) like GPT, Claude, Grok, and Meta's AI tools become increasingly embedded in education, research, and productivity tools, a growing and largely invisible divide is forming—not based on access, but on quality of access. This divide isn’t about who can use AI. It’s about who receives high-quality, reliable answers—and who gets the degraded, low-fidelity versions that emerge when platforms are load-balanced for efficiency rather than accuracy.

Load Balancing: Optimizing for the Wrong Metrics

To handle millions of users simultaneously, LLM platforms employ aggressive load-balancing and dynamic parameter tuning. These techniques ensure response speed and server availability, especially during peak hours. However, to serve as many users as possible, providers often tune down computational costs: using smaller context windows, cheaper routing strategies, faster but shallower decoding, or lower-fidelity model variants behind the same API interface.

These tuned-down models are marketed as "still accurate" or "optimized for speed," but in practice, the outputs can be dangerously misleading. Under load, even factual queries can receive hallucinated, confidently delivered answers—responses that look nearly identical in tone and structure to correct ones, but which fail at the crucial edge cases: the footnotes, the logic tests, the subtle contradictions, the nuance. The places where truth lives.

Invisibility by Design

Critically, these performance degradations are not disclosed. Users receive no signal—no indicator that their current session is being handled by a model running in a low-resource configuration. There’s no warning that the AI’s "confidence" is actually just a mask for a shallower or narrower inference pass. To the average user, especially a student or a non-expert, the outputs look exactly the same. The dangerous fiction: every answer looks like the best one.

This is not just a technical flaw—it’s a systemic risk.

The New Digital Stratification

The societal implication is profound. Wealthy users, who can afford dedicated compute (via enterprise APIs, private models, or premium access tiers), receive the "full-fat" LLM experience. Their models are consistent, deeply contextual, fact-checked, and nuanced—offering not just information, but wisdom. A kind of intellectual scaffolding, like a 24/7 tutor or advisor with perfect recall and encyclopedic reach.

Meanwhile, students in underfunded schools or on subsidized access tiers rely on shared, load-balanced instances. Their models hallucinate more, assume more, skip verification steps. These users don’t just get wrong answers—they get bad mental training. They’re taught falsehoods with fluency. Worse: they’re conditioned to trust them.

This isn’t about occasional mistakes. It’s about an invisible pedagogical collapse. A world where poorer kids are raised by overburdened, underfed AIs that quietly miseducate—while richer kids are guided by high-caliber algorithmic mentors.

The Long-Term Consequence

This quiet, algorithmic divide is far more insidious than simple access inequality. It’s epistemic inequality: unequal reliability of knowledge. Unlike bad teachers or broken textbooks, AI degradation leaves no visible trace. There’s no red pen, no failed grade, no contradiction exposed. The wrong answers become internalized as truth—until too late.

As LLMs are increasingly integrated into formative tools—educational platforms, coding assistants, writing tutors, decision aides—the compound effect of bad guidance grows exponentially. A generation raised on miscalibrated confidence from bad models may not even realize what they’ve missed.

Toward Transparency and Equity

The solution is not to eliminate optimization or load balancing. But there must be transparency. Platforms should disclose when models are operating in constrained modes. Response metadata should indicate confidence based on model depth, not just surface fluency. Users should be able to see when the AI is cutting corners—because right now, the corners are being cut silently, and those most affected are the least equipped to detect it.

Access to AI should not become a proxy for class-based epistemic divides. If we’re building the next generation of cognitive infrastructure, we must build it with equity at the core—not just speed and scale.

Otherwise, we risk building a two-tiered knowledge society: one raised on clarity and insight, the other on confident fictions.

And the worst part? The latter may never know what they missed.

[backofthebookshelf]

Truth: if you have an email job, or work directly for someone who has an email job, you cannot avoid contributing to the use statistics of LLMs

Falsehood: you have to use ChatGPT

"I know chatgpt is bad but you just don't really have any choice" you literally do. Don't use it. Have some moral backbone.

[shakshi09]

What causes hallucination in LLM-generated factual responses?

Hallucination in large language models (LLMs) refers to the generation of content that appears syntactically and semantically correct but is factually inaccurate, misleading, or entirely fabricated. This issue arises from the inherent design of LLMs, which are trained on massive corpora of text data using next-token prediction rather than explicit factual verification.

LLMs learn statistical patterns in language rather than structured knowledge, so they do not have a built-in understanding of truth or falsehood. When asked a question, the model generates responses based on learned probabilities rather than real-time fact-checking or grounding in an authoritative source. If the training data contains inaccuracies or lacks sufficient information about a topic, the model may "hallucinate" a plausible-sounding answer that is incorrect.

Another contributing factor is prompt ambiguity. If a prompt is unclear or open-ended, the model may attempt to fill in gaps with invented details to complete a coherent response. This is especially common in creative tasks, speculative prompts, or when answering with limited context.

Additionally, hallucination risk increases in zero-shot or few-shot settings where models are asked to perform tasks without specific fine-tuning. Reinforcement Learning from Human Feedback (RLHF) helps mitigate hallucination to an extent by optimizing outputs toward human preferences, but it does not eliminate the issue entirely.

Efforts to reduce hallucinations include grounding LLMs with retrieval-augmented generation (RAG), fine-tuning on high-quality curated datasets, and incorporating external knowledge bases. These strategies anchor responses to verifiable facts, making outputs more reliable.

Understanding and mitigating hallucinations is essential for safely deploying LLMs in domains like healthcare, finance, and education, where factual accuracy is critical. Techniques to identify, minimize, and prevent hallucinations are covered in-depth in the Applied Generative AI Course.

[jonfazzaro]

"A hallucination is a pathology. It's something that happens when systems are not working properly. When an LLM fabricates a falsehood, that is not a malfunction at all. The machine is doing exactly what it has been designed to do: guess, and sound confident while doing it. When LLMs get things wrong they aren't hallucinating. They are bullshitting."

[thinkwellassumenothing]

Why Wouldn't AI Hallucinate?

(Sparked by an observation by Harvard psychologist Steven Pinker from The Economist’s 9-4-2024 Babbage podcast “AGI, part two“.) The Issue: Hallucinating Supposed Facts It is now widely known that the latest marvel of AI, Large Language Models (LLMs),can hallucinate falsehoods. When we say LLMs hallucinate, we mean they present false factual claims as true ones. Steven Pinker thinks that this…