The Pitfalls of Reasoning Models in Factual Tasks: Lessons from DeepSeek R1

The Pitfalls of Reasoning Models in Factual Tasks: Lessons from DeepSeek R1 and OpenAI O1

Large Language Models (LLMs) have made remarkable strides in natural language understanding, offering everything from chatbots that mimic human conversation to sophisticated tools that can compose essays, explain scientific concepts, or provide programming help.

At their core, these models rely on patterns in huge text datasets to predict the most likely next word or phrase, allowing them to emulate reasoning, discuss complex ideas, and even generate creative narratives.

However, these models can sometimes go astray when it comes to factual accuracy—especially around real-world events or numbers. In this post, we’ll explore the pitfalls of LLMs that appear to “reason” about factual content but end up mixing up or misrepresenting critical details, using two example models: R1 and O1.

I chose the 2020 election because it’s both controversial and well-documented, making it the perfect test to see how a model’s “reasoning” handles a high-stakes but firmly established factual event.

When “Reasoning” Goes Wrong: A Look at R1

The R1 model is heralded for its strong reasoning abilities. It can weave together threads of logic, recall context, and draw connections between concepts—at least superficially.

However, it made a glaring mistake when asked about the 2020 U.S. presidential election: it claimed that Joe Biden won the popular vote but did not secure the most electoral votes.

In reality, the 2020 election results are well-documented: Biden received 306 electoral votes to Trump’s 232, meaning Biden won both the popular vote and the Electoral College.

Why did R1 stumble so badly?

A likely cause is that R1 latched onto a familiar narrative in U.S. elections: occasionally, a candidate wins the popular vote yet loses the electoral vote (as happened in 2000 and 2016). Despite R1’s adeptness at “reasoning,” it incorrectly blended that general scenario with the 2020 election’s specifics.

This phenomenon illustrates our prodigal problem with hallucination, where an LLM conjures plausible-sounding but incorrect statements (Refer to my earlier post on this topic).

Now, let’s go with the examples from the Attached Screenshots (and… The Bigger Issue)

Screenshot 1:

R1 tried to summarize the 2020 U.S. presidential election, stating that Joe Biden won the popular vote but lost the electoral vote—an outright contradiction of reality. It conflated an often-cited scenario in U.S. politics (popular vote winners losing elections) with what happened in 2020

R1 and its reasoning

Screenshot 2:

Another model, O1, provided a succinct breakdown of electoral and popular votes (306 vs. 232 electoral votes, 81 million vs. 74 million popular votes), matching official records.Its precision underscores that sometimes being straightforwardly factual is more valuable than grandiose “reasoning.”

O1 outcomes

Issue with Reasoning on Facts:

R1’s logical leaps seem impressive at first glance, but when it pieces together fragments of real-world knowledge, the results can be riddled with inaccuracies.

Models like R1 don’t inherently “know” facts; instead, they stitch together likely-sounding prose, which can be dangerously misleading for anyone seeking exact information.

A Bad Scenario in Business: Imagine a high-stakes scenario where a major corporation deploys R1 to reason about quarterly financial statements. The model might combine partial data—such as old earnings reports and anecdotal indicators of market trends—into a polished-sounding narrative.

If executives rely on these spurious analytics to drive strategic decisions (e.g., pulling funding from profitable divisions or investing heavily in a failing product line), the outcome could be not just a blow to the company’s finances but also a severe dent in the brand reputation.

Worse, employees and investors, too, suffer the fallout from decisions made on misguided “reasoning.”

A Strong Warning Against Using R1 for Many Use Cases

Use Case #1: Critical Legal Interpretation

When the stakes involve laws, contracts, or courtroom decisions, misinformation can spark disastrous results. If R1 “reasons” that a legal precedent applies when it doesn’t, that error might cost clients money, freedom, or justice.

Use Case #2: Health and Medical Advice

A public-facing healthcare chatbot powered by R1 might produce advice that feels logical yet is entirely incorrect—potentially endangering patient health if vital steps or warnings are missing.

Use Case #3: News Fact-Checking

Journalists or newsrooms relying on R1 to swiftly produce headlines risk spreading falsehoods, especially around breaking events that require pinpoint accuracy. Even a slight distortion can rapidly multiply across social media, damaging public trust and the outlet’s credibility.

In all these scenarios, R1’s coherent reasoning style can lull people into accepting flawed results without cross-verification.

Let's review the note: Where to Use REASONING vs. Where NOT To

Models that excel at reasoning, like R1, can be phenomenal for brainstorming, creativity, and hypotheticals. If you need a detailed conceptual explanation of a philosophy text or an imaginative outline for a fiction novel, R1’s knack for connecting ideas and forming narratives can shine. In these creative contexts, small inaccuracies don’t necessarily do real harm—they may even spur further innovation or dialogue.

However, in domains that demand strict factual correctness—such as legal judgments, medical diagnostics, financial auditing, or official announcements—using a purely “reasoning-driven” LLM is ill-advised. In these spheres, the danger is not just a minor embarrassment but potentially large-scale harm: flawed lawsuits, health risks, financial losses, or grave public misinformation. Imagine an LLM acting as a judge in a court of law; the repercussions of even a single flawed inference or a misread precedent could tarnish the justice system for real people’s lives.

Ultimately, reason-first language models have their place: exploring ideas, summarizing complex arguments, or generating creative text. But in any scenario where facts must be unimpeachable, a more grounded approach—cross-verification of sources, narrower domain specificity, or a simpler retrieval-based model—should take precedence. The power of these advanced LLMs is undeniable, yet it must be wielded with caution, ensuring that reasoning never overshadows the unyielding need for verifiable truth.