While deeply researching a Main Conference paper published at ACL (a top conference in Computational Linguistics) recently, something both amusing and frustrating happened to me. To develop my new paper framework, I meticulously went through the paper’s open-source GitHub code and raw data distribution. As a result, I discovered a glaring, basic flaw that even the three original reviewers missed: this paper, which claims to evaluate “financial reasoning ability,” got even basic addition and subtraction wrong! The main issue was: “The total number of questions N claimed by the paper did not equal the sum of its listed components A + B.” Simply put, the authors confidently detailed the data sources in both the main text and the appendix, yet these two numbers, when added together, surprisingly did not match the total number they repeatedly emphasized in the abstract, main text, and figures.
When I wrote to the first author to verify this, they very sincerely apologized and admitted it was a typo, thanking me for helping them clarify the data consistency.
This incident provided me with two key insights: 1️⃣ Do not blindly trust authority (Critical Thinking): Even papers published at top-tier conferences and peer-reviewed can contain extremely basic errors. If I had chosen to “mentally rationalize” this number back then, my experimental baseline would have been skewed from the very beginning.
2️⃣ Why we need validation frameworks: The theme of this paper is “financial numerical reasoning,” yet the authors themselves stumbled on numerical reasoning. This is precisely the research topic I care about—we need a more rigorous, Human-in-the-Loop mechanism to prevent AI (or even human authors) from “guessing intentions” or generating logical hallucinations in such high-stakes domains.
I appreciate the author’s honest response; this discrepancy of 90 questions unexpectedly became my ticket to establishing an academic connection with a leading researcher. Moving forward, with this “spirit of skepticism,” I will continue to sharpen my validation framework. 🛠️ #AcademicIntegrity #ACL2025 #LLM #DataScience #FactCheck #NTU