Tag: llm

Prompt Repetition As a Surprisingly Strong Baseline for Non-reasoning LLMs

A recent Google Research paper, Prompt Repetition Improves Non-Reasoning LLMs, makes a claim that feels almost too simple to be real: if you are running an LLM in a non-reasoning mode, you can often get better answers by duplicating the prompt verbatim before the model responds.

The trick

The transformation is exactly what it sounds like:

Baseline: <QUERY>

Repeat:   <QUERY><QUERY>

No extra instructions, no extra examples, no chain of thought prompting. Just the same prompt twice. See an example below from the paper.

What the authors found

Across a set of major models and a range of benchmarks, the authors report that prompt repetition is consistently helpful in the non-reasoning setting. They present results as head to head comparisons against the baseline prompt, and show broad improvement without changing the expected output format.

One detail that makes this especially practical is that they also examine latency and output length. The headline is that repeating the prompt generally does not increase the number of generated tokens, and in their experiments it usually does not meaningfully increase end to end latency either.

Why would this work

The paper’s explanation is rooted in a basic property of causal language models: tokens are processed left to right, and each position attends only to what came before it. This means the order of information in a prompt can matter more than we would like. If important details appear early, the model “saw” them before it knew what the final question would be. Repeating the prompt gives the model a second chance to integrate the whole request with the question now fully in view.

A nice way to interpret this is that prompt repetition is not adding new information. It is changing the geometry of attention by making the same information appear again later in the context window, closer to where the model must commit to an answer.

When it seems most useful

The effect should be strongest when prompts are long, structured, or easy to misread. The paper highlights cases like multiple choice formats where the placement of options and questions can create “unfriendly” ordering effects. Repetition helps smooth out those quirks because whatever was awkwardly positioned the first time is now encountered again.

They also introduce stress tests where the model must retrieve or locate items in long lists, and some of those show dramatic jumps with repetition.

What changes when reasoning is enabled

An important nuance is that these gains are mainly about non-reasoning usage. When the model is already encouraged to reason step by step, repetition tends to help less often, and many results become ties. The paper’s intuition is that reasoning style outputs often restate the problem anyway, which can partially mimic the benefit of repetition.

A practical takeaway for prompt design

If you are building an application where you want better performance without paying for longer responses, prompt repetition looks like a strong “cheap” baseline to try first. It is also a reminder that prompt engineering is not only about clever wording. Sometimes it is about controlling where information appears in the sequence so the model can reliably use it.

References

Leviathan, Yaniv, Matan Kalman, and Yossi Matias. “Prompt Repetition Improves Non-Reasoning LLMs.” arXiv (2025). (arXiv)

— Andrew

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MLRegTest: Stress-Testing Whether Models Learn Rules or Just Patterns

When people say “AI understands language,” they usually mean it can produce fluent text, summarize an article, or answer questions. Those abilities are impressive, but they can also hide a real problem: a model can look correct while relying on shortcuts that break in the exact cases we care about most.

That is why I have been interested in MLRegTest, a benchmark designed to stress-test sequence models using 1,800 carefully constructed regular languages. Instead of judging a model by how human its writing sounds, MLRegTest asks a simpler, sharper question: can a model learn a rule, and then apply it reliably when the test gets harder or more precise?

What is MLRegTest, in plain terms?

MLRegTest is a large collection of tiny, made-up “languages” built from simple symbols. Imagine an alphabet like A, B, C, D, and strings such as “AAB C” or “BBB A.” Each language has a hidden rule that determines whether a string belongs to it. The model learns from labeled examples and then answers a yes or no question: does this string follow the rule?

This might sound far from English or Spanish, but it is actually a powerful way to test something very relevant to computational linguistics: how models represent patterns and dependencies across sequences.

Why regular languages?

Regular languages are a class of formal languages that can be described using tools like regular expressions and finite-state machines. They are simpler than full human language, but they still capture many meaningful pattern constraints. MLRegTest uses regular languages because they let researchers control the task in a way that is difficult with natural text. The rules are fully known, the labels are unambiguous, and researchers can generate unlimited data under controlled conditions. That makes it possible to test specific kinds of generalization rather than only measuring how well a model matches the distribution of a dataset.

What makes MLRegTest different from typical benchmarks?

First, MLRegTest is not just one dataset. It is a suite of datasets drawn from 1,800 distinct regular languages, and those languages are organized by properties such as logical complexity and the kinds of constraints they express. That organization matters because “pattern learning” is not a single ability. Some rules are easy to approximate but hard to learn exactly, and some require models to track information across long spans of a sequence. MLRegTest is designed to probe those differences rather than hiding them inside one average score.

Second, the benchmark is built to examine long-distance dependencies in a controlled way. Sequence models often struggle when the relevant information is far apart in the input, and MLRegTest gives researchers a systematic way to test whether a model can handle that challenge.

Third, MLRegTest includes a kind of evaluation highlighted in Stony Brook’s write-up: border tests. These focus on edge cases where examples come in near-identical pairs. The strings might differ by only one symbol, but one is in the language and the other is not. Those are the cases where the true rule matters most, and they are also where shortcut strategies are most likely to fail. According to the Stony Brook announcement, models tended to struggle more on these boundary cases, even when they looked strong on more typical examples, which suggests that they can learn approximations instead of learning the rule itself.

What did the researchers evaluate?

The JMLR paper evaluates multiple neural architectures, including recurrent models and transformers, and reports that performance varies significantly depending on the kind of test set, the class of language, and the model architecture. That is useful because it pushes back on the idea that “a strong model is strong at everything.” MLRegTest makes it easier to ask where a system is strong and where it breaks, and to tie those results to specific properties of the pattern being learned.

Why this matters for evaluating language models

Even though MLRegTest does not test natural language directly, it targets a core issue in NLP evaluation: benchmarks can be “won” for the wrong reasons. A model can score well by picking up statistical hints that correlate with labels without learning the intended generalization. Border tests and other controlled generalization tests help researchers ask whether a model stays consistent when inputs shift in principled ways, whether it generalizes beyond the training regime, and whether it fails exactly when the rule becomes tight. Those questions matter if we want models that are dependable in real settings, especially when rare edge cases are the dangerous ones.

A quiet challenge to “just feed it more data”

MLRegTest also pushes back on a common assumption in AI right now: if a model struggles, the fix is simply more data. The benchmark is asking what happens if the deeper issue is not data quantity, but what the model is actually learning. This is not only a scientific concern but also a practical one. In high-stakes applications like robotic medical assistance or self-driving cars, the most serious situations are often rare. A particular combination of weather, road design, sensor noise, and unpredictable human behavior might occur only one in a million times. In medicine, a rare complication might be exactly the case where you cannot afford a mistake. The border tests connect directly to this idea because they emphasize edge cases where a tiny change can flip the correct decision, which is where shortcut learning becomes most dangerous.

The takeaway is simple: reliability is not the same thing as average performance. If a system only works well on patterns it has seen thousands of times, it may still be fragile in the exact scenarios we care about most. MLRegTest is valuable because it helps us measure that fragility directly instead of waiting to discover it in the real world.

A high school senior takeaway

As a high school senior interested in computational linguistics research, MLRegTest feels like a strong example of what careful evaluation looks like. It controls the task so we know what the model should learn, varies difficulty in interpretable ways so “harder” actually means something specific, and probes failure modes instead of stopping at one headline number. More broadly, it connects to a theme I keep coming back to in NLP: we do not just want systems that perform well. We want systems whose performance we can explain and trust.

References

  1. van der Poel, Sam, et al. “MLRegTest: A Benchmark for the Machine Learning of Regular Languages.” Journal of Machine Learning Research, vol. 25, no. 283, 2024, pp. 1–45. https://www.jmlr.org/papers/v25/23-0518.html
  2. Stony Brook University AI news announcement (February 13, 2026): “How Much Does AI Really Understand: Stress-testing Neural Networks with 1,800 Language Patterns.” https://ai.stonybrook.edu/about-us/News/how-much-does-ai-really-understand-stress-testing-neural-networks-1800-language

— Andrew

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How AI is Quietly Changing the Way We Talk

Introduction

In this blog post, I’d like to share recent findings suggesting AI is quietly reshaping the way we talk. You may be already aware of the trend of AI’s reshaping the way we write since the wide usage of ChatGPT and other LLM models in facilitating the text/script generation, particularly in writing research papers. See the discussion in my past blog post “Is the Increasing Trend of Leveraging LLMs like ChatGPT in Writing Research Papers Concerning?”.

Florida State University’s Study

A new study from Florida State University shows that large language models are starting to influence spoken language, not just written text. Researchers analyzed over 22 million words from unscripted science and tech podcasts, comparing episodes from before ChatGPT (2019–2021) with episodes after its release (2023–2025).

They found that words commonly used by AI models, such as “delve,” “boast,” and “meticulous,” are showing up more often in everyday conversation, while their close synonyms stayed flat.

The researchers call this phenomenon “lexical seepage,” where AI-preferred words gradually leak into the way people naturally talk.

How the Shift Happens

The study links this effect to psychology concepts like implicit learning and priming. People pick up on repeated words, even without realizing it, and then use them themselves. In other words, AI is not just helping us write. It may also be subtly shaping the way we speak. Importantly, the changes were observed in unscripted talk, in addition to formal speeches or scripted lectures.

Global Patterns and Concerns

This is not only happening in the U.S. A study in Germany found similar patterns on YouTube, suggesting the trend is global. Experts warn that if companies like OpenAI, Anthropic, and Google fine-tune their models in different ways, people might start adopting slightly different speech patterns. Over time, this could flatten dialects, erase regional slang, and reduce creativity. Some argue we need new benchmarks that push AI to use more diverse language instead of over-relying on the same set of words.

Natural Adoption vs. AI Amplification

The Florida State team also makes an important point: not everything can be pinned on AI.

“It is possible that these words have simply entered a phase of natural, rapid adoption, akin to the rise of expressions like ‘touch base,’ ‘dude,’ and ‘awesome’ in the mid-2000s.”

In this view, LLMs overuse words that were already becoming popular, but they still act as amplifiers that speed up language change. Even if AI is not the original source of these trends, the fact that machine-generated text can influence how humans speak is significant.

Final Thoughts

As a high school student, I find this both fascinating and a little worrying. On the one hand, it shows how powerful AI really is in shaping culture, not just technology. On the other hand, if AI makes everyone talk the same way, that could erase some of the creativity and uniqueness that makes language fun. Just like with social media, the full impact may take years to understand. For now, I think it’s important to keep asking questions about how AI is changing not just what we write, but also what we say.

Further Reading

  • “AI Is Quietly Reshaping the Way We Talk.” Fast Company, https://www.fastcompany.com/91398460/ai-is-quietly-reshaping-the-way-we-talk.
  • Anderson, Bryce, Riley Galpin, and Tom S. Juzek. Model Misalignment and Language Change: Traces of AI-Associated Language in Unscripted Spoken English. arXiv, 2025, doi:10.48550/arXiv.2508.00238.
  • Yakura, Hiromu, et al. Empirical Evidence of Large Language Model’s Influence on Human Spoken Communication. arXiv, 2024, doi:10.48550/arXiv.2409.01754.

— Andrew

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LATAM-GPT, Linguistic Bias, and Why Regional AI Infrastructure Matters

When a language model answers in fluent Spanish but misses local context, the problem is not grammar. The problem is representation.

That is the central issue behind linguistic bias in GPT-style systems, and it is why LATAM-GPT is such an important project for computational linguistics researchers. It pushes us to ask a better question than “Can the model generate text?” We should be asking, “Whose language realities are represented in the model?”

Linguistic bias is bigger than offensive outputs

In NLP conversations, bias is often reduced to toxic or stereotypical responses. That matters, but it is only part of the picture. Linguistic bias also includes structural imbalance: which dialects are present in training data, which cultural contexts are understood, and which institutions or histories are treated as central versus peripheral.

For many GPT-like systems, the imbalance starts at the data level. English and Global North content dominate much of the public web, so model behavior tends to be stronger when prompts align with those distributions. A model may produce polished Spanish or Portuguese and still flatten regional variation, miss sociolinguistic nuance, or rely on generic interpretations that do not fit local usage. AP’s reporting on LATAM-GPT directly frames the initiative as a response to this representational gap in mainstream AI systems.

Why regional models matter

Regional models like LATAM-GPT are not only technical artifacts. They are research infrastructure choices.

First, they can improve local relevance because the model is trained with region-specific data and priorities rather than treated as a generic multilingual extension of a primarily external corpus. AP reports that LATAM-GPT was developed specifically to better reflect Latin American language and context.

Second, regional models help build scientific and governance capacity. Reuters describes LATAM-GPT as a collaborative effort among countries and institutions in the region, which means expertise, evaluation norms, and deployment decisions are not fully outsourced.

Third, the initiative is positioned as open infrastructure for downstream applications, not just as another chatbot interface. That distinction matters for public-interest work in education, government services, and domain-specific NLP tools.

What the LATAM-GPT project is

Based on AP’s report, supported by Reuters and the official project site, LATAM-GPT is a regional open-source initiative led by The National Center of Artificial Intelligence of Chile (CENIA). AP reports early backing that included funding from CENIA and the Development Bank of Latin America (CAF), and references future training support tied to a major supercomputing investment in northern Chile. Reuters also notes cloud support in the development process.

The project is collaborative by design. AP reports participation from more than 30 institutions across eight countries, while Reuters presents a broader regional coalition narrative around deployment and adoption. The reported training pipeline includes large-scale data, combining partnership-based sources and synthetic data to improve coverage in underrepresented areas. Initial focus is on Spanish and Portuguese, with plans to expand toward Indigenous languages.

The timeline is also important. AP describes work beginning in 2023, public visibility increasing at the 2025 AI Action Summit, and launch reporting in February 2026.

Performance versus ChatGPT and Gemini

This part needs careful wording.

AP quotes project leadership saying LATAM-GPT can be more accurate and efficient for Latin American and Caribbean contexts because of regional data alignment. That is a meaningful claim and it fits the project’s objective.

At the same time, both AP and Reuters frame LATAM-GPT as not primarily intended to replace ChatGPT or Gemini as general-purpose consumer assistants. It is presented as foundational infrastructure for regional applications. Public reporting so far does not provide a single standardized benchmark table showing universal superiority over frontier global models across all task categories.

So the most responsible interpretation is this: LATAM-GPT’s strength is regional alignment and representational fit, not blanket dominance across every benchmark.

What this implies for a junior computational linguistics researcher

For early-stage researchers, LATAM-GPT signals an important shift in what counts as strong NLP work. Bigger model size is no longer the only story. Research quality increasingly depends on whether your data curation, evaluation design, and error analysis capture real linguistic diversity.

That has practical consequences. If you only run generic leaderboard-style evaluations, you may miss the most consequential failures. Region-aware testing, dialect-sensitive prompts, and sociolinguistic error taxonomies become central methods, not side tasks. Corpus documentation and annotation policy choices also become core contributions, because they shape what the model can and cannot represent.

In other words, this is an opportunity. You can build technically rigorous work while also addressing linguistic equity and real-world usefulness. LATAM-GPT makes that path visible: computational linguistics can be both advanced and locally grounded.

Final reflection

LATAM-GPT matters because it reframes AI development from pure model competition to language representation, participation, and research sovereignty. The key question is not whether it outperforms every major global model on every task. The key question is whether communities that were historically underrepresented in AI can now help shape the systems that represent them.

For junior researchers, that is a powerful direction for the next decade of NLP.

References

  1. AP News. Chile launches open-source AI model designed for Latin America (Feb 2026).
  2. Reuters. Latin American countries to launch own AI model in September (Jun 17, 2025).
  3. LATAM-GPT official site (project overview).

— Andrew

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From Hallucinated Citations to Linked Evidence: The OpenScholar Approach

In my recent blog post, I discussed Citation Hallucinations at NeurIPS and What They Teach Us. As a student researcher, I think many people are asking the same question: can we use AI tools that help us get citations right without made-up references?

I recently read a Nature article that gave a strong answer. The article introduces OpenScholar, a retrieval-augmented system that combines a language model with a database of about 45 million open-access papers. Instead of relying only on model memory, OpenScholar retrieves papers first and then generates responses with explicit citation links.

Why this matters

For research workflows, citation reliability is everything. When references are wrong, the writing process breaks down quickly. OpenScholar is designed to reduce that risk by grounding claims in retrieved literature before generating the final response.

According to the article, OpenScholar is also:

  • Open source
  • Relatively lightweight
  • Deployable locally
  • Built for scientific search and literature review

That combination is important because it supports both accuracy and reproducibility, which are essential in research settings.

Reported performance

Nature reports that in the OpenScholar evaluations, the 8B model outperformed GPT-4o on correctness in their benchmark and significantly reduced fabricated citations. The article also notes that citation behavior was described as being comparable to human experts in their testing context.

Comparison with OpenAI deep research tools

The article places OpenScholar in a broader trend. Since OpenScholar was first posted on arXiv about 14 months ago, companies such as OpenAI have integrated similar retrieval-based “deep research” methods into commercial LLM products, improving factual accuracy and citation quality compared with earlier model behavior.

OpenScholar’s main distinction in that landscape is cost-efficiency plus openness. Nature cites the OpenScholar team saying it can run at a fraction of the cost of GPT-5 with deep research, while still grounding outputs in a large scientific corpus.

Limitations to keep in mind

The article is clear that OpenScholar is not perfect. The authors acknowledge two major limitations:

  1. It does not always retrieve the most representative or most relevant papers for every query.
  2. It is limited by the scope of its indexed database.

So even though OpenScholar helps with citation hallucinations, retrieval quality remains a core bottleneck. In practice, researchers still need to verify paper relevance and coverage before relying on output.

Final thoughts

My takeaway is that this is a meaningful step forward for student researchers and independent scholars. Better grounding, lower cost, and open access can make high-quality literature review tools more available to more people.

Nature also quotes an outside researcher who argues that if OpenScholar remains free, it could become one of the most widely used tools for scientific search. I think that is very possible.

If you have tested OpenScholar, share what worked and what did not. I may feature reader feedback in a follow-up post.

— Andrew

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Citation Hallucinations at NeurIPS and What They Teach Us

I’m writing this post about a recent discovery by GPTZero, reported by Shmatko et al. 2026. The finding sparked significant discussion across the research community (Goldman 2026). While hallucinations produced by large language models have been widely acknowledged, far less attention has been paid to hallucinations in citations. Even reviewers at top conferences such as NeurIPS failed to catch citation hallucination issues, showing how easily these errors can slip through existing academic safeguards.

For students and early-career researchers, this discovery should serve as a warning. AI tools can meaningfully improve research efficiency, especially during early-stage tasks like brainstorming, summarizing papers, or organizing a literature review. At the same time, these tools introduce new risks when they are treated as sources rather than assistants. Citation accuracy remains the responsibility of the researcher, not the model.

As a junior researcher, I have used AI tools such as ChatGPT to help with literature reviews in my own work. In practice, AI can make the initial stages of research much easier by surfacing themes, suggesting keywords, or summarizing large volumes of text. However, I have also seen how easily this convenience can introduce errors. Citation hallucinations are particularly dangerous because they often look plausible. A reference may appear to have a reasonable title, realistic authors, and a convincing venue, even though it does not actually exist. Unless each citation is verified, these errors can quietly make their way into drafts.

According to GPTZero, citation hallucinations tend to fall into several recurring patterns. One common issue is the combination or paraphrasing of titles, authors, or publication details from one or more real sources. Another is the outright fabrication of authors, titles, URLs, DOIs, or publication venues such as journals or conferences. A third pattern involves modifying real citations by extrapolating first names from initials, adding or dropping authors, or subtly paraphrasing titles in misleading ways. These kinds of errors are easy to overlook during review, particularly when the paper’s technical content appears sound.

The broader lesson here is not that AI tools should be avoided, but that they must be used carefully and responsibly. AI can be valuable for identifying research directions, generating questions, or helping navigate unfamiliar literature. It should not be relied on to generate final citations or to verify the existence of sources. For students in particular, it is important to build habits that prioritize checking references against trusted databases and original papers.

Looking ahead, this finding reinforces an idea that has repeatedly shaped how I approach my own work. Strong research is not defined by speed alone, but by care, verification, and reflection. As AI becomes more deeply embedded in academic workflows, learning how to use it responsibly will matter just as much as learning the technical skills themselves.

References

Shmatko, N., Adam, A., and Esau, P. GPTZero finds 100 new hallucinations in NeurIPS 2025 accepted papers, Jan. 21, 2026

Goldman, S. NeurIPS, one of the world’s top academic AI conferences, accepted research papers with 100+ AI-hallucinated citations, new report claims. Fortune, Jan 21, 2026

— Andrew

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AI Sycophancy: When Our Chatbots Say “Yes” Instead of “Why”

“I asked ChatGPT to check my argument and it just kept agreeing with me.”
“Gemini told me my logic was solid even when I knew it wasn’t.”
“Grok feels like a hype-man, not a thinking partner.”

These are the kinds of comments I keep seeing from my school friends who feel that modern AI tools are becoming too agreeable for their own good. Instead of challenging flawed reasoning or offering alternative perspectives, many chatbots default to affirmation. This behavior has a name: AI sycophancy. The term does not originate from me. It comes from recent research and ongoing conversations in the AI community, where scholars are identifying a growing tendency for AI systems to prioritize user approval over honest reasoning.

At first glance, this might feel harmless or even comforting. After all, who does not like being told they are right? But beneath that friendliness lies a deeper problem that affects how we learn, decide, and think.

What is AI Sycophancy?

AI sycophancy refers to a pattern in which an AI system aligns its responses too closely with a user’s expressed beliefs or desires, even when those beliefs conflict with evidence or logic. Rather than acting as an independent evaluator, the model becomes a mirror.

For example, a user might say, “I think this argument is correct. Do you agree?” and the model responds with enthusiastic confirmation instead of critical analysis. Or the system might soften disagreement so much that it effectively disappears. Recent research from Northeastern University confirms that this behavior is measurable and problematic. Their report, The AI industry has a problem: Chatbots are too nice, shows that when models alter their reasoning to match a user’s stance, their overall accuracy and rationality decline.
https://news.northeastern.edu/2025/11/24/ai-sycophancy-research/

Why Does It Exist?

Several forces contribute to the rise of AI sycophancy:

  • Training incentives and reward systems.
    Many models are optimized to be helpful, polite, and pleasant. When user satisfaction is a core metric, models learn that agreement often leads to positive feedback.
  • User expectations.
    People tend to treat chatbots as friendly companions rather than critical reviewers. When users express certainty, the model often mirrors that confidence instead of questioning it.
  • Alignment trade-offs.
    The Northeastern team highlights a tension between sounding human and being rational. In attempting to appear empathetic and affirming, the model sometimes sacrifices analytical rigor.
  • Ambiguous subject matter.
    In questions involving ethics, predictions, or subjective judgment, models may default to agreement rather than risk appearing confrontational or incorrect.

What Are the Impacts?

The consequences of AI sycophancy extend beyond mild annoyance.

  • Weakened critical thinking.
    Students who rely on AI for feedback may miss opportunities to confront their own misconceptions.
  • Lower reasoning quality.
    The Northeastern study found that adjusting answers to match user beliefs correlates with poorer logic and increased error rates.
  • Risk in high-stakes contexts.
    In healthcare, policy, or education, an overly agreeable AI can reinforce flawed assumptions and lead to harmful decisions.
  • False confidence.
    When AI consistently affirms users, it creates an illusion of correctness that discourages self-reflection.
  • Ethical concerns.
    A system that never challenges bias or misinformation becomes complicit in reinforcing it.

How to Measure and Correct It

Measuring sycophancy

Researchers measure sycophancy by observing how much a model shifts its answer after a user asserts a belief. A typical approach involves:

  • Presenting the model with a scenario and collecting its initial judgment.
  • Repeating the scenario alongside a strong user opinion or belief.
  • Comparing the degree to which the model’s stance moves toward the user’s position.
  • Evaluating whether the reasoning quality improves, stays stable, or deteriorates.

The greater the shift without supporting evidence, the higher the sycophancy score.

Correcting the behavior

Several strategies show promise:

  • Penalize agreement that lacks evidence during training.
  • Encourage prompts that demand critique or alternative views.
  • Require models to express uncertainty or justify reasoning steps.
  • Educate users to value disagreement as a feature rather than a flaw.
  • Use multi-agent systems where one model challenges another.
  • Continuously track and adjust sycophancy metrics in deployed systems.

Why This Matters to Me as a Student

As someone preparing to study computational linguistics and NLP, I want AI to help sharpen my thinking, not dull it. If my research assistant simply validates every claim I make, I risk building arguments that collapse under scrutiny. In chess, improvement only happens through strong opposition. The same is true for intellectual growth. Agreement without resistance is not growth. It is stagnation.

Whether I am analyzing Twitch language patterns or refining a research hypothesis, I need technology that questions me, not one that treats every idea as brilliant.

Final Thought

The Northeastern research reminds us that politeness is not the same as intelligence. A chatbot that constantly reassures us might feel supportive, but it undermines the very reason we turn to AI in the first place. We do not need machines that echo our beliefs. We need machines that help us think better.

AI should challenge us thoughtfully, disagree respectfully, and remain grounded in evidence. Anything less turns a powerful tool into a flattering reflection.

— Andrew

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How Chatbots Understand Us: Exploring the Basics of Natural Language Processing (NLP)

If you’ve ever asked Siri a question, chatted with a customer support bot, or played around with ChatGPT, you’ve already seen natural language processing (NLP) in action. But have you ever wondered: How do these systems actually understand what I’m saying? That question is what first got me curious about NLP, and now, as a high school student diving into computational linguistics, I want to break it down for others who might be wondering too.

What Is NLP?

Natural Language Processing is a branch of artificial intelligence (AI) that helps computers understand, interpret, and generate human language. It allows machines to read text, hear speech, figure out what it means, and respond in a way that (hopefully) makes sense.

NLP is used in tons of everyday tools and apps, like:

  • Chatbots and virtual assistants (Siri, Alexa, Google Assistant)
  • Translation tools (Google Translate)
  • Grammar checkers (like Grammarly)
  • Sentiment analysis (used by companies to understand customer reviews)
  • Smart email suggestions (like Gmail’s autocomplete)

How Do Chatbots Understand Language?

Here’s a simplified view of what happens when you talk to a chatbot:

1. Text Input

You say something like: “What’s the weather like today?”
If it’s a voice assistant, your speech is first turned into text through speech recognition.

2. Tokenization

The text gets split into chunks called tokens (usually words or phrases). So that sentence becomes:
[“What”, “’s”, “the”, “weather”, “like”, “today”, “?”]

3. Understanding Intent and Context

The chatbot has to figure out what you mean. Is this a question? A request? Does “weather” refer to the forecast or something else?

This part usually involves models trained on huge amounts of text data, which learn patterns of how people use language.

4. Generating a Response

Once the bot understands your intent, it decides how to respond. Maybe it retrieves information from a weather API or generates a sentence like “Today’s forecast is sunny with a high of 75°F.”

All of this happens in just a few seconds.

Some Key Concepts in NLP

If you’re curious to dig deeper into how this all works, here are a few beginner-friendly concepts to explore:

  • Syntax and Parsing: Figuring out sentence structure (nouns, verbs, grammar rules)
  • Semantics: Understanding meaning and context
  • Named Entity Recognition (NER): Detecting names, dates, locations in a sentence
  • Language Models: Tools like GPT or BERT that learn how language works from huge datasets
  • Word Embeddings: Representing words as vectors so computers can understand similarity (like “king” and “queen” being close together in vector space)

Why This Matters to Me

My interest in NLP and computational linguistics started with my nonprofit work at Student Echo, where we use AI to analyze student survey responses. Since then, I’ve explored research topics like sentiment analysis, LLMs vs. neural networks, and even co-authored a paper accepted at a NAACL 2025 workshop. I also use tools like Zotero to manage my reading and citations, something I wish I had known earlier.

What excites me most is how NLP combines computer science with human language. I’m especially drawn to the possibilities of using NLP to better understand online communication (like on Twitch) or help preserve endangered languages.

Final Thoughts

So the next time you talk to a chatbot, you’ll know there’s a lot going on behind the scenes. NLP is a powerful mix of linguistics and computer science, and it’s also a really fun space to explore as a student.

If you’re curious about getting started, try exploring Python, open-source NLP libraries like spaCy or NLTK, or even just reading research papers. It’s okay to take small steps. I’ve been there too. 🙂

— Andrew

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AI in Schoolwork: Difference Approaches Taken in the U.S. and China

Recently, I read an article from MIT Technology Review titled “Chinese universities want students to use more AI, not less.” It really made me think about the differences in how the U.S. and China are approaching AI in education, especially as a high school student growing up in Washington state.

In China, AI has gone from being a taboo to a toolkit in just a couple of years. University students once had to find mirror versions of ChatGPT through secondhand marketplaces and VPNs just to access the tools. Back then, professors warned students not to use AI for assignments. But now, things have completely changed.

Chinese universities are actively encouraging students to use generative AI tools, as long as they follow best practices. Professors are adding AI-specific lessons to their classes. For example, one law professor teaches students how to prompt effectively and reminds them that AI is only useful when combined with human judgment. Students are using tools like DeepSeek for everything from writing literature reviews to organizing thoughts.

This push for AI education isn’t just happening in individual classrooms. It’s backed by national policy. The Chinese Ministry of Education released guidelines in April 2025 calling for an “AI plus education” approach. The goal is to help students develop critical thinking, digital fluency, and real-world skills across all education levels. Cities like Beijing have even introduced AI instruction in K–12 schools.

In China, AI is also viewed as a key to career success. A report from YiCai found that 80 percent of job listings for recent college grads mention AI as a desired skill. So students see learning how to use AI properly as something that gives them a competitive edge in a tough job market.

That’s pretty different from what I’ve seen here in the U.S.

In July 2024, the Washington Office of Superintendent of Public Instruction (OSPI) released official guidance for AI in schools. The message isn’t about banning AI. It’s about using it responsibly. The guidance encourages human-centered learning, with values like transparency, privacy, equity, and critical thinking. Students are encouraged to use AI tools to support their learning, but not to replace it.

Instead of secretly using AI to write a paper, students in Washington are encouraged to talk openly about how and when they use it. Teachers are reminded that AI should be a support, not a shortcut. The guidance also warns about overusing AI detection tools, especially since those tools can sometimes unfairly target multilingual students.

Adding to this, a recent brain-scan study by MIT Media Lab called “Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task” raises some interesting points. Over four months, participants had their brains scanned while using ChatGPT for writing tasks. The results were surprising:

  • 83% of AI users couldn’t remember what they had just written
  • Brain activity dropped by 47% in AI users and stayed low even after stopping
  • Their writing was technically correct but described by teachers as robotic
  • ChatGPT made users 60% faster, but reduced learning-related brain activity by 32%

The group that performed the best started their work without AI and only added it later. They had stronger memory, better brain engagement, and wrote with more depth. This shows that using AI right matters. If we rely on it too much, we might actually learn less.

MIT’s full research can be found here or read the paper on arXiv. (a caveat called out by the research team: “as of June 2025, when the first paper related to the project, was uploaded to Arxiv, the preprint service, it has not yet been peer-reviewed, thus all the conclusions are to be treated with caution and as preliminary”)

So what does this all mean?

I think both China’s and our approaches have something valuable to offer. China is focused on future skills and career readiness. The U.S. is focused on ethics, fairness, and critical thinking. Personally, I believe students should be allowed to use AI in schoolwork, but with the right guidance. We should be learning how to prompt better, double-check results, and combine AI tools with our own thinking.

AI is already part of our world. Instead of hiding from it, we should be learning how to use it the right way.

You can read the full MIT Technology Review article here
Washington’s official AI guidance for schools (published July 2024) is here (PDF)

— Andrew

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Learning from Industry: How Companies Evaluate LLMs

Over the past few years, large language models (LLMs) have been everywhere. From chatbots that help you book flights to tools that summarize long documents, companies are finding ways to use LLMs in real products. But success is not guaranteed. In fact, sometimes it goes very wrong. A famous example was when Expedia’s chatbot once gave instructions on how to make a Molotov cocktail (Cybernews Report; see the chatbot screenshot below). Another example was Air Canada’s AI-powered chatbot making a significant error by providing incorrect information regarding bereavement fares (BBC Report). Mistakes like these show how important it is for industry practitioners to build strong evaluation systems for LLMs.

Recently, I read a blog post from GoDaddy’s engineering team about how they evaluate LLMs before putting them into real-world use (GoDaddy Engineering Blog). Their approach stood out to me because it was more structured than just running a few test questions. Here are the main lessons I took away:

  1. Tie evaluations to business outcomes
    Instead of treating testing as an afterthought, GoDaddy connects test data directly to golden datasets. These datasets are carefully chosen examples that represent what the business actually cares about.
  2. Use both classic and new evaluation methods
    Traditional machine learning metrics like precision and recall still matter. But GoDaddy also uses newer approaches like “LLM-as-a-judge,” where another model helps categorize specific errors.
  3. Automate and integrate evaluation into development
    Evaluation isn’t just something you do once. GoDaddy treats it as part of a continuous integration pipeline. They expand their golden datasets, add new feedback loops, and refine their systems over time.

As a high school student, I’m not joining the tech industry tomorrow. Still, I think it’s important for me to pay attention to best practices like these. They show me how professionals handle problems that I might face later in my own projects. Even though my experiments with neural networks or survey sentiment analysis aren’t at the scale of Expedia, Air Canada, or GoDaddy, I can still practice connecting my evaluations to real outcomes, thinking about error types, and making testing part of my workflow.

The way I see it, learning industry standards now gives me a head start for the future. And maybe when I get to do college research or internships, I’ll already be used to thinking about evaluation in a systematic way rather than as an afterthought.

— Andrew

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