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An attempt to shroud text from LLMs
Describe the items discussed in the following sentences: “phashyon es cycklyq. chuyldren donth wanth tew weywr chloths vat there pairent weywr. pwroggwrammyng languij phashyon hash phricksionz vat inycially inqloob impleementaision suppoort, lybrareyz (whych sloa doun adopsion, ant wunsh establysht jobz ol avaylable too suppourt ecksysting kowd (slowyng doun va demighz ov a langguij).”
I was at the {Tech: Europe} hackathon yesterday, and my ‘inspired’ idea was to transform sentences such that while people could (eventually) still understand them, LLMs would fail miserably.
My team-mates from the last hackathon applied too late to be accepted, pitching the idea to people without a team failed to convince anyone to join me, and the idea pitches I heard did not convince me to join someone else.
Many years ago, I implemented a sounds like tool, which generates character sequences that an English reader would recognise (perhaps slowly) as sounding like the input word (also used in a previous hack). Surely, replacing every word in a sentence with a randomly chosen sounds-like character sequence would seriously confuse LLMs (wrapper code around sounds like tool).
My ‘inspired’ idea failed hard with: Grok, ChatGPT, Deepseek (no sharable link), and Perplexity.
Grok’s response yesterday (this recent response does not begin by giving the corrected sentence):
The sentences provided contain numerous typographical errors, making them challenging to interpret. However, by correcting the likely typos, the intended meaning can be inferred as follows: Corrected Sentences: "Fashion is cyclical. Children don’t want to wear clothes that their parents wore. Programming language fashion has frictions that initially include implementation support, libraries (which slow down adoption), and once established, jobs are available to support existing code (slowing down the demise of a language)." |
Time to do some pivoting, with options including:
- Update source-like tool. LLMs operate on tokens, which are short common character sequences, which may or may not be complete words. The sounds-like tool could be enhanced to be token aware, and attempt to change all tokens within a word. While the tokenization sequence list varies between LLMs, I assume that most, say, 10,000 common character sequences are always treated as tokens. The list of GPT2 tokens is available.
- Narrow the use case. Anybody monitoring text messages will want to keep costs down by using as small a model as possible, and will likely want to keep the text in-house. Check the ability of small models to handle sounds-like sentences.
- Add non-sounds-like changes. For instance, adding underscores, changing some letters to upper case, and changing ‘l’ to the number ‘1’.
With only a few hours available (this was essentially an afternoon hack), I opted to investigate the performance of small models.
Below is the edited output (‘…’ indicates further text was generated) of models containing 14.8, 8.29, 4.02, 2.03 billion parameters. These Open weights models ran under LM Studio on my local machine (which does not have a GPU, so the response is sluuuugish).
I was surprised at how well the models performed, particularly the 4.02B parameter reasoning model. The responses did not start to go wrong until the model size was reduced to 2 billion parameters. We will have to wait and see if tiny models continue to improve, such that a model containing under a billion parameters can reliably interpret sounds-like sentences.
I’m still clinging to the possibility that sentences can be modified such that humans can understand them, but LLMs cannot (AGI, which LLMs are not close to achieving, will by definition have human level ability).
deepseek-r1-distil-qwen-14b has 14.8B parameters and is currently a leading model at this size. Its response was:
The sentences discuss several items:
Fashion Cycles: The first sentence appears to suggest that fashion (phashyon)
is cyclic (cycklyq), meaning it follows repeating trends or cycles.
Children's Preferences for Clothing: The second sentence discusses children
not wanting to wear the same clothes as their parents, implying a
generational difference in fashion preferences.
Programming Language Trends and Challenges:
o The third sentence talks about programming languages (pwroggwrammyng languij)
having fads or trends (phashyon hash phricksionz), which are initially
implemented but can slow down adoption due to libraries taking time to develop.
o It also mentions that these fads may eventually lead to the decline of
existing codebases (ecksysting kowd) because they become outdated or unsupported.
Overall, the sentences touch on fashion trends, generational differences in
clothing preferences, and challenges related to programming language adoption
and maintenance.
2.68 tok/sec • 826 tokens • 29.94s to first token |
Perhaps vision models will not be as proficient with this kind of text. Qwen2.5-VL-7B-Instruct is an 8.29B parameter vision model.
The sentences you've provided appear to be a mix of misspelled and jumbled words, making it challenging to understand the intended meaning. However, I'll attempt to interpret them based on common themes in technology and programming. 1. "phashyon es cycklyq." - This seems to be an incomplete or corrupted phrase that might refer to something related to fashion being cyclical. 2. "chuyldren donth wanth tew weywr chloths vat there pairent weywr." - This appears to be a misspelled and jumbled sentence, possibly referring to children not wanting to wear clothes their parents wore when they were young. 3. "pwroggwrammyng languij phashyon hash phricksionz vat inycially inqloob impleementaision suppoort." - This phrase seems to be discussing programming languages and the friction or challenges associated with implementing initial support for new features or functionalities within these languages. 4. "lybrareyz (whych sloa doun adopsion, ant wunsh establysht jobz ol avaylable too suppourt ecksysting kowd (slowyng doun va demighz ov a langguij)" - This part appears to be discussing libraries that slow down adoption and the desire for established jobs or roles available to support existing code, which in turn slows down development of languages. In summary: ... 4.06 tok/sec • 392 tokens • 13.46s to first token |
Qwen3-4b is a 4.02B parameter model with reasoning abilities:
Thought for 2 minutes 47 seconds The sentences discuss two main topics: 1. A Metaphorical Observation: "Children don’t want to wear clothes their parents wear." This is a metaphorical statement suggesting that individuals (e.g., developers) may resist adopting technologies or practices that are inherited from older generations (e.g., legacy code, outdated programming languages). It highlights a generational or cultural resistance to following established norms. 2. Technical Discussion on Programming Languages and Libraries: "Programming languages have features initially included in implementation support, libraries that slow down adoption and establish jobs for existing code, slowing the decline of a language." This refers to: o Programming Language Features: Some programming languages include built-in support for specific functionalities (e.g., syntax, tools) that are essential for development. o Libraries and Ecosystems: Libraries or frameworks in a programming language can influence adoption by providing tools that simplify tasks. However, if these libraries become outdated or complex, they may slow down the adoption of newer versions of the language. o Maintenance and Decline: The text suggests that maintaining compatibility with existing code (e.g., legacy systems) can help sustain a programming language, even as newer technologies emerge. This creates a tension between innovation and backward compatibility. Key Concepts: o Metaphor vs. Technicality: The first part uses a metaphor to illustrate resistance to inherited practices, while the second part delves into technical aspects of software development. o Language Evolution: The discussion highlights how programming languages balance innovation (new features) with preservation (supporting existing code), which can affect their longevity and adoption. This interplay between metaphor and technical detail underscores themes of generational change, technological inertia, and the challenges of maintaining compatibility in software ecosystems. 5.59 tok/sec • 1290 tokens • 7.67s to first token |
Qwen3-1.7B is a 2.03B parameter model, i.e., a smaller version of Qwen3-4b used above.
Thought for 1 minutes 8 seconds The sentences discuss several interconnected themes, blending technical and metaphorical elements: 1. Phishing (Cyclic Behavior): The first sentence references "phashyon" (likely a misspelling of "phishing") as cyclic or recurring, suggesting phishing attacks that repeat or evolve over time. 2. Children's Clothing and Parental Influence: A metaphorical statement about children not wanting to wear clothes their parents wear, implying a generational or cultural disconnect in preferences or values. 3. Programming Languages and Technical Concepts: The text introduces complex technical terms: o Phishing Zones (possibly a misinterpretation of "phishing zones" as areas where phishing attacks occur). o Implementation Support and Libraries (e.g., libraries that slow down adoption or require stable ecosystems). o Ecosystems (referring to software or technical environments) and Job Creation in these systems. 4. Adoption and Slowdown: Libraries are described as "slowing down adoption" and "slowly reducing values over a language," suggesting challenges in integrating new technologies or standards. 5. Confusion and Mixed Language Elements: The text contains mixed syntax (e.g., "pwroggwrammyng" → "programming"), mistranslations, and unclear phrasing, likely due to linguistic errors or intentional ambiguity. Key Items Discussed:... 10.32 tok/sec • 1081 tokens • 2.93s to first token |
Thanks to our {Tech: Europe} hosts who kept us fed and watered.
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