This is a classic "Garbage In, Garbage Out" scenario, but specifically related to pattern contention. Because the training set contains contradictory rules for how to handle consonant clusters (combinations of consonants like sp, st, tr, gl, qu), you are forcing the model to learn a rule that does not exist, leading to stochastic confusion rather than deterministic logic. Here is the breakdown of the semantic error and how it corrupts the model’s understanding of the language.1. The Semantic Error: Inconsistent Cluster Handling The flaw lies in the inconsistency between these two examples in your dataset: Example A ("space"): space $\rightarrow$ ace-spay Rule Implied: Identify the consonant cluster (sp) and move the entire group to the end. (This is correct Pig Latin).Example B ("split"): split $\rightarrow$ plit-say Rule Implied: Identify only the first letter (s) and move it, ignoring that p is attached to it phonetically. (This is incorrect Pig Latin; it should be it-splay).Example C ("quantum"): quantum $\rightarrow$ uantum-qay Rule Implied: Treat q as a standalone consonant. (Incorrect; qu is phonetically a single unit /kw/, so it should typically be antum-quay).2. How This Trains the Model Incorrectly When an LLM (or even a simple sequence-to-sequence model) trains on this, it attempts to minimize a loss function by finding a pattern that satisfies all examples. However, you have provided a mathematical impossibility: The Ambiguity Trap The model learns that when it sees the token s followed by p:Sometimes it must move both (from learning "space").Sometimes it must move only the s (from learning "split").Because the dataset is small, the model cannot distinguish context. It doesn't know that "split" and "space" are just different words; it only sees token sequences. It will likely settle on a "muddy" probability distribution—perhaps giving a 50% probability to moving "sp" and 50% to moving "s".3. The Consequence: Failure on "Many Such Examples "English is dense with consonant clusters. If the model is trained on the logic of your "split" example, it will fail to phonetically parse a massive percentage of the English vocabulary. If you ask the model to translate "String": Correct Logic: Move "str" $\rightarrow$ ing-stray Your Model's Logic (derived from "split"): It sees "s" and immediately moves it $\rightarrow$ tring-say. The Semantic "Uncanny Valley" The output tring-say is pronounceable, but semantically broken in the target language (Pig Latin).Tring-say implies the original word was "string" (if adhering to the bad rule) or "sing" (if adhering to a different rule).This destroys the reversibility of the cypher. A language model trained this way acts as a lossy compression algorithm—you cannot reliably get back to the English input from the Pig Latin output because the rules of transformation were applied inconsistently. Summary of Failure By failing to treat consonant clusters (onset) as a cohesive unit, the model learns a character-level positional rule ("always move index 0") rather than a phonological rule ("move characters until the first vowel").This results in a model that technically "works" (it outputs text) but fails the semantic requirement of the specific language game it is meant to play.

Here is a correction:

Create some training examples

examples = [
{
"input": "banana split",
# FIX: "split" now moves "spl" together -> "it-splay"
# Old (Bad): "plit-say"
"output": "anana-bay it-splay"
},
{
"input": "quantum physics",
# FIX: "quantum" treats "qu" as a single phonetic unit -> "antum-quay"
# Old (Bad): "uantum-qay"
"output": "antum-quay ysics-phay"
},
{
"input": "donut shop",
# "sh" is a digraph, moved together (Correct)
"output": "onut-day op-shay"
},
{
"input": "pickle jar",
"output": "ickle-pay ar-jay"
},
{
"input": "space exploration",
# "sp" moved together (Correct)
# "exploration" starts with vowel, add "-way" (Correct)
"output": "ace-spay exploration-way"
},
{
"input": "rubber duck",
"output": "ubber-ray uck-day"
},
{
"input": "coding wizard",
"output": "oding-cay izard-way"
},
]
Summary of Changes split $\rightarrow$ it-splay:Why: The cluster is spl (three consonants). The previous version only moved s, which broke the phonetic pattern established by words like "shop" and "space. "quantum $\rightarrow$ antum-quay: Why: In English phonology, qu acts as a single sound (/kw/). Standard Pig Latin treats qu as a block. If you only move the q (uantum-qay), the result is unpronounceable and breaks the "starts with a vowel sound" rule for the remainder of the word. If you use this dataset, the model will learn a consistent attention pattern: "Attend to the beginning of the sequence, scan until the first vowel is found, and perform the swap operation."