Subtask 1: Error Analysis (English to FOL)
Top Performers: SmolLM-145-1B and SmallLM-1.1B were the most reliable models, consistently achieving the highest accuracy at 91.09%.

Model Comparison: Larger models like Llama-3.2-1B-Instruct (75.91%) and Gemma-2-2B-it (73.82%) performed significantly worse on this specific logic translation task.

Prompt Length Impact: The long Eng2FOL prompt (1721 chars) outperformed the Eng2FOLultrashort prompt (320 chars) by ~6%, proving that detailed syntactic instructions reduce errors.

Error Bias (FN vs. FP): Across all runs, False Negatives (Mixed Proofs) were much more common than False Positives (False Proofs).

Cautious Reasoning: Models were more likely to miss a valid logical connection than to "hallucinate" a fake one.

Belief Bias: Accuracy dropped when premises were "implausible" (semantically weird), showing that the models' pre-trained knowledge sometimes interferes with pure formal logic.

Best Setup: The most stable configuration for Subtask 1 was SmolLM-145-1B using the Eng2FOL prompt variant.

General Notes: um nichts zu vergessen:

Our overall best performing Model is the SmolLM
Results: 
Large LLMs vs Small LLMs?
most common errors:
False proof: 
what could have been the reason behind these errors occurring?
Plausibility?
Language?
The Sentences themselves? 
Did the model have more trouble with flawed logic and no weird meaning  or flawed sense but perfect logic?
as for subtask 2:
which premises made it extra hard for the model to do it's Job? Lots of negations, viele verundungen und so
Did it have a lot of trouble ignoring the irrelevant premises?
did it actually take all premises in and filtered the unnecessary ones out?

Belief Bias vs Formal Logic: Search parquet files for cases where the logic was valid but the sentence was absurd, if the model failed here its a clear case of belief bias( the models common sense overrode the logic it was supposed to strictly follow.
Premise Noise ( Subtask 2)
Look for examples where the input had 5 premises but only 2 were needed for the proof, count how many times the model tried to include the distractor premises in its FOL Translation.
Linguistic vs. Structural Errors: Did the model fail because of the Language (complex sentence structure) or the Target (complex FOL syntax)? Since you have the character counts for your prompts, check if the "Longer" prompts actually helped the model ignore those irrelevant premises or just confused it more.
 Subtask 3: Translation vs. Native Multilingual
 look for Translation Artifacts, did prior translation to english mess up?. Open your results and check for these specific errors:

Loss of Nuance: Did Deep Translator turn a specific quantifier (like "Few" or "Many") into a generic "Some"? If the model failed because the translation was too simple, that's a Translation Bottleneck.

Cultural Logic: Did Qwen perform better on certain languages (like Chinese) compared to the translated English versions?

The "Double Error" Trap: Sometimes the translator makes a mistake, and then the model makes a second mistake on top of it.

Subtask 4: Multilingual + Irrelevant Premises
This is the hardest part of the task because the model has to do two things at once: handle a non-English language AND filter out "noise".

Cognitive Overload: Did the model "forget" to filter irrelevant premises when the language wasn't English?

Language-Specific Noise: Did the model get confused by irrelevant premises because they contained "trigger words" that looked like logic in that specific language?

