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This work proposes test-aware mutant generation, in which an LLM receives the problem statement, canonical solution and base tests in a single prompt, and must generate a nontrivial mutant that passes the base unit tests.
This work incorporates fairness constraints directly into TFM training, enabling fair predictions in a single forward pass and proposes FairTFM, a scalable training strategy based on synthetic fairness tasks and a fairness-aware architecture using a gradient reversal layer, which encourages the model to learn represent...
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