Search papers, labs, and topics across Lattice.
4
0
7
5
It is observed that the natural language of problem statements affects LLM-based code generation performance and translation improved Accuracy, but its effectiveness was not consistent across datasets and model types.
TnT-LLM delivers high-quality taxonomies but at a steep cost, while CLIMB offers rapid generation with lower quality鈥攈ighlighting a crucial trade-off for SE researchers.
RepTran achieves a remarkable 74.7% repair rate for Transformer models, significantly outperforming existing repair methods.
ThinkLog achieves a 20.55% accuracy in log statement generation, marking a significant leap over previous methods while slashing inference costs in half.
