External context enables language models to accomplish tasks which rely on information beyond what is captured during pretraining. A standard way to improve contextual faithfulness in these settings is to fine-tune on synthetic, modified questions for which the model must adhere to provided contexts inconsistent with information that may have been seen during training. While this approach reliably improves contextual faithfulness, we find that adapting to a narrowly targeted counterfactual signal yields degradation on other faithfulness evaluations and regression on tasks that evaluate a model’s parametric world knowledge and reasoning. We find that data augmentation, in which a model is trained on both parametrically consistent and counterfactual examples improves performance on faithfulness tasks with reduced regression on parametric world knowledge and reasoning tasks. We find that these improvements are largely consistent across post-training objectives (i.e. SFT, DPO) and across ground-truth, counterfactual data augmentation ratios.

As large language models (LLMs) scale in size and adoption, their computational and environmental costs continue to rise. Prior benchmarking efforts have primarily focused on latency reduction in idealized settings, often overlooking the diverse real-world inference workloads that shape energy use. In this work, we systematically analyze the energy implications of common inference efficiency optimizations across diverse Natural Language Processing (NLP) and generative Artificial Intelligence (AI) workloads, including conversational AI and code generation. We introduce a modeling approach that approximates real-world LLM workflows through a binning strategy for input-output token distributions and batch size variations. Our empirical analysis spans software frameworks, decoding strategies, GPU architectures, online and offline serving settings, and model parallelism configurations. We show that the effectiveness of inference optimizations is highly sensitive to workload geometry, software stack, and hardware accelerators, demonstrating that naive energy estimates based on FLOPs or theoretical GPU utilization significantly underestimate real-world energy consumption.Our findings reveal that the proper application of relevant inference efficiency optimizations can reduce total energy use by up to 73% from unoptimized baselines. These insights provide a foundation for sustainable LLM deployment and inform energy-efficient design strategies for future AI infrastructure.