Aryan Ashok Chandramania supervised by Dr. Radhika Mamidi received his Master of Science – Dual Degree in Computational Linguistics (CLD). Here’s a summary of his research work on Machine-Generated Text Detection and Attribution under Distribution Shift

Large language models now produce fluent text across domains, creating a need for systems that can distinguish human-authored text from machine-generated text and, when needed, identify the generator. We study whether RoBERTa’s multi-layer representations can support both goals under distribution shift. In our initial SemEval study, we proposed weighted layer averaging (WLA), which learns scalar weights over RoBERTa layer representations, and AdaLoRA, which adapts the encoder through parameter-efficient low-rank updates. The WLA component follows the view that different encoder depths can expose different contextual features. We extend that inquiry from binary detection to generator attribution. The main finding is task-conditional. For binary detection, aggregation can support a broad human-versus-machine boundary. For generator attribution, the same kind of compression appears less suitable. Further experiments indicate two practical risks: WLA layer averaging may blur layer-specific generator cues, and the tested low-rank adaptation setup may be too restrictive for six-way source identification under domain shift. To address this compression problem, we evaluate layer-slice concatenation, which preserves selected RoBERTa layers as independent channels before classification. The strongest reported region consists of slices ending at layer 9: layers 5–9 and 6–9 reach 0.9323 test accuracy on OUTFOX, with layers 2–9 marginally lower at 0.9319 and effectively tied after rounding. These results exceed the plain RoBERTa attribution anchor of 0.8730 accuracy and suggest that generator attribution benefits from preserving layer channels rather than using early scalar compression. Overall, robust detection and generator attribution require different architectures: aggregation for broad invariance, preservation for fine-grained source identification.

July 2026