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Alessio Devoto

5 accepted papers

2026

Universal Properties of Activation Sparsity in Modern Large Language Models

ICLR 2026poster

Activation sparsity is an intriguing property of deep neural networks that has been extensively studied in ReLU-based models, due to its advantages for efficiency, robustness, and interpretability. However, methods relying on exact zero activations do not directly apply to modern Large Language Mod…

Cited by 0SourceScholar
2025

Adaptive Computation Modules: Granular Conditional Computation for Efficient Inference

AAAI 2025technical

While transformer models have been highly successful, they are computationally inefficient. We observe that for each layer, the full width of the layer may be needed only for a small subset of tokens inside a batch and that the "effective" width needed to process a token can vary from layer to layer…

2025

Are We Done with MMLU?

NAACL 2025long

Maybe not. We identify and analyse errors in the popular Massive Multitask Language Understanding (MMLU) benchmark. Even though MMLU is widely adopted, our analysis demonstrates numerous ground truth errors that obscure the true capabilities of LLMs. For example, we find that 57% of the analysed que…

2025

Steering Knowledge Selection Behaviours in LLMs via SAE-Based Representation Engineering

NAACL 2025long

Large language models (LLMs) can store a significant amount of factual knowledge in their parameters. However, their parametric knowledge may conflict with the information provided in the context—this phenomenon, known as context-memory knowledge conflicts, can lead to undesirable model behaviour, s…

2024

A Simple and Effective L_2 Norm-Based Strategy for KV Cache Compression

EMNLP 2024main

The deployment of large language models (LLMs) is often hindered by the extensive memory requirements of the Key-Value (KV) cache, especially as context lengths increase. Existing approaches to reduce the KV cache size involve either fine-tuning the model to learn a compression strategy or leveragin…