5 research outputs found
Reprogramming under constraints: Revisiting efficient and reliable transferability of lottery tickets
In the era of foundation models with huge pre-training budgets, the
downstream tasks have been shifted to the narrative of efficient and fast
adaptation. For classification-based tasks in the domain of computer vision,
the two most efficient approaches have been linear probing (LP) and visual
prompting/reprogramming (VP); the former aims to learn a classifier in the form
of a linear head on the features extracted by the pre-trained model, while the
latter maps the input data to the domain of the source data on which the model
was originally pre-trained on. Although extensive studies have demonstrated the
differences between LP and VP in terms of downstream performance, we explore
the capabilities of the two aforementioned methods via the sparsity axis: (a)
Data sparsity: the impact of few-shot adaptation and (b) Model sparsity: the
impact of lottery tickets (LT). We demonstrate that LT are not universal
reprogrammers, i.e., for certain target datasets, reprogramming an LT yields
significantly lower performance than the reprogrammed dense model although
their corresponding upstream performance is similar. Further, we demonstrate
that the calibration of dense models is always superior to that of their
lottery ticket counterparts under both LP and VP regimes. Our empirical study
opens a new avenue of research into VP for sparse models and encourages further
understanding of the performance beyond the accuracy achieved by VP under
constraints of sparsity. Code and logs can be accessed at
\url{https://github.com/landskape-ai/Reprogram_LT}.Comment: Preprin
A latent linear model for nonlinear coupled oscillators on graphs
A system of coupled oscillators on an arbitrary graph is locally driven by
the tendency to mutual synchronization between nearby oscillators, but can and
often exhibit nonlinear behavior on the whole graph. Understanding such
nonlinear behavior has been a key challenge in predicting whether all
oscillators in such a system will eventually synchronize. In this paper, we
demonstrate that, surprisingly, such nonlinear behavior of coupled oscillators
can be effectively linearized in certain latent dynamic spaces. The key insight
is that there is a small number of `latent dynamics filters', each with a
specific association with synchronizing and non-synchronizing dynamics on
subgraphs so that any observed dynamics on subgraphs can be approximated by a
suitable linear combination of such elementary dynamic patterns. Taking an
ensemble of subgraph-level predictions provides an interpretable predictor for
whether the system on the whole graph reaches global synchronization. We
propose algorithms based on supervised matrix factorization to learn such
latent dynamics filters. We demonstrate that our method performs competitively
in synchronization prediction tasks against baselines and black-box
classification algorithms, despite its simple and interpretable architecture.Comment: 23 pages, 14 figure
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The Wisdom of Partisan Crowds: Comparing Collective Intelligence in Humans and LLM-based Agents
Human groups are able to converge to more accurate beliefs through deliberation, even in the presence of polarization and partisan bias --- a phenomenon known as the ``wisdom of partisan crowds.'' Large Language Models (LLMs) are increasingly being used to simulate human collective behavior, yet few benchmarks exist for evaluating their dynamics against the behavior of human groups. In this paper, we examine the extent to which the wisdom of partisan crowds emerges in groups of LLM-based agents that are prompted to role-play as partisan personas (e.g., Democrat or Republican). We find that they not only display human-like partisan biases, but also converge to more accurate beliefs through deliberation, as humans do. We then identify several factors that interfere with convergence, including the use of chain-of-thought prompting and lack of details in personas. Conversely, fine-tuning on human data appears to enhance convergence. These findings show the potential and limitations of LLM-based agents as a model of human collective intelligence
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Simulating Opinion Dynamics with Networks of LLM-based Agents
Accurately simulating human opinion dynamics is crucial for understanding a variety of societal phenomena, including polarization and the spread of misinformation. However, the agent-based models (ABMs) commonly used for such simulations often over-simplify human behavior. We propose a new approach to simulating opinion dynamics based on populations of Large Language Models (LLMs). Our findings reveal a strong inherent bias in LLM agents towards producing accurate information, leading simulated agents to consensus in line with scientific reality. This bias limits their utility for understanding resistance to consensus views on issues like climate change. After inducing confirmation bias through prompt engineering, however, we observed opinion fragmentation in line with existing agent-based modeling and opinion dynamics research. These insights highlight the promise and limitations of LLM agents in this domain and suggest a path forward: refining LLMs with real-world discourse to better simulate the evolution of human beliefs