Methodological Rigour in Algorithm Application: An Illustration of Topic Modelling Algorithm
- URL: http://arxiv.org/abs/2507.00547v1
- Date: Tue, 01 Jul 2025 08:11:07 GMT
- Title: Methodological Rigour in Algorithm Application: An Illustration of Topic Modelling Algorithm
- Authors: Malmi Amadoru,
- Abstract summary: I discuss how to ensure rigour in topic modelling studies.<n>I contribute to the literature on topic modelling and join the emerging dialogue on methodological rigour in theory construction research.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: The rise of advanced computational algorithms has opened new avenues for computationally intensive research approaches to theory development. However, the opacity of these algorithms and lack of transparency and rigour in their application pose methodological challenges, potentially undermining trust in research. The discourse on methodological rigour in this new genre of research is still emerging. Against this backdrop, I attempt to offer guidance on methodological rigour, particularly in the context of topic modelling algorithms. By illustrating the application of the structural topic modelling algorithm and presenting a set of guidelines, I discuss how to ensure rigour in topic modelling studies. Although the guidelines are for the application of topic modelling algorithms, they can be applied to other algorithms with context-specific adjustments. The guidelines are helpful, especially for novice researchers applying topic modelling, and editors and reviewers handling topic modelling manuscripts. I contribute to the literature on topic modelling and join the emerging dialogue on methodological rigour in computationally intensive theory construction research.
Related papers
- Position: We Need An Algorithmic Understanding of Generative AI [7.425924654036041]
This position paper proposes AlgEval: a framework for systematic research into the algorithms that LLMs learn and use.<n>AlgEval aims to uncover algorithmic primitives, reflected in latent representations, attention, and inference-time compute, and their algorithmic composition to solve task-specific problems.
arXiv Detail & Related papers (2025-07-10T08:38:47Z) - ClustOpt: A Clustering-based Approach for Representing and Visualizing the Search Dynamics of Numerical Metaheuristic Optimization Algorithms [4.740182373135037]
We propose a novel representation and visualization methodology that clusters solution candidates explored by the algorithm.<n>We quantify the consistency of search trajectories across runs of an individual algorithm and the similarity between different algorithms.<n>We apply this methodology to a set of ten numerical metaheuristic algorithms, revealing insights into their stability and comparative behaviors.
arXiv Detail & Related papers (2025-07-03T06:01:02Z) - Speculative Decoding and Beyond: An In-Depth Survey of Techniques [4.165029665035158]
Sequential dependencies present a fundamental bottleneck in deploying large-scale autoregressive models.<n>Recent advances in generation-refinement frameworks demonstrate that this trade-off can be significantly mitigated.
arXiv Detail & Related papers (2025-02-27T03:53:45Z) - Inference-Time Alignment in Diffusion Models with Reward-Guided Generation: Tutorial and Review [59.856222854472605]
This tutorial provides an in-depth guide on inference-time guidance and alignment methods for optimizing downstream reward functions in diffusion models.<n> practical applications in fields such as biology often require sample generation that maximizes specific metrics.<n>We discuss (1) fine-tuning methods combined with inference-time techniques, (2) inference-time algorithms based on search algorithms such as Monte Carlo tree search, and (3) connections between inference-time algorithms in language models and diffusion models.
arXiv Detail & Related papers (2025-01-16T17:37:35Z) - The Algorithm Configuration Problem [0.8075866265341176]
This article focuses on optimizing parametrized algorithms for solving specific instances of decision/optimization problems.
We present a comprehensive framework that not only formalizes the Algorithm Configuration Problem, but also outlines different approaches for its resolution.
arXiv Detail & Related papers (2024-03-01T17:29:34Z) - Enhancing AI Research Paper Analysis: Methodology Component Extraction
using Factored Transformer-based Sequence Modeling Approach [10.060305577353633]
We propose a factored approach to sequence modeling, which leverages a broad-level category information of methodology domains.
We conduct experiments following a simulated chronological setup (newer methodologies not seen during the training process)
Our experiments demonstrate that the factored approach outperforms state-of-the-art baselines by margins of up to 9.257% for the methodology extraction task with the few-shot setup.
arXiv Detail & Related papers (2023-11-05T16:33:35Z) - Neural Improvement Heuristics for Graph Combinatorial Optimization
Problems [49.85111302670361]
We introduce a novel Neural Improvement (NI) model capable of handling graph-based problems where information is encoded in the nodes, edges, or both.
The presented model serves as a fundamental component for hill-climbing-based algorithms that guide the selection of neighborhood operations for each.
arXiv Detail & Related papers (2022-06-01T10:35:29Z) - A review of approaches to modeling applied vehicle routing problems [77.34726150561087]
We review the approaches for modeling vehicle routing problems.
We formulate several criteria for evaluating modeling methods.
We discuss several future research avenues in the field of modeling VRP domains.
arXiv Detail & Related papers (2021-05-23T14:50:14Z) - A Survey on Deep Semi-supervised Learning [51.26862262550445]
We first present a taxonomy for deep semi-supervised learning that categorizes existing methods.
We then offer a detailed comparison of these methods in terms of the type of losses, contributions, and architecture differences.
arXiv Detail & Related papers (2021-02-28T16:22:58Z) - A Diagnostic Study of Explainability Techniques for Text Classification [52.879658637466605]
We develop a list of diagnostic properties for evaluating existing explainability techniques.
We compare the saliency scores assigned by the explainability techniques with human annotations of salient input regions to find relations between a model's performance and the agreement of its rationales with human ones.
arXiv Detail & Related papers (2020-09-25T12:01:53Z) - Reinforcement Learning as Iterative and Amortised Inference [62.997667081978825]
We use the control as inference framework to outline a novel classification scheme based on amortised and iterative inference.
We show that taking this perspective allows us to identify parts of the algorithmic design space which have been relatively unexplored.
arXiv Detail & Related papers (2020-06-13T16:10:03Z) - How Far are We from Effective Context Modeling? An Exploratory Study on
Semantic Parsing in Context [59.13515950353125]
We present a grammar-based decoding semantic parsing and adapt typical context modeling methods on top of it.
We evaluate 13 context modeling methods on two large cross-domain datasets, and our best model achieves state-of-the-art performances.
arXiv Detail & Related papers (2020-02-03T11:28:10Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.