190 citations · 202 across the 8 of their papers we have counts for
11 papers
NaijaFacts: A Framework for Disentangling Language from Knowledge in Facts about Nigeria
Zainab Afolabi, Nikita Kozodoi, Jack Butler +1
Multilingual benchmarks for African languages mostly translate generic questions into local languages, measuring linguistic competence but not whether models know facts about Afric…
Model Merging Does Not Require Uniform LoRA Ranks
Zirong Xie, Zainab Afolabi, Nikita Kozodoi +1
Model merging combines separately fine-tuned experts into a single model without retraining. Standard practice trains every expert at the same LoRA rank, which couples the rank cho…
Evolving LLM-Generated Features for Interpretable Classification
Jack Butler, Zainab Afolabi, Nikita Kozodoi
Large language models (LLMs) are increasingly used as classifiers, yet they operate as opaque systems whose decisions are difficult to interpret, which complicates their use in reg…
Test-Time Augmentation for LLMs: Where to Inject Diversity at Inference Time
Nikita Kozodoi, Zainab Afolabi, Jack Butler
Aggregating several LLM responses to the same question can improve accuracy, but every method that does so must decide where the responses should differ. Established test-time scal…
Are we Merging the Right Models? Impact of Expert Training Duration on Model Merging for LLMs
Nikita Kozodoi, Zainab Afolabi, Jack Butler
Multi-task model merging combines separately trained expert models into a single model that handles all tasks without co-training. Standard practice merges experts at their optimal…
SWE-InfraBench: Evaluating Language Models on Cloud Infrastructure Code
Natalia Tarasova, Enrique Balp-Straffon, Aleksei Iancheruk +10
Building infrastructure-as-code (IaC) in cloud computing is a critical task, underpinning the reliability, scalability, and security of modern software systems. Despite the remarka…
Finding the Sweet Spot: Trading Quality, Cost, and Speed During Inference-Time LLM Reflection
Jack Butler, Nikita Kozodoi, Zainab Afolabi +2
As Large Language Models (LLMs) continue to evolve, practitioners face increasing options for enhancing inference-time performance without model retraining, including budget tuning…
Fighting Sampling Bias: A Framework for Training and Evaluating Credit Scoring Models
Nikita Kozodoi, Stefan Lessmann, Morteza Alamgir +2
Scoring models support decision-making in financial institutions. Their estimation and evaluation are based on the data of previously accepted applicants with known repayment behav…
Probabilistic Demand Forecasting with Graph Neural Networks
Nikita Kozodoi, Elizaveta Zinovyeva, Simon Valentin +2
Demand forecasting is a prominent business use case that allows retailers to optimize inventory planning, logistics, and core business decisions. One of the key challenges in deman…
Fairness in Credit Scoring: Assessment, Implementation and Profit Implications
Nikita Kozodoi, Johannes Jacob, Stefan Lessmann
The rise of algorithmic decision-making has spawned much research on fair machine learning (ML). Financial institutions use ML for building risk scorecards that support a range of…
Shallow Self-Learning for Reject Inference in Credit Scoring
Nikita Kozodoi, Panagiotis Katsas, Stefan Lessmann +2
Credit scoring models support loan approval decisions in the financial services industry. Lenders train these models on data from previously granted credit applications, where the…