activity
20232026
most citedElo Uncovered: Robustness and Best Practices in Language Model Evaluation

5 citations · 9 across the 6 of their papers we have counts for

collaborators

7 papers

cs.CL2026

Query-aware routing for Cross-lingual performance gains in Encoders

Akshay Jain, Edward Kim

Multilingual encoders can exhibit reduced retrieval effectiveness when queries and relevant documents differ in language, despite strong same-language performance. We investigate w…

cs.LG2025

Explaining Robustness to Catastrophic Forgetting Through Incremental Concept Formation

Nicki Barari, Edward Kim, Christopher MacLellan

Catastrophic forgetting remains a central challenge in continual learning, where models are required to integrate new knowledge over time without losing what they have previously l…

cs.CL2025

Command A: An Enterprise-Ready Large Language Model

Team Cohere, :, Aakanksha +227

In this report we describe the development of Command A, a powerful large language model purpose-built to excel at real-world enterprise use cases. Command A is an agent-optimised…

cs.CL2024

Nevermind: Instruction Override and Moderation in Large Language Models

Edward Kim

Given the impressive capabilities of recent Large Language Models (LLMs), we investigate and benchmark the most popular proprietary and different sized open source models on the ta…

cs.CL2023★ 5 cited

Elo Uncovered: Robustness and Best Practices in Language Model Evaluation

Meriem Boubdir, Edward Kim, Beyza Ermis +2

In Natural Language Processing (NLP), the Elo rating system, originally designed for ranking players in dynamic games such as chess, is increasingly being used to evaluate Large La…

cs.CL2023★ 4 cited

Which Prompts Make The Difference? Data Prioritization For Efficient Human LLM Evaluation

Meriem Boubdir, Edward Kim, Beyza Ermis +2

Human evaluation is increasingly critical for assessing large language models, capturing linguistic nuances, and reflecting user preferences more accurately than traditional automa…

cs.CV2023

DiffuGen: Adaptable Approach for Generating Labeled Image Datasets using Stable Diffusion Models

Michael Shenoda, Edward Kim

Generating high-quality labeled image datasets is crucial for training accurate and robust machine learning models in the field of computer vision. However, the process of manually…