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Edward Kim

6 papers hereh-index 3167 citations9 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • sole author1
  • middle author3
  • last author1

Across the 5 of 6 papers where every author was matched, so the position is known.

fields
  • cs.CL4
  • cs.CV1
  • cs.LG1
same name
  • Edward Kim — 5 papers, h 2
  • Edward Kim — 4 papers, h 6
  • Edward Kim — 3 papers, h 7
  • Edward Kim — 3 papers, h 6
  • Edward Kim — 3 papers, h 3
  • Edward Kim — 3 papers, h 2

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20232025
most citedWhich Prompts Make The Difference? Data Prioritization For Efficient Human LLM Evaluation

4 citations · 4 across the 5 of their papers we have counts for

collaborators
Showing cs.CLShow all

4 papers · 1 filter

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

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…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.