Summary of Considers-the-human Evaluation Framework: Rethinking Human Evaluation For Generative Large Language Models, by Aparna Elangovan et al.
ConSiDERS-The-Human Evaluation Framework: Rethinking Human Evaluation for Generative Large Language Models
by Aparna Elangovan, Ling Liu, Lei Xu, Sravan Bodapati, Dan Roth
First submitted to arxiv on: 28 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This position paper argues that evaluating large language models (LLMs) requires a multidisciplinary approach combining insights from user experience research, human behavioral psychology, and other fields. To ensure reliable results, the evaluation must consider factors like usability, aesthetics, cognitive biases, and scalability. The authors highlight how cognitive biases can affect rating scores and propose an evaluation framework, ConSiDERS-The-Human, consisting of six pillars: Consistency, Scoring Criteria, Differentiating, User Experience, Responsible, and Scalability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Generative large language models (LLMs) are becoming increasingly powerful, but evaluating their performance is crucial to wider adoption. In this position paper, experts argue that human evaluation should be multidisciplinary, considering factors like usability, aesthetics, and cognitive biases. The goal is to ensure reliable results and highlight the capabilities and weaknesses of these powerful models. To achieve this, a new evaluation framework, ConSiDERS-The-Human, is proposed, consisting of six key pillars. |
