Summary of Evaluating Cost-accuracy Trade-offs in Multimodal Search Relevance Judgements, by Silvia Terragni et al.
Evaluating Cost-Accuracy Trade-offs in Multimodal Search Relevance Judgements
by Silvia Terragni, Hoang Cuong, Joachim Daiber, Pallavi Gudipati, Pablo N. Mendes
First submitted to arxiv on: 25 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Information Retrieval (cs.IR)
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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 study evaluates several Large Language Models (LLMs) and Multimodal Language Models (MLLMs) to determine which models consistently perform well across various search scenarios. The analysis assesses the trade-off between cost and accuracy, revealing significant variations in performance depending on the context. Notably, smaller models with visual components may actually degrade performance rather than improve it. These findings underscore the complexities involved in selecting the most suitable model for practical applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well big language models can help find relevant information. Right now, there’s no clear guide on which models work best in different situations or use cases. The study compares several models to see which ones are closest to what humans think is important. It also explores the balance between cost and accuracy, showing that each model performs differently depending on the context. Interestingly, sometimes adding visual elements can actually make things worse. This shows how hard it is to choose the right model for real-world applications. |
