Summary of Glider: Grading Llm Interactions and Decisions Using Explainable Ranking, by Darshan Deshpande et al.
GLIDER: Grading LLM Interactions and Decisions using Explainable Ranking
by Darshan Deshpande, Selvan Sunitha Ravi, Sky CH-Wang, Bartosz Mielczarek, Anand Kannappan, Rebecca Qian
First submitted to arxiv on: 18 Dec 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 paper introduces GLIDER, a powerful Large Language Model (LLM) that can evaluate model outputs on arbitrary user-defined criteria. Unlike closed-source LLMs, which struggle with fine-grained metrics and explainability, GLIDER addresses these limitations by scoring text inputs and associated context using 685 domains and 183 criteria. Compared to GPT-4o, GLIDER shows higher Pearson’s correlation on FLASK and outperforms prior evaluation models, achieving comparable performance to LLMs 17x its size. The model supports fine-grained scoring, multilingual reasoning, span highlighting, and is trained on a vast dataset of user-defined criteria. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GLIDER is a new kind of computer program that can evaluate how good other programs are at understanding text. Right now, people have to use special machines called Large Language Models (LLMs) to do this evaluation. But these LLMs are not very good at explaining why they made certain decisions or working with different types of languages and tasks. The new GLIDER program is better than the old ones because it can be trained on lots of different criteria, like how well a program does a specific task or how well it understands a certain type of language. This makes GLIDER very good at evaluating programs that do different things. |
Keywords
» Artificial intelligence » Gpt » Large language model
