Fundamental Large Language Models (LLMs) such as GPT-4, Gemini, and Claude have demonstrated notable capabilities, matching or exceeding human performance. In this context, benchmarks become difficult but necessary tools for distinguishing various models and pinpointing their limitations. Comprehensive evaluations of language models have been done in order to examine models in a number of different dimensions. An integrated assessment framework is becoming more and more crucial as generative AI moves beyond a language-only approach to include other modalities.Â
Evaluations that are transparent, standardized, and reproducible are essential, but there isn’t one comprehensive technique for language models or multimodal models currently. Custom evaluation pipelines with varying degrees of data preparation, output postprocessing, and metrics calculation are frequently developed by model developers. Transparency and reproducibility are hampered by this fluctuation.
In order to solve this, a team of researchers from LMMs-Lab Team and S-Lab, NTU, Singapore, has created LMMS-EVAL, a standardized and trustworthy benchmark suite made to evaluate multimodal models as a whole. More than ten multimodal models and about 30 variants are evaluated by LMMS-EVAL, which spans more than 50 tasks in a variety of contexts. It has a uniform interface to make it easier to integrate new models and datasets, and it offers a standardized assessment pipeline to guarantee openness and repeatability.
Reaching a benchmark that is contaminant-free, low-cost, and widely covered is a difficult and frequently paradoxical objective. A common term for this is the impossible triangle. An affordable method for assessing language models on a variety of tasks is the Hugging Face OpenLLM leaderboard, although it is prone to contamination and overfitting. On the other hand, because they require a lot of human input, rigorous evaluations like those from the LMSys Chatbot Arena and AI2 WildVision that rely on actual user interactions are more costly.
Realizing how hard it is to break through this impenetrable triangle, the team has added LMMS-EVAL LITE and LiveBench to the LMM evaluation scene. Because LMMS-EVAL LITE concentrates on a variety of tasks and eliminates superfluous data instances, it offers an affordable, comprehensive evaluation. LiveBench, on the other hand, provides a cheap and broadly applicable method of running benchmarks by creating test data using the most recent information obtained from news and internet forums.
The team has summarized their primary contributions as follows.
LMMS-EVAL is a unified multimodal model evaluation suite that evaluates over ten models with over 30 sub-variants and covers over 50 tasks. The goal of LMMS-EVAL is to ensure that comparisons between various models are impartial and consistent by streamlining and standardizing the evaluation process.
An effective version of the entire evaluation set is called LMMS-EVAL LITE. Eliminating pointless data instances lowers expenses while providing dependable and consistent results with a thorough LMMS-EVAL. Because LMMS-EVAL LITE preserves good evaluation quality, it’s an affordable substitute for in-depth model evaluations.
LIVEBENCH benchmark evaluates models’ zero-shot generalization ability on current events by using up-to-date data from news and forum websites. LIVEBENCH offers an affordable and broadly applicable approach to assess multimodal models, guaranteeing their continued applicability and precision in ever-changing, real-world situations.
In conclusion, solid benchmarks are essential to the advancement of AI. They offer the essential information to distinguish between models, spot flaws, and direct future advancements. Standardized, clear, and repeatable benchmarks are becoming increasingly important as AI develops, particularly when it comes to multimodal models. LMMS-EVAL, LMMS-EVAL LITE, and LiveBench are intended to close the gaps in the existing assessment frameworks and facilitate the continuous development of AI.
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