Benchmarks orient AI. They encapsulate ideals and priorities that describe how the AI community should progress. When properly developed and analyzed, they allow the larger community to understand better and influence the direction of AI technology. The AI technology that has evolved the most in recent years is foundation models, highlighted by the advent of language models. A language model is essentially a box that accepts text and generates text. Despite their simplicity, these models may be customized (e.g., prompted or fine-tuned) to a wide range of downstream scenarios when trained on vast amounts of comprehensive data. However, there still needs to be more knowledge on the enormous surface of model capabilities, limits, and threats. They must benchmark language models holistically due to their fast growth, growing importance, and limited comprehension. But what does it mean to evaluate language models from a global perspective?
Language models are general-purpose text interfaces that may be used in various circumstances. And for each scenario, they may have a long list of requirements: models should be accurate, resilient, fair, and efficient, for example. In truth, the relative relevance of various desires is frequently determined by one’s perspective and ideals and the circumstance itself (e.g., inference efficiency might be of greater importance in mobile applications). They think that holistic assessment includes three components:
- Broad coverage and acknowledgment of incompleteness: Given the huge surface of capabilities and dangers associated with language models, they must examine language models over various scenarios. Broadening the assessment has been a constant trend in the NLP field, progressing from single datasets like SQuAD to small collections of datasets like SuperGLUE to big collections of datasets like the GPT-3 evaluation suite, Eleuther AI LM Harness, and BIGBench. However, it is practical to analyze only some of the circumstances or desiderata that (may) apply to LMs. As a result, holistic evaluation should give a top-down taxonomy and make clear all missing important situations and indicators.
- Measuring on several metrics: Systems that are advantageous to society reflect more than simply accuracy. These several desires should be evaluated holistically, weighing each want against each possibility under consideration.
- Normalization: It is not a scenario-specific system, but the language model is what they are evaluating. Therefore, the method for tailoring an LM to a scenario should be accounted for to compare several LMs fairly.
Furthermore, to the greatest degree practicable, each LM should be tested in the same situations. Overall, holistic assessment increases transparency by analyzing language models as a whole. Rather than focusing on a single feature, they aim for a more comprehensive characterization of language models to advance scientific knowledge and guide societal influence. Researchers refer to it as HELM (Holistic Evaluation of Language Models). It is divided into two parts: (i) an abstract taxonomy of situations and metrics to define the design space for language model assessment and (ii) a concrete collection of implemented scenarios and metrics chosen to prioritize coverage. The HELM framework is completely open-sourced on GitHub.
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Aneesh Tickoo is a consulting intern at MarktechPost. He is currently pursuing his undergraduate degree in Data Science and Artificial Intelligence from the Indian Institute of Technology(IIT), Bhilai. He spends most of his time working on projects aimed at harnessing the power of machine learning. His research interest is image processing and is passionate about building solutions around it. He loves to connect with people and collaborate on interesting projects.