Standardized Benchmarks

Standardized Benchmarks#

Standardized benchmarks are important for evaluating LLMs because they provide a consistent and objective way to compare the performance of different models across various tasks. This allows researchers and developers to accurately assess progress, identify strengths and weaknesses, all while ensuring fair comparisons between different LLMs.

Overview#

These benchmarks assess a model’s general and domain-specific knowledge, its comprehension and ability for commonsense reasoning and logical analysis, entity recognition, factuality and truthfulness, as well as mathematical and coding capabilities. In standardized benchmarks, the prompts are structured in a way so that possible answers can be predefined.

The most common method to limit the answer space for standardized tasks is asking the model to select the correct answer from set of multiple-choice options (e.g., A, B, C, D), based on its understanding and reasoning about the input. Another way is limiting the answer space to a single word or a short phrase, which can be directly extracted from the text. In this case, the model’s task is to identify the correct word/phrase that answers a question or matches the entity required. An alternative setup is asking the model to chronologically rank a set of statements, rank them to achieve logical consistency, or rank them on metrics such as plausibility/correctness, importance, or relevance. Finally, fill-in-the-blank questions, masking answer tasks, and True/False questions are also popular options for limiting the answer space.

Oumi uses EleutherAI’s LM Evaluation Harness to power scalable, high-performance evaluations of LLMs, providing robust and consistent benchmarking across a wide range of standardized tasks.

Trade-offs#

Advantages#

The closed nature of standardized benchmarks allows for more precise and objective evaluation, focusing on a model’s ability to understand, reason, and extract information accurately. The benchmarks assess a wide range of model skills in a controlled and easily quantifiable way.

  1. Objective and consistent evaluation. With a closed answer space, there are no subjective interpretations of what constitutes the correct answer, since there’s a clear right answer among a set of predefined choices. This ensures consistency in scoring, allowing evaluators to use standard metrics (F1 score, precision, recall, accuracy, etc.) in a straightforward manner. In addition, results from different models can be directly compared because the possible answers are fixed, ensuring consistency across evaluations.

  2. Reproducibility. When models are tested on the same benchmark with the same set of options, other researchers can replicate the results and verify claims, as long as (i) all the environmental settings are the same (Oumi thoroughly logs all settings that could affect evaluation variability) and (ii) the model is prompted with temperature 0.0 and a consistent seed. Reproducibility is crucial to track improvements across models or versions, as well as scientific rigor and advancing the state of the art in AI research.

  3. Task and domain diversity. These benchmarks have very wide coverage and include a broad spectrum of tasks, which can highlight specific areas where a model excels or falls short. They reflect real-world challenges and complexities. There is also a multitude of benchmarks that test a model on domain-specific intricacies, assessing its ability to apply specialized knowledge within a particular field, ensuring that evaluation is closely tied to practical performance.

  4. Low cost inference and development. In closed spaces, the model’s output is often a straightforward prediction (e.g., a multiple choice letter or a single word), which is less resource-intensive since it only requires generating a few tokens (vs. a complex full-text response). In addition, the model doesn’t need to consider an infinite range of possible responses, it can focus its reasoning or search on a smaller, fixed set of options, also contributing in faster inference. Developing such benchmarks also involves a simpler annotation process and low-cost labelling.

Limitations#

While standardized benchmarks offer several advantages, they also come with several limitations compared to generative benchmarks, especially in assessing the broader, more complex language abilities that are required in many real-world applications such as creativity or nuanced reasoning.

  1. Open-ended problem solving and novelty: Models are not tested on their ability to generate creative or novel responses, explain the steps required to address a problem, being aware of the previous context to keep a conversation engaging, or to handle tasks where there isn’t a single correct answer. Many real-world applications, such as conversational agents, generating essays and stories, or summarization demand open-ended problem solving.

  2. Language quality and human alignment. In tasks that require text generation, the style, fluency, and coherence of a model’s output are crucial. Closed-answer benchmarks do not assess how well a model can generate meaningful, varied, or contextually rich language. Adapting to a persona or tone, if requested by the user, is also not assessed. Finally, alignment with human morals and social norms, being diplomatic when asked controversial questions, understanding humor and being culturally aware are outside the scope of standardized benchmarks.

  3. Ambiguity. Closed-answer benchmarks do not evaluate the model’s ability to handle ambiguous prompts. This is a common real-word scenario and an important conversational skill for agents. Addressing ambiguity typically involves asking for clarifications, requesting more context, or engaging in a dynamic context-sensitive back-and-forth conversation with targeted questions until the user’s intention is revealed and becomes clear and actionable.

  4. Overfitting and cheating. Boosting performance on standardized benchmarks requires that the model is trained on similar benchmarks. However, since the answer space is fixed and closed, models may overfit and learn to recognize patterns that are only applicable to multiple choice answers, struggling to generalize in real-world scenarios where the “correct” answer isn’t part of a predefined set. In addition, intentionally or unintentionally training on the test set is an emerging issue, which is recently (only partially) addressed by contamination IDs.