The landscape of large language models (LLMs) evaluation is expanding, with various benchmarks emerging to gauge their capabilities across distinct domains. These benchmarks offer nuanced insights into LLMs’ performance on tasks that encompass coding proficiency, natural language understanding, multilingual comprehension, and more. Examining LLMs on these benchmarks provides a comprehensive picture of LLMs’ strengths and limitations.
Even though there is growing discussion about how reliable it is to trust the models based on the metrics, it is still essential to note the viability of the model and comprehend its capabilities, just like comparing your model against GPT.
While LLMs showcase promise, they continue to grapple with complexities inherent to language, coding, and context across these diverse evaluations. However, like the AI models, the benchmarks are constantly evolving, and will continue to do so.
Here are 5 benchmarks to evaluate the efficiency of language models-
HumanEval
The HumanEval benchmark is a set of 164 programming problems specifically created to evaluate the coding capabilities of large language models (LLMs). These problems cover a range of skills, including understanding language, working with algorithms, and basic mathematical operations.
Each problem within the HumanEval benchmark is presented in the form of a docstring, a concise piece of text that outlines the problem’s description and the expected outcome. The LLM’s task is to generate Python code that effectively solves the problem, based on the given docstring. This generated code is then evaluated by a human judge to determine its correctness and functionality.
Although the HumanEval benchmark is relatively new, it has already been employed to assess several LLMs, such as GPT-3, LLAMA, LLAMA 2 and PaLM. These evaluations have indicated that LLMs possess the ability to produce accurate and functional code. However, it’s worth noting that they still make errors, particularly on more complex challenges.
MBPP (Mostly Basic Python Programming)
The MBPP benchmark, stands for Mostly Basic Python Programming, is a collection of 1,000 Python programming problems sourced from the crowd. Its purpose is to assess the code generation capabilities of large language models (LLMs). The problems are intentionally designed to be solvable by individuals at an introductory programming level, using core programming concepts and standard library functionalities.
Each problem within the MBPP benchmark consists of three components: a concise task description, a Python code solution, and three automated test cases. The task description provides a brief explanation of the problem, while the code solution entails a Python function crafted to resolve the given problem. The automated test cases serve the purpose of confirming the accuracy of the provided code solution.
Although the MBPP benchmark is currently in its developmental phase, it has already been employed to evaluate several LLMs. Notable among these are LEVER + Codex, Reviewer + Codex002, MBR-Exec. The results of these evaluations demonstrate the capability of LLMs to generate functional and correct code for fundamental Python programming problems.
MMLU (5-shot)
MMLU, which stands for Multilingual Multitask Learning for Understanding, serves as an evaluation benchmark for large language models (LLMs) to execute diverse natural language understanding tasks in various languages. Covering question answering, summarization, translation, natural language inference, and dialogue tasks among the total 57 tasks, MMLU is crafted to be challenging, necessitating robust language comprehension by LLMs.
Evaluation considers accuracy and fluency, measuring correct responses and natural coherence. Utilized in assessing LLMs like Flan-PaLM 2, Codex + REPLUG LSR, Chinchilla, MMLU reveals LLMs’ capacity for multilingual understanding tasks, even though errors persist in intricate challenges.
TriviaQA (1-shot)
The TriviaQA (1-shot) benchmark assesses the capacity of large language models (LLMs) to respond to questions using just one training instance. This dataset comprises 100,000 questions and answers, categorized into 10,000 training, 10,000 validation, and 80,000 test examples. Questions span varying levels of difficulty, occasionally demanding real-world knowledge or common sense.
In the 1-shot framework, each question is assigned a sole training example for the LLM. This intensifies the challenge as the LLM must generalize from this single instance to address similar questions.
Various LLMs, such as PaLM 2-L, GLaM 62B/64E, FiE+PAQ have been evaluated using the 1-shot TriviaQA benchmark. While these evaluations indicate LLMs’ competence in answering questions with only one training example, errors persist, particularly with tougher questions.
BIG (Beyond the Imitation Game) -Bench Hard
The BIG Bench Hard serves as an extensive evaluation tool for large language models (LLMs), an initiative established by Clark et al. in 2021. Comprising over 200 tasks, the BIG-bench benchmark spans a diverse array of tasks categorized into 10 distinct categories.
These categories encompass a spectrum of language understanding tasks, including textual entailment, question answering, natural language inference, commonsense reasoning, code completion, translation, summarization, data analysis, creative writing, and miscellaneous tasks such as sentiment analysis and creative text generation. The benchmark is meticulously designed to challenge LLMs, requiring them to showcase various skills and abilities across an extensive range of tasks.
Designed with an extensible framework, the BIG-bench benchmark can accommodate the addition of new tasks as they are developed, enabling it to stay up-to-date with emerging language understanding challenges. This adaptability ensures that it remains a relevant and dynamic benchmark for assessing the evolving capabilities of LLMs.
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