Certainly! Large Language Models (LLMs) require rigorous benchmarking to evaluate their performance comprehensively. Below, I will describe some of the standard benchmarks for LLMs, illustrate how they measure various aspects of model performance, and provide sources that were utilized to construct this answer.

1. 1. GLUE (General Language Understanding Evaluation)

The GLUE benchmark is one of the most commonly used evaluation frameworks for LLMs. It assesses models based on a diversified set of tasks including sentiment analysis, linguistic acceptability, text similarity, question answering, and more.

1. Examples of GLUE tasks:
   - MNLI (Multi-Genre Natural Language Inference): Evaluates the model’s ability to determine the relationship between pairs of sentences.
   - QQP (Quora Question Pairs): Tests the model’s ability to identify whether two questions are semantically equivalent.
   - SST-2 (Stanford Sentiment Treebank): Measures the model’s performance on binary sentiment classification tasks.

1. Source:
   - Wang, Alex, et al. “GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding.” arXiv preprint arXiv:1804.07461 (2018).

1. 2. SUPERGLUE

SUPERGLUE is an extension to GLUE, specifically designed to contain more challenging tasks that go beyond the capabilities of models evaluated on GLUE.

1. Examples of SUPERGLUE tasks:
   - BoolQ: Entails answering Yes/No questions based on given paragraphs.
   - ReCoRD (Reading Comprehension with Commonsense Reasoning Dataset): Assesses the ability to reason and fill in the blanks in a passage using commonsense knowledge.

1. Source:
   - Wang, Alex, et al. “SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems.” arXiv preprint arXiv:1905.00537 (2019).

1. 3. SQuAD (Stanford Question Answering Dataset)

SQuAD benchmarks LLMs by their ability to answer questions from a set of Wikipedia articles. It mainly tests the reading comprehension abilities of the model.

1. Examples of SQuAD tasks:
   - SQuAD 1.1: Provides a context paragraph and asks questions based on that paragraph. The answer has to be a span of text from the paragraph.
   - SQuAD 2.0: Includes questions unanswerable from the paragraph, making the model discern between ‘answerable’ and ‘unanswerable’ questions.

1. Source:
   - Rajpurkar, Pranav, et al. “SQuAD: 100,000+ Questions for Machine Comprehension of Text.” arXiv preprint arXiv:1606.05250 (2016).

1. 4. HellaSwag

HellaSwag is designed to evaluate models on commonsense reasoning which is often difficult for many LLMs. It typically involves completing sentences with the most plausible ending out of multiple choices.

1. Example:
   Given a partially completed sentence, choose the most accurate and sensible completion from multiple options.

1. Source:
   - Zellers, Rowan, et al. “HellaSwag: Can a Machine Really Finish Your Sentence?.” arXiv preprint arXiv:1905.07830 (2019).

1. 5. CoQA (Conversational Question Answering Challenge)

CoQA aims to evaluate a model’s ability to handle conversational question answering, taking into account context from the entire conversation history.

1. Example:
   A question-answering sequence based on a conversation history pertaining to various topics such as literature, history, etc.

1. Source:
   - Reddy, Siva, Danqi Chen, and Christopher D. Manning. “CoQA: A Conversational Question Answering Challenge.” Transactions of the Association for Computational Linguistics 7 (2019): 249-266.

1. 6. LAMBADA

LAMBADA is a word prediction task assessing whether models can predict the last word of a given passage. The challenge is particularly pertinent to modeling wider context understanding.

1. Example:
   Given a passage, the model has to predict the last word of the passage.

1. Source:
   - Paperno, Denis, et al. “The LAMBADA dataset: Word prediction requiring a broad discourse context.” arXiv preprint arXiv:1606.06031 (2016).

1. Conclusion

Using benchmarks like GLUE, SUPERGLUE, SQuAD, HellaSwag, CoQA, and LAMBADA ensures that models are assessed on a variety of critical language understanding and generation tasks. These benchmarks represent a blend of synthetic and real-world scenarios, demanding a wide range of capabilities from LLMs.

1. Sources Used:
   1. Wang, Alex, et al. “GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding.” arXiv preprint arXiv:1804.07461 (2018).
   2. Wang, Alex, et al. “SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems.” arXiv preprint arXiv:1905.00537 (2019).
   3. Rajpurkar, Pranav, et al. “SQuAD: 100,000+ Questions for Machine Comprehension of Text.” arXiv preprint arXiv:1606.05250 (2016).
   4. Zellers, Rowan, et al. “HellaSwag: Can a Machine Really Finish Your Sentence?.” arXiv preprint arXiv:1905.07830 (2019).
   5. Reddy, Siva, Danqi Chen, and Christopher D. Manning. “CoQA: A Conversational Question Answering Challenge.” Transactions of the Association for Computational Linguistics 7 (2019): 249-266.
   6. Paperno, Denis, et al. “The LAMBADA dataset: Word prediction requiring a broad discourse context.” arXiv preprint arXiv:1606.06031 (2016).

DinoGeek offers simple articles on complex technologies

Would you like to be quoted in this article? It's very simple, contact us at dino@eiki.fr

CSS | NodeJS | DNS | DMARC | MAPI | NNTP | htaccess | PHP | HTTPS | Drupal | WEB3 | LLM | Wordpress | TLD | Domain name | IMAP | TCP | NFT | MariaDB | FTP | Zigbee | NMAP | SNMP | SEO | E-Mail | LXC | HTTP | MangoDB | SFTP | RAG | SSH | HTML | ChatGPT API | OSPF | JavaScript | Docker | OpenVZ | ChatGPT | VPS | ZIMBRA | SPF | UDP | Joomla | IPV6 | BGP | Django | Reactjs | DKIM | VMWare | RSYNC | Python | TFTP | Webdav | FAAS | Apache | IPV4 | LDAP | POP3 | SMTP