Data Classification Model Evaluation Quiz

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Questions and Answers

What is the title of the work by Leo Gao, Stella Biderman, and others that presents an 800gb dataset for language modeling?

The Pile: An 800gb Dataset of Diverse Text

In which year was 'Easylm: A Simple and Scalable Training Framework for Large Language Models' published?

2023

Which organization partially funded the work of Leo Gao, Stella Biderman, and others?

ELIAI (The Edinburgh Laboratory for Integrated Artificial Intelligence)

Who are the authors of the work 'Pre-training to learn in context' presented at ACL 2023?

Yuxian Gu, Li Dong, Furu Wei, Minlie Huang Signup and view all the answers

Which work discusses 'Faster attention with better parallelism and work partitioning'?

Flashattention-2: Faster Attention with Better Parallelism Signup and view all the answers

Where was the work 'Pre-training to learn in context' presented?

Toronto, Canada Signup and view all the answers

According to Table 3, which few-shot example number had the highest Exact Match scores on closed-book closed-book QA tasks?

25 Signup and view all the answers

Which models, according to Figure 2, obtained similar results for both 2K and 8K settings using random packing strategies?

MixChunk and UniChunk Signup and view all the answers

Which model, according to the context, did not improve the accuracy when increasing the number of few-shot demonstrations for 8K models?

MixChunk Signup and view all the answers

According to Table 2, how many demonstrations were used for 2K models in few-shot learning settings?

20 Signup and view all the answers

Based on the context, which dataset was not used for few-shot learning experiments?

IMDb movie reviews dataset Signup and view all the answers

Which model demonstrates superior performance using causal masking in pre-training chunks?

U NIChunk Signup and view all the answers

Which statement is true about the Exact Match scores of 8K models compared to 2K models, as shown in Table 3?

The Exact Match scores of 8K models were higher than those of 2K models when using fewer few-shot examples, but lower when using more few-shot examples Signup and view all the answers

Which model obtains a significantly higher accuracy compared to B M 25Chunk on the 8K setting?

I NTRADoc Signup and view all the answers

Which of the following statements correctly describes a possible implication of the results presented in Figure 2?

Using a longer context window size can result in increased distractions for causal masking pre-training. Signup and view all the answers

What datasets were used to evaluate the knowledge memorisation properties of the models?

NaturalQuestions (NQ) and TriviaQA (TQA) Signup and view all the answers

How many demonstrations were used for the 2K and 8K models, respectively?

12 and 48 demonstrations Signup and view all the answers

What metric was used to calculate the mean scores in Table 3?

Exact Match (EM) Signup and view all the answers

Study Notes

Data Distributional Properties and Emergent In-Context Learning in Transformers

Data distributional properties drive emergent in-context learning in transformers.
Work was supported by the Edinburgh International Data Facility (EIDF) and the DataDriven Innovation Programme at the University of Edinburgh.

FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning

FlashAttention-2 is a method that provides faster attention with better parallelism and work partitioning.
The authors of FlashAttention-2 were partially funded by ELIAI, EPSRC, Cisco, Accenture LLP, and received GPU donations from NVIDIA.

The Pile: An 800GB Dataset of Diverse Text for Language Modeling

The Pile is an 800GB dataset of diverse text for language modeling.
The dataset was introduced by Leo Gao, Stella Biderman, Sid Black, Laurence Golding, and others in 2021.

Easylm: A Simple and Scalable Training Framework for Large Language Models

Easylm is a simple and scalable training framework for large language models.
Easylm was introduced by Xinyang Geng in 2023.

Pre-training to Learn in Context

Pre-training to learn in context is a method introduced by Yuxian Gu, Li Dong, Furu Wei, and Minlie Huang in 2023.
This method was presented in the 61st Annual Meeting of the Association for Computational Linguistics.

Model Performance and In-Context Learning Accuracy

The average in-context learning accuracy of models using different numbers of few-shot demonstrations is presented in Figure 2.
Models pre-trained using causal masking show that U NIChunk produces more accurate results than M IXChunk, while B M 25Chunk yields a higher average accuracy than M IXChunk for 2K and 8K models.

Knowledge Memorisation

Knowledge memorisation is evaluated using two open-domain question-answering (ODQA) datasets: NaturalQuestions (NQ) and TriviaQA (TQA).
The mean Exact Match (EM) scores are calculated based on 5 different sets of demonstrations, with 12 demonstrations for 2K models and 48 demonstrations for 8K models.

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