paper 2 question 5 writing a speech

AQA GCSE English Language Paper 2 – Revision Guide

Welcome to the complete revision guide for AQA GCSE English Language paper 2. Keep reading for our top tips and advice on each question, as we break down the English Language paper 2. Find out what to expect from each question, how to revise effectively and how to get top marks. 

To revise effectively for GCSE English Language you do need to set aside plenty of revision time. Our tutors always hear students say that they don’t need to – or even can’t – revise for English Language. That’s all wrong. You must   revise for the subject, you just need to know how. 

You should focus on:

• understanding how each of the papers is structured; 
• knowing what the examiner is looking for on each question; and
• doing lots of practice questions and marking your own work to improve quickly. 

You can also find our guide to AQA GCSE English Language Paper 1 here .

Alongside our revision guides, our team of top English tutors provide one-to-one lessons designed specifically to help you succeed in the exams. Contact us directly to book your first lesson. 

Overview of the paper

AQA GCSE English Language paper 2 explores non-fiction writers’ viewpoints and perspectives. Section A consists of 4 questions, in which you’ll analyse two linked sources across different time periods and genres. Section B consists of a single big question where you will write your own text for a specified audience, purpose and form. Here you’ll provide your own perspective on a task related to the theme that was introduced in section A. 

There is 1 hour 45 minutes to complete the paper. There are 80 marks at stake, making up 50% of your GCSE English Language qualification. 

AQA English Language Paper 2 Section A

Read the sources carefully – spend 15 mins here.

Make sure you spend the first 10-15 minutes of the exam carefully reading the sources. You should: 

• read the texts carefully and thoroughly;
• read the questions; and
• highlight important parts of the sources for use later. 

Question 1 – select 4 true statements – spend 5 mins here

The first question should be fairly straightforward but do ensure you take a few minutes to think it through carefully. You’ll be asked to focus on a small part of source A and select four true statements from a selection of eight. Be a little careful here because there will be some statements that you think could possibly be true, but you should be able to point to where you’re told this in the text for it to be true. That will confirm your choice. 

Things to remember on this question: 

• focus only on the specific section of source A; 
• pinpoint where in the text the writer tells you each of your choices; and
• don’t spend more than 5 minutes here. 

Question 2 – summary of differences or similarities – spend 10 mins here

For question 2 you need to refer to both source A and B. The question is out of 8 marks. You’ll be asked to write a summary of the differences or similarities between something/ someone in source A and something/ someone in source B. You don’t need to worry about analysing language or structure here, simply identify 3-4 differences or similarities (focused on what is in your specific question). Summarise each of the differences in turn, with quotations, and explore perceptive inferences from both texts. Inferences are not explicitly said, they are the extra layers of understanding that are suggested by the writer and which you pick up  from “reading between the lines”. To get top marks you need to examine what the writers imply about the issue.  

Things to remember for this question: 

• you do  not  need to analyse any language or structure here;
• focus on explaining perceptive inferences from both texts to explain the key differences or similarities; and
• cover three or four points with quotations. 

Question 3 – the writer’s use of language to describe something – spend 12 mins here

There are 12 marks up for grabs here. You will always be asked, ‘how does the writer use language to describe…’ followed by something specific from part of one source. You need to analyse the effects of the writer’s choice of language. Make around four points and explore them using good quotations and sophisticated use of subject terminology. The language features you pick out could include things like: metaphors, similes, alliteration, onomatopoeia, personification, sentence forms and the use of specific types of words or phrases. 

• focus on analysing the effects of the writer’s choice of language; 
• use accurate subject terminology by identifying specific language techniques; and
• cover four points with excellent quotations. 

Question 4 – comparing different perspectives – spend 18 mins here

Question 4 is a bigger, extended question with 16 marks at stake. As such, the examiner is expecting more from you. The question will always ask you to “compare how the writers convey their different perspectives/attitudes about…” something specific to the sources. You need to perceptively compare their different perspectives or attitudes, and most importantly,  compare the methods the writers use  to convey their different perspectives or attitudes. Everything is in scope here, so you can analyse any methods, including both language and structure. Aim to write about four good comparison points. These four paragraphs will cover both sources (in order to compare them properly) and they’ll be more developed than in the earlier questions. 

• keep focused on the specific task in the question;
• analyse the methods used by the writers to convey their attitudes to the topic; and
• consider both language and structure. 

AQA GCSE English Language Paper 2 Section B

Question 5 – writing to present a viewpoint – spend 45 mins here.

Question 5 provides your opportunity to show the examiner how you can write effectively to explain your point of view on a specific issue. There are a whopping 40 marks up for grabs here, so make sure you spend the full 45 mins doing this question. You’ll be given a statement on an issue related to those discussed in section A. Your task will be to write in a specific form, for a specific audience, to present your point of view on the issue. You might be asked to write a newspaper article, letter or speech, amongst other forms. The marks are allocated specifically, with 24 marks available for content and organisation and 16 marks for technical accuracy (spelling, punctuation and grammar). 

To do well in this question we recommend spending the first 5 minutes thinking deeply about the task and planning your answer. This plan will ensure your answer has a good, consistent argument and structure. When writing, make sure your style and use of language reflects the form and audience of the task. Use ambitious vocabulary, language techniques and structural features to really demonstrate what you can do. You should, of course, always ensure your spelling, punctuation and grammar are spot on. Leave a couple of minutes at the end to double check your technical accuracy. 

• match your style of writing to the purpose and audience of the task; 
• plan your answer first;
• leave the full 45 minutes to complete the task fully; and
• use ambitious vocabulary, language and structural features to present your viewpoint and really show the examiner what you can do. 

Now Keep revising for your AQA GCSE English Language Paper 2

The key thing after reading this guide is to practise. Do as many past papers and practice questions as you can. Mark your own work and try answering the questions again focusing on areas to develop. You can find all of the past AQA papers here . For fully personalised advice and support, why not try a lesson with one of our online GCSE English experts? Simply drop us a quick message and we’ll arrange your free tutoring consultation. 

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AQA English Language Paper 2 Question 5

Last updated

6 July 2023

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Resources included (20)

AQA Paper 2: Section B Speech Writing

AQA English Language Paper 2 Exam Preparation

Black History Month

Greta Thunberg Climate Change Speech

Letters of Complaint - Writing to Persuade

Assessment Planning - Writing to Argue

Assessment Planning - Formal Letter Writing

AQA English Language Paper 2 Question 5 Scheme of Work

AQA English Language Paper 2 Article Writing

AQA English Language Paper 2 June 2018

Nonfiction Writing - Speech Structure

Writing A Formal Letter

Writing Speeches - Speech Openers

• Writing to Persuade - Football

Writing to Advise - Language Paper 2

Speech Writing

Newspaper Article Writing

A collection of TWENTY English Language Paper 2 Question 5 lessons (17 x1 hour and 3x 2 hour) that cover writing to argue, writing to advise, writing to persuade, letter writing and essay writing. A great collection of differentiated activities, modelled examples, scaffolded sentences and guided peer and self reflection that enables students to learn from others and improve their non-fiction writing in preparation for AQA English Language Paper 2 Section B or Question 5.

The suggested order of lessons is as follows (although this is by no means obligatory):

• AQA Paper 2 Section B Speech Writing
• Speech Openers
• Churchill Speech Writing
• Lincoln Speech Writing
• Speech Structure
• Newspaper Writing
• Magazine Article Writing
• Greta Thunberg Speech Writing
• Black History Month - Essay Writing
• Writing to Advise
• Letter Writing - Writing A Formal Letter
• Writing to Persuade - Letters of Complaint
• AQA English Language Exam Prep/Mock prep lesson

Pack also contains:

• Assessment planning for writing to argue - could be used as a separate writing to argue lesson
• Assessment planning for letter writing - could be used as a separate letter writing lesson
• Paper 2 Question 5 revision pack
• Knowledge organiser for revision
• June 2018 AQA exam review lesson if you use this paper as a mock/prep

Check out our English Shop for loads more free and inexpensive KS3, KS4, KS5, Literacy and whole school resources.

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AQA English Language Paper 2 Question 5 (2024 onwards)

Updated: April 24, 2024

Paper 2 Question 5 is a writing task in English exams with a focus on viewpoints analysis. Section B requires presenting a personal viewpoint on a controversial statement based on provided sources. Different writing forms like letters, articles, and speeches are used to express viewpoints effectively. Understanding the audience is key for crafting convincing arguments, as demonstrated in examples like persuading parents for a holiday trip in a letter.

TABLE OF CONTENTS

Introduction to Paper 2 Question 5

Types of questions in section b, features of writing forms, audience consideration.

Paper 2 Question 5 is a writing question worth 40 marks, with 24 marks for A05 and 16 marks for A06. Section B continues from Section A, where two sources are provided for viewpoints analysis. In Section B, you present your personal viewpoint on a controversial statement.

Section B questions may ask for agreement or disagreement with a statement or explanation of a view. Examples include arguments for or against school uniform and qualities of a good school lesson.

Different forms like letters, articles, leaflets, speeches, or essays may be required in the exam. Key features include clear audience, purpose, form, and appropriate structure for the chosen form of writing.

Considering the audience is crucial for high marks. An example of persuading parents in a letter to allow a holiday trip demonstrates the importance of understanding the audience to craft a convincing argument.

Q: What is the format of Paper 2 Question 5?

A: Paper 2 Question 5 is a writing question worth 40 marks, with 24 marks for A05 and 16 marks for A06.

Q: What is the purpose of Section B in Paper 2 Question 5?

A: In Section B, the purpose is to present your personal viewpoint on a controversial statement.

Q: What are some examples of topics that may be covered in Section B?

A: Examples include arguments for or against school uniform and qualities of a good school lesson.

Q: What are the key features to consider in Section B?

A: Key features include clear audience, purpose, form, and appropriate structure for the chosen form of writing.

Q: Why is considering the audience crucial in Section B?

A: Considering the audience is crucial for high marks as it demonstrates an understanding of who you are addressing and tailoring your writing accordingly.

Q: Can you provide an example that demonstrates the importance of understanding the audience in Section B?

A: An example of persuading parents in a letter to allow a holiday trip showcases the significance of understanding the audience to craft a convincing argument.

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Today, we’re excited to share the first two models of the next generation of Llama, Meta Llama 3, available for broad use. This release features pretrained and instruction-fine-tuned language models with 8B and 70B parameters that can support a broad range of use cases. This next generation of Llama demonstrates state-of-the-art performance on a wide range of industry benchmarks and offers new capabilities, including improved reasoning. We believe these are the best open source models of their class, period. In support of our longstanding open approach, we’re putting Llama 3 in the hands of the community. We want to kickstart the next wave of innovation in AI across the stack—from applications to developer tools to evals to inference optimizations and more. We can’t wait to see what you build and look forward to your feedback.

Our goals for Llama 3

With Llama 3, we set out to build the best open models that are on par with the best proprietary models available today. We wanted to address developer feedback to increase the overall helpfulness of Llama 3 and are doing so while continuing to play a leading role on responsible use and deployment of LLMs. We are embracing the open source ethos of releasing early and often to enable the community to get access to these models while they are still in development. The text-based models we are releasing today are the first in the Llama 3 collection of models. Our goal in the near future is to make Llama 3 multilingual and multimodal, have longer context, and continue to improve overall performance across core LLM capabilities such as reasoning and coding.

State-of-the-art performance

Our new 8B and 70B parameter Llama 3 models are a major leap over Llama 2 and establish a new state-of-the-art for LLM models at those scales. Thanks to improvements in pretraining and post-training, our pretrained and instruction-fine-tuned models are the best models existing today at the 8B and 70B parameter scale. Improvements in our post-training procedures substantially reduced false refusal rates, improved alignment, and increased diversity in model responses. We also saw greatly improved capabilities like reasoning, code generation, and instruction following making Llama 3 more steerable.

*Please see evaluation details for setting and parameters with which these evaluations are calculated.

In the development of Llama 3, we looked at model performance on standard benchmarks and also sought to optimize for performance for real-world scenarios. To this end, we developed a new high-quality human evaluation set. This evaluation set contains 1,800 prompts that cover 12 key use cases: asking for advice, brainstorming, classification, closed question answering, coding, creative writing, extraction, inhabiting a character/persona, open question answering, reasoning, rewriting, and summarization. To prevent accidental overfitting of our models on this evaluation set, even our own modeling teams do not have access to it. The chart below shows aggregated results of our human evaluations across of these categories and prompts against Claude Sonnet, Mistral Medium, and GPT-3.5.

Preference rankings by human annotators based on this evaluation set highlight the strong performance of our 70B instruction-following model compared to competing models of comparable size in real-world scenarios.

Our pretrained model also establishes a new state-of-the-art for LLM models at those scales.

To develop a great language model, we believe it’s important to innovate, scale, and optimize for simplicity. We adopted this design philosophy throughout the Llama 3 project with a focus on four key ingredients: the model architecture, the pretraining data, scaling up pretraining, and instruction fine-tuning.

Model architecture

In line with our design philosophy, we opted for a relatively standard decoder-only transformer architecture in Llama 3. Compared to Llama 2, we made several key improvements. Llama 3 uses a tokenizer with a vocabulary of 128K tokens that encodes language much more efficiently, which leads to substantially improved model performance. To improve the inference efficiency of Llama 3 models, we’ve adopted grouped query attention (GQA) across both the 8B and 70B sizes. We trained the models on sequences of 8,192 tokens, using a mask to ensure self-attention does not cross document boundaries.

Training data

To train the best language model, the curation of a large, high-quality training dataset is paramount. In line with our design principles, we invested heavily in pretraining data. Llama 3 is pretrained on over 15T tokens that were all collected from publicly available sources. Our training dataset is seven times larger than that used for Llama 2, and it includes four times more code. To prepare for upcoming multilingual use cases, over 5% of the Llama 3 pretraining dataset consists of high-quality non-English data that covers over 30 languages. However, we do not expect the same level of performance in these languages as in English.

To ensure Llama 3 is trained on data of the highest quality, we developed a series of data-filtering pipelines. These pipelines include using heuristic filters, NSFW filters, semantic deduplication approaches, and text classifiers to predict data quality. We found that previous generations of Llama are surprisingly good at identifying high-quality data, hence we used Llama 2 to generate the training data for the text-quality classifiers that are powering Llama 3.

We also performed extensive experiments to evaluate the best ways of mixing data from different sources in our final pretraining dataset. These experiments enabled us to select a data mix that ensures that Llama 3 performs well across use cases including trivia questions, STEM, coding, historical knowledge, etc.

Scaling up pretraining

To effectively leverage our pretraining data in Llama 3 models, we put substantial effort into scaling up pretraining. Specifically, we have developed a series of detailed scaling laws for downstream benchmark evaluations. These scaling laws enable us to select an optimal data mix and to make informed decisions on how to best use our training compute. Importantly, scaling laws allow us to predict the performance of our largest models on key tasks (for example, code generation as evaluated on the HumanEval benchmark—see above) before we actually train the models. This helps us ensure strong performance of our final models across a variety of use cases and capabilities.

We made several new observations on scaling behavior during the development of Llama 3. For example, while the Chinchilla-optimal amount of training compute for an 8B parameter model corresponds to ~200B tokens, we found that model performance continues to improve even after the model is trained on two orders of magnitude more data. Both our 8B and 70B parameter models continued to improve log-linearly after we trained them on up to 15T tokens. Larger models can match the performance of these smaller models with less training compute, but smaller models are generally preferred because they are much more efficient during inference.

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Instruction fine-tuning

To fully unlock the potential of our pretrained models in chat use cases, we innovated on our approach to instruction-tuning as well. Our approach to post-training is a combination of supervised fine-tuning (SFT), rejection sampling, proximal policy optimization (PPO), and direct preference optimization (DPO). The quality of the prompts that are used in SFT and the preference rankings that are used in PPO and DPO has an outsized influence on the performance of aligned models. Some of our biggest improvements in model quality came from carefully curating this data and performing multiple rounds of quality assurance on annotations provided by human annotators.

Learning from preference rankings via PPO and DPO also greatly improved the performance of Llama 3 on reasoning and coding tasks. We found that if you ask a model a reasoning question that it struggles to answer, the model will sometimes produce the right reasoning trace: The model knows how to produce the right answer, but it does not know how to select it. Training on preference rankings enables the model to learn how to select it.

Building with Llama 3

Our vision is to enable developers to customize Llama 3 to support relevant use cases and to make it easier to adopt best practices and improve the open ecosystem. With this release, we’re providing new trust and safety tools including updated components with both Llama Guard 2 and Cybersec Eval 2, and the introduction of Code Shield—an inference time guardrail for filtering insecure code produced by LLMs.

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A system-level approach to responsibility

We have designed Llama 3 models to be maximally helpful while ensuring an industry leading approach to responsibly deploying them. To achieve this, we have adopted a new, system-level approach to the responsible development and deployment of Llama. We envision Llama models as part of a broader system that puts the developer in the driver’s seat. Llama models will serve as a foundational piece of a system that developers design with their unique end goals in mind.

Instruction fine-tuning also plays a major role in ensuring the safety of our models. Our instruction-fine-tuned models have been red-teamed (tested) for safety through internal and external efforts. ​​Our red teaming approach leverages human experts and automation methods to generate adversarial prompts that try to elicit problematic responses. For instance, we apply comprehensive testing to assess risks of misuse related to Chemical, Biological, Cyber Security, and other risk areas. All of these efforts are iterative and used to inform safety fine-tuning of the models being released. You can read more about our efforts in the model card .

Llama Guard models are meant to be a foundation for prompt and response safety and can easily be fine-tuned to create a new taxonomy depending on application needs. As a starting point, the new Llama Guard 2 uses the recently announced MLCommons taxonomy, in an effort to support the emergence of industry standards in this important area. Additionally, CyberSecEval 2 expands on its predecessor by adding measures of an LLM’s propensity to allow for abuse of its code interpreter, offensive cybersecurity capabilities, and susceptibility to prompt injection attacks (learn more in our technical paper ). Finally, we’re introducing Code Shield which adds support for inference-time filtering of insecure code produced by LLMs. This offers mitigation of risks around insecure code suggestions, code interpreter abuse prevention, and secure command execution.

With the speed at which the generative AI space is moving, we believe an open approach is an important way to bring the ecosystem together and mitigate these potential harms. As part of that, we’re updating our Responsible Use Guide (RUG) that provides a comprehensive guide to responsible development with LLMs. As we outlined in the RUG, we recommend that all inputs and outputs be checked and filtered in accordance with content guidelines appropriate to the application. Additionally, many cloud service providers offer content moderation APIs and other tools for responsible deployment, and we encourage developers to also consider using these options.

Deploying Llama 3 at scale

Llama 3 will soon be available on all major platforms including cloud providers, model API providers, and much more. Llama 3 will be everywhere .

Our benchmarks show the tokenizer offers improved token efficiency, yielding up to 15% fewer tokens compared to Llama 2. Also, Group Query Attention (GQA) now has been added to Llama 3 8B as well. As a result, we observed that despite the model having 1B more parameters compared to Llama 2 7B, the improved tokenizer efficiency and GQA contribute to maintaining the inference efficiency on par with Llama 2 7B.

For examples of how to leverage all of these capabilities, check out Llama Recipes which contains all of our open source code that can be leveraged for everything from fine-tuning to deployment to model evaluation.

What’s next for Llama 3?

The Llama 3 8B and 70B models mark the beginning of what we plan to release for Llama 3. And there’s a lot more to come.

Our largest models are over 400B parameters and, while these models are still training, our team is excited about how they’re trending. Over the coming months, we’ll release multiple models with new capabilities including multimodality, the ability to converse in multiple languages, a much longer context window, and stronger overall capabilities. We will also publish a detailed research paper once we are done training Llama 3.

To give you a sneak preview for where these models are today as they continue training, we thought we could share some snapshots of how our largest LLM model is trending. Please note that this data is based on an early checkpoint of Llama 3 that is still training and these capabilities are not supported as part of the models released today.

We’re committed to the continued growth and development of an open AI ecosystem for releasing our models responsibly. We have long believed that openness leads to better, safer products, faster innovation, and a healthier overall market. This is good for Meta, and it is good for society. We’re taking a community-first approach with Llama 3, and starting today, these models are available on the leading cloud, hosting, and hardware platforms with many more to come.

Try Meta Llama 3 today

We’ve integrated our latest models into Meta AI, which we believe is the world’s leading AI assistant. It’s now built with Llama 3 technology and it’s available in more countries across our apps.

You can use Meta AI on Facebook, Instagram, WhatsApp, Messenger, and the web to get things done, learn, create, and connect with the things that matter to you. You can read more about the Meta AI experience here .

Visit the Llama 3 website to download the models and reference the Getting Started Guide for the latest list of all available platforms.

You’ll also soon be able to test multimodal Meta AI on our Ray-Ban Meta smart glasses.

As always, we look forward to seeing all the amazing products and experiences you will build with Meta Llama 3.

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