essay and report writing skills pdf

Tips for Reading an Assignment Prompt

Asking analytical questions, introductions, what do introductions across the disciplines have in common, anatomy of a body paragraph, transitions, tips for organizing your essay, counterargument, conclusions.

Essay and dissertation writing skills

Planning your essay

Writing your introduction

Structuring your essay

• Writing essays in science subjects
• Brief video guides to support essay planning and writing
• Writing extended essays and dissertations
• Planning your dissertation writing time

Structuring your dissertation

• Top tips for writing longer pieces of work

Advice on planning and writing essays and dissertations

University essays differ from school essays in that they are less concerned with what you know and more concerned with how you construct an argument to answer the question. This means that the starting point for writing a strong essay is to first unpick the question and to then use this to plan your essay before you start putting pen to paper (or finger to keyboard).

A really good starting point for you are these short, downloadable Tips for Successful Essay Writing and Answering the Question resources. Both resources will help you to plan your essay, as well as giving you guidance on how to distinguish between different sorts of essay questions. 

You may find it helpful to watch this seven-minute video on six tips for essay writing which outlines how to interpret essay questions, as well as giving advice on planning and structuring your writing:

Different disciplines will have different expectations for essay structure and you should always refer to your Faculty or Department student handbook or course Canvas site for more specific guidance.

However, broadly speaking, all essays share the following features:

Essays need an introduction to establish and focus the parameters of the discussion that will follow. You may find it helpful to divide the introduction into areas to demonstrate your breadth and engagement with the essay question. You might define specific terms in the introduction to show your engagement with the essay question; for example, ‘This is a large topic which has been variously discussed by many scientists and commentators. The principle tension is between the views of X and Y who define the main issues as…’ Breadth might be demonstrated by showing the range of viewpoints from which the essay question could be considered; for example, ‘A variety of factors including economic, social and political, influence A and B. This essay will focus on the social and economic aspects, with particular emphasis on…..’

Watch this two-minute video to learn more about how to plan and structure an introduction:

The main body of the essay should elaborate on the issues raised in the introduction and develop an argument(s) that answers the question. It should consist of a number of self-contained paragraphs each of which makes a specific point and provides some form of evidence to support the argument being made. Remember that a clear argument requires that each paragraph explicitly relates back to the essay question or the developing argument.

• Conclusion: An essay should end with a conclusion that reiterates the argument in light of the evidence you have provided; you shouldn’t use the conclusion to introduce new information.
• References: You need to include references to the materials you’ve used to write your essay. These might be in the form of footnotes, in-text citations, or a bibliography at the end. Different systems exist for citing references and different disciplines will use various approaches to citation. Ask your tutor which method(s) you should be using for your essay and also consult your Department or Faculty webpages for specific guidance in your discipline. 

Essay writing in science subjects

If you are writing an essay for a science subject you may need to consider additional areas, such as how to present data or diagrams. This five-minute video gives you some advice on how to approach your reading list, planning which information to include in your answer and how to write for your scientific audience – the video is available here:

A PDF providing further guidance on writing science essays for tutorials is available to download.

Short videos to support your essay writing skills

There are many other resources at Oxford that can help support your essay writing skills and if you are short on time, the Oxford Study Skills Centre has produced a number of short (2-minute) videos covering different aspects of essay writing, including:

• Approaching different types of essay questions  
• Structuring your essay  
• Writing an introduction  
• Making use of evidence in your essay writing  
• Writing your conclusion

Extended essays and dissertations

Longer pieces of writing like extended essays and dissertations may seem like quite a challenge from your regular essay writing. The important point is to start with a plan and to focus on what the question is asking. A PDF providing further guidance on planning Humanities and Social Science dissertations is available to download.

Planning your time effectively

Try not to leave the writing until close to your deadline, instead start as soon as you have some ideas to put down onto paper. Your early drafts may never end up in the final work, but the work of committing your ideas to paper helps to formulate not only your ideas, but the method of structuring your writing to read well and conclude firmly.

Although many students and tutors will say that the introduction is often written last, it is a good idea to begin to think about what will go into it early on. For example, the first draft of your introduction should set out your argument, the information you have, and your methods, and it should give a structure to the chapters and sections you will write. Your introduction will probably change as time goes on but it will stand as a guide to your entire extended essay or dissertation and it will help you to keep focused.

The structure of  extended essays or dissertations will vary depending on the question and discipline, but may include some or all of the following:

• The background information to - and context for - your research. This often takes the form of a literature review.
• Explanation of the focus of your work.
• Explanation of the value of this work to scholarship on the topic.
• List of the aims and objectives of the work and also the issues which will not be covered because they are outside its scope.

The main body of your extended essay or dissertation will probably include your methodology, the results of research, and your argument(s) based on your findings.

The conclusion is to summarise the value your research has added to the topic, and any further lines of research you would undertake given more time or resources. 

Tips on writing longer pieces of work

Approaching each chapter of a dissertation as a shorter essay can make the task of writing a dissertation seem less overwhelming. Each chapter will have an introduction, a main body where the argument is developed and substantiated with evidence, and a conclusion to tie things together. Unlike in a regular essay, chapter conclusions may also introduce the chapter that will follow, indicating how the chapters are connected to one another and how the argument will develop through your dissertation.

For further guidance, watch this two-minute video on writing longer pieces of work . 

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Reports and essays: key differences

Know what to expect

Explore the main differences between reports and essays and how to write for your assignments

You'll complete assignments with different requirements throughout your degree, so it's important to understand what you need to do for each of them. Here we explore the key differences between reports and essays. 

This page describes general features of academic reports and essays. Depending on your subject you may use all of these features, a selection of them, or you may have additional requirements. 

There is no single right way to write a report or essay, but they are different assignments. At a glance: 

• Reports depend heavily on your subject and the type of report.
• Essays usually have specific content and a planned structure with a focus on sense and flow. You subject might need different types of information in your introduction –  some disciplines include a short background and context here, while others begin their discussion, discuss their resources or briefly signpost the topic.

Differences between reports and essays

This table compares reports and essays and provides an outline of the standard structure for each. Your assignment will also depend on your discipline, the purpose of your work, and your audience – so you should check what you need to do in your course and module handbooks, instructions from your lecturer, and your subject conventions.

Table adapted from Cottrell, 2003, p. 209.

The structure of reports

Most reports use an IMRaD structure: Introduction, Methods, Results and Discussion.

Below are some common sections that also appear in reports. Some sections include alternative headings.

1. Table of contents

Your contents shows the number of each report section, its title, page number and any sub-sections. Sub-section numbers and details start under the section title, not the margin or the number.

2. Abstract or Executive summary

This brief summary of the report is usually the last thing you write.

3. Introduction

Your introduction describes the purpose of the report, explains why it necessary or useful, and sets out its precise aims and objectives.

4. Literature review

This describes current research and thinking about the problem or research question, and is often incorporated into the introduction.

5. Methods or Methodology

This describes and justifies the methods or processes used to collect your data.

6. Results or Findings

This section presents the results (or processed data) from the research and may consist of mainly tables, charts and or diagrams.

7. Discussion, or Analysis, or Interpretation

This section analyses the results and evaluates the research carried out.

8. Conclusion

The conclusion summarises the report and usually revisits the aims and objectives.

9. Recommendations

In this section the writer uses the results and conclusions from the report to make practical suggestions about a problem or issue. This may not be required.

10. Appendices

You can include raw data or materials that your report refers to in the appendix, if you need to. The data is often presented as charts, diagrams and tables. Each item should be numbered : for example, write Table 1 and its title; Table 2 and its title, and so on as needed.

Structure of essays

Introduction.

Your essay introduction contextualises and gives background information about the topic or questions being discussed, and sets out what the essay is going to cover.

Your essay body is divided into paragraphs. These paragraphs help make a continuous, flowing text.

The conclusion summarises the main points made in the essay. Avoid introducing new information in your conclusion.

Bibliography or Reference list

This is a list of the resources you've used in your essay. This is usually presented alphabetically by authors’ surname.

Reference for the Table of Distinctions above: 

Cottrell, S. (2003).  The Study Skills Handbook  (2nd ed.). Basingstoke: Palgrave.

Download our report and essay differences revision sheet

Download this page as a PDF for your report and essay revision notes.

Key features of academic reports

Basic essay structure

Writing clear sentences

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Our approach

• Responsibility
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• Try Meta AI

RECOMMENDED READS

• 5 Steps to Getting Started with Llama 2
• The Llama Ecosystem: Past, Present, and Future
• Introducing Code Llama, a state-of-the-art large language model for coding
• Meta and Microsoft Introduce the Next Generation of Llama
• Today, we’re introducing Meta Llama 3, the next generation of our state-of-the-art open source large language model.
• Llama 3 models will soon be available on AWS, Databricks, Google Cloud, Hugging Face, Kaggle, IBM WatsonX, Microsoft Azure, NVIDIA NIM, and Snowflake, and with support from hardware platforms offered by AMD, AWS, Dell, Intel, NVIDIA, and Qualcomm.
• We’re dedicated to developing Llama 3 in a responsible way, and we’re offering various resources to help others use it responsibly as well. This includes introducing new trust and safety tools with Llama Guard 2, Code Shield, and CyberSec Eval 2.
• In the coming months, we expect to introduce new capabilities, longer context windows, additional model sizes, and enhanced performance, and we’ll share the Llama 3 research paper.
• Meta AI, built with Llama 3 technology, is now one of the world’s leading AI assistants that can boost your intelligence and lighten your load—helping you learn, get things done, create content, and connect to make the most out of every moment. You can try Meta AI here .

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.

To train our largest Llama 3 models, we combined three types of parallelization: data parallelization, model parallelization, and pipeline parallelization. Our most efficient implementation achieves a compute utilization of over 400 TFLOPS per GPU when trained on 16K GPUs simultaneously. We performed training runs on two custom-built 24K GPU clusters . To maximize GPU uptime, we developed an advanced new training stack that automates error detection, handling, and maintenance. We also greatly improved our hardware reliability and detection mechanisms for silent data corruption, and we developed new scalable storage systems that reduce overheads of checkpointing and rollback. Those improvements resulted in an overall effective training time of more than 95%. Combined, these improvements increased the efficiency of Llama 3 training by ~three times compared to Llama 2.

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.

We’ve also co-developed Llama 3 with torchtune , the new PyTorch-native library for easily authoring, fine-tuning, and experimenting with LLMs. torchtune provides memory efficient and hackable training recipes written entirely in PyTorch. The library is integrated with popular platforms such as Hugging Face, Weights & Biases, and EleutherAI and even supports Executorch for enabling efficient inference to be run on a wide variety of mobile and edge devices. For everything from prompt engineering to using Llama 3 with LangChain we have a comprehensive getting started guide and takes you from downloading Llama 3 all the way to deployment at scale within your generative AI application.

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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