(14 minutes per pair of candidates; 20 minutes per group of three)
The B2 First Reading and Use of English paper is in seven parts and has a mix of text types and questions.
For Parts 1 to 4 , you read a range of texts and do grammar and vocabulary tasks.
For Parts 5 to 7 , you read a series of texts and answer questions that test your reading ability and show that you can deal with a variety of different types of texts.
Time allowed: | 1 hour 15 minutes |
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Number of parts: | 7 |
Number of questions: | 52 |
Marks: | 40% of total |
Length of texts: | About 2,200 words to read in total. |
Texts may be from: | Newspapers and magazines, journals, books (fiction and non-fiction), promotional and informational material. |
Part 1 (Multiple-choice cloze)
What's in Part 1? | A text with some multiple-choice questions. Each question has four options (A, B, C or D) – you have to decide which is the correct answer. |
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What do I have to practise? | Vocabulary – idioms, collocations, shades of meaning, phrasal verbs, fixed phrases etc. |
How many questions are there? | 8 |
How many marks are there? | 1 mark for each correct answer. |
Part 2 (Open cloze)
What's in Part 2? | A text in which there are some gaps, each of which represents one missing word. You have to think of the correct word for each gap. |
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What do I have to practise? | Grammar and vocabulary. |
How many questions are there? | 8 |
How many marks are there? | 1 mark for each correct answer. |
Part 3 (Word formation)
What's in Part 3? | A text containing eight gaps. Each gap represents a word. At the end of the line is a ‘prompt’ word which you have to change in some way to complete the sentence correctly. |
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What do I have to practise? | Vocabulary. |
How many questions are there? | 8 |
How many marks are there? | 1 mark for each correct answer. |
Part 4 (Key word transformations)
What's in Part 4? | Each question consists of a sentence followed by a ‘key’ word and a second sentence with a gap in the middle. You have to use this key word to complete the second sentence so that it has a similar meaning to the first sentence. |
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What do I have to practise? | Grammar and vocabulary. |
How many questions are there? | 6 |
How many marks are there? | Up to 2 marks for each correct answer. |
Part 5 (Multiple choice)
What's in Part 5? | A text with some multiple-choice questions. For each question, there are four options and you have to choose A, B, C or D. |
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What do I have to practise? | Reading for detail, opinion, tone, purpose, main idea, implication, attitude. |
How many questions are there? | 6 |
How many marks are there? | 2 marks for each correct answer. |
Part 6 (Gapped text)
What's in Part 6? | A single page of text with some numbered gaps which represent missing sentences. After the text there are some sentences which are not in the right order. You have to read the text and the sentences and decide which sentence best fits each gap. |
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What do I have to practise? | How to understand the structure and development of a text. |
How many questions are there? | 6 |
How many marks are there? | 2 marks for each correct answer. |
Part 7 (Multiple matching)
What's in Part 7? | A series of statements followed by a text divided into sections or several short texts. You have to match each statement to the section or text in which you can find the information. |
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What do I have to practise? | Reading for specific information, detail, opinion and attitude. |
How many questions are there? | 10 |
How many marks are there? | 1 mark for each correct answer. |
In the two parts of the B2 First Writing paper, you have to show that you can write different types of text in English.
Time allowed: | 1 hour 20 minutes |
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Number of parts: | 2 |
Number of questions: | Part 1: one compulsory question, Part 2: one question from a choice of three |
Types of task: | Articles, email, essay, letter, report, review. |
Part 1 (Compulsory question)
What's in Part 1? | You’re given an essay title and two ideas clearly linked to the title. You write an essay giving your opinions about the title, using the ideas given. You must also add a third, different idea of your own linked to the title. The title will be a subject of general interest – you won’t need any specialised knowledge. |
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What do I have to practise? | Using language functions, such as evaluating, expressing opinions, hypothesising, justifying, persuading. |
How many questions are there? | One compulsory question. |
How much do I have to write? | 140–190 words |
Part 2 (Situationally based writing task)
What's in Part 2? | You write a text from a choice of text types – article, email/letter, report or review. To guide your writing, you’ll be given information about context, topic purpose and target reader. |
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What do I have to practise? | Writing different types of text that could be included in the exam. |
How many questions are there? | One task to be selected from a choice of three. |
How much do I have to write? | 140–190 words |
The B2 First Listening paper has four parts. For each part you have to listen to a recorded text or texts and answer some questions. You hear each recording twice.
Time allowed: | About 40 minutes |
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Number of parts: | 4 |
Number of questions: | 30 |
Marks: | 20% total |
Recordings may be from: | Monologues: answer phone messages, radio broadcasts and features, news, public announcements, stories and anecdotes, lectures and talks; or interacting speakers: conversations, interviews, discussions, radio plays. |
Part 1 (Multiple choice)
What's in Part 1? | Eight short extracts from monologues or conversations between interacting speakers. There is one multiple-choice question for each extract, and you have to choose A, B or C. |
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What do I have to practise? | Listening for feeling, attitude, opinion, purpose, function, agreement, gist and detail. |
How many questions are there? | 8 |
How many marks are there? | 1 mark for each correct answer. |
Part 2 (Sentence completion)
What's in Part 2? | A monologue (which may be introduced by a presenter) lasting approximately 3 minutes. You have to complete the sentences on the question paper with the missing information which you hear on the recording. |
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What do I have to practise? | Listening for specific information, stated opinion. |
How many questions are there? | 10 |
How many marks are there? | 1 mark for each correct answer. |
Part 3 (Multiple matching)
What's in Part 3? | A series of five themed monologues of approximately 30 seconds each. On the question paper, you have to select five correct options from a list of eight possible answers. |
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What do I have to practise? | Listening for gist, attitude, opinion, purpose, feeling, main points and detail. |
How many questions are there? | 5 |
How many marks are there? | 1 mark for each correct answer. |
Part 4 (Multiple choice)
What's in Part 4? | A conversation between two or more speakers of approximately 3–4 minutes. You have to answer some multiple-choice questions by choosing the correct answer from three options (A, B or C). |
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What do I have to practise? | Listening for attitude, opinion, detail, gist, main idea and specific information. |
How many questions are there? | 7 |
How many marks are there? | 1 mark for each correct answer. |
The B2 First Speaking test has four parts and you take it together with another candidate.
There are two examiners. One of the examiners asks you questions and gives you the booklet with things to talk about. The other examiner listens to what you say.
Time allowed: | 14 minutes per pair of candidates |
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Number of parts: | 4 |
Marks: | 20% total |
You have to talk: | with the examiner with the other candidate on your own |
Part 1 (Interview)
What's in Part 1? | Conversation with the examiner. The examiner asks questions and you may have to give information about your interests, studies, career, etc. |
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What do I have to practise? | Giving information about yourself and expressing your opinion about various topics. |
How long do I have to speak? | 2 minutes |
Part 2 (Long turn)
What's in Part 2? | The examiner gives you two photographs and asks you to talk about them. You have to speak for 1 minute without interruption and the interlocutor then asks the other candidate to comment on your photographs for about 30 seconds. The other candidate receives a different set of photographs and you have to listen and comment when they have finished speaking. The question you have to answer about your photographs is written at the top of the page to remind you what you should talk about. |
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What do I have to practise? | Talking on your own about something: comparing, describing, expressing opinions, speculating. |
How long do I have to speak? | 1 minute per candidate |
Part 3 (Collaborative task)
What's in Part 3? | Conversation with the other candidate. The examiner gives you some material and a task to do. You have to talk with the other candidate and make a decision. |
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What do I have to practise? | Exchanging ideas, expressing and justifying opinions, agreeing and/or disagreeing, suggesting, speculating, evaluating, reaching a decision through negotiation, etc. |
How long do we have to speak? | 3 minutes (a 2-minute discussion followed by a 1-minute decision-making task) |
Part 4 (Discussion)
What's in Part 4? | Further discussion with the other candidate, guided by questions from the examiner, about the topics or issues raised in the task in Part 3. |
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What do I have to practise? | Expressing and justifying opinions, agreeing and/or disagreeing. |
How long do we have to speak? | 4 minutes |
Communications Chairs 2023 2023 Conference awards , neurips2023
By Amir Globerson, Kate Saenko, Moritz Hardt, Sergey Levine and Comms Chair, Sahra Ghalebikesabi
We are honored to announce the award-winning papers for NeurIPS 2023! This year’s prestigious awards consist of the Test of Time Award plus two Outstanding Paper Awards in each of these three categories:
This year’s organizers received a record number of paper submissions. Of the 13,300 submitted papers that were reviewed by 968 Area Chairs, 98 senior area chairs, and 396 Ethics reviewers 3,540 were accepted after 502 papers were flagged for ethics reviews .
We thank the awards committee for the main track: Yoav Artzi, Chelsea Finn, Ludwig Schmidt, Ricardo Silva, Isabel Valera, and Mengdi Wang. For the Datasets and Benchmarks track, we thank Sergio Escalera, Isabelle Guyon, Neil Lawrence, Dina Machuve, Olga Russakovsky, Hugo Jair Escalante, Deepti Ghadiyaram, and Serena Yeung. Conflicts of interest were taken into account in the decision process.
Congratulations to all the authors! See Posters Sessions Tue-Thur in Great Hall & B1-B2 (level 1).
Privacy Auditing with One (1) Training Run Authors: Thomas Steinke · Milad Nasr · Matthew Jagielski
Poster session 2: Tue 12 Dec 5:15 p.m. — 7:15 p.m. CST, #1523
Oral: Tue 12 Dec 3:40 p.m. — 4:40 p.m. CST, Room R06-R09 (level 2)
Abstract: We propose a scheme for auditing differentially private machine learning systems with a single training run. This exploits the parallelism of being able to add or remove multiple training examples independently. We analyze this using the connection between differential privacy and statistical generalization, which avoids the cost of group privacy. Our auditing scheme requires minimal assumptions about the algorithm and can be applied in the black-box or white-box setting. We demonstrate the effectiveness of our framework by applying it to DP-SGD, where we can achieve meaningful empirical privacy lower bounds by training only one model. In contrast, standard methods would require training hundreds of models.
Are Emergent Abilities of Large Language Models a Mirage? Authors: Rylan Schaeffer · Brando Miranda · Sanmi Koyejo
Poster session 6: Thu 14 Dec 5:00 p.m. — 7:00 p.m. CST, #1108
Oral: Thu 14 Dec 3:20 p.m. — 3:35 p.m. CST, Hall C2 (level 1)
Abstract: Recent work claims that large language models display emergent abilities, abilities not present in smaller-scale models that are present in larger-scale models. What makes emergent abilities intriguing is two-fold: their sharpness, transitioning seemingly instantaneously from not present to present, and their unpredictability , appearing at seemingly unforeseeable model scales. Here, we present an alternative explanation for emergent abilities: that for a particular task and model family, when analyzing fixed model outputs, emergent abilities appear due to the researcher’s choice of metric rather than due to fundamental changes in model behavior with scale. Specifically, nonlinear or discontinuous metrics produce apparent emergent abilities, whereas linear or continuous metrics produce smooth, continuous, predictable changes in model performance. We present our alternative explanation in a simple mathematical model, then test it in three complementary ways: we (1) make, test and confirm three predictions on the effect of metric choice using the InstructGPT/GPT-3 family on tasks with claimed emergent abilities, (2) make, test and confirm two predictions about metric choices in a meta-analysis of emergent abilities on BIG-Bench; and (3) show how to choose metrics to produce never-before-seen seemingly emergent abilities in multiple vision tasks across diverse deep networks. Via all three analyses, we provide evidence that alleged emergent abilities evaporate with different metrics or with better statistics, and may not be a fundamental property of scaling AI models.
Scaling Data-Constrained Language Models Authors : Niklas Muennighoff · Alexander Rush · Boaz Barak · Teven Le Scao · Nouamane Tazi · Aleksandra Piktus · Sampo Pyysalo · Thomas Wolf · Colin Raffel
Poster session 2: Tue 12 Dec 5:15 p.m. — 7:15 p.m. CST, #813
Oral: Tue 12 Dec 3:40 p.m. — 4:40 p.m. CST, Hall C2 (level 1)
Abstract : The current trend of scaling language models involves increasing both parameter count and training dataset size. Extrapolating this trend suggests that training dataset size may soon be limited by the amount of text data available on the internet. Motivated by this limit, we investigate scaling language models in data-constrained regimes. Specifically, we run a large set of experiments varying the extent of data repetition and compute budget, ranging up to 900 billion training tokens and 9 billion parameter models. We find that with constrained data for a fixed compute budget, training with up to 4 epochs of repeated data yields negligible changes to loss compared to having unique data. However, with more repetition, the value of adding compute eventually decays to zero. We propose and empirically validate a scaling law for compute optimality that accounts for the decreasing value of repeated tokens and excess parameters. Finally, we experiment with approaches mitigating data scarcity, including augmenting the training dataset with code data or removing commonly used filters. Models and datasets from our 400 training runs are freely available at https://github.com/huggingface/datablations .
Direct Preference Optimization: Your Language Model is Secretly a Reward Model Authors: Rafael Rafailov · Archit Sharma · Eric Mitchell · Christopher D Manning · Stefano Ermon · Chelsea Finn
Poster session 6: Thu 14 Dec 5:00 p.m. — 7:00 p.m. CST, #625
Oral: Thu 14 Dec 3:50 p.m. — 4:05 p.m. CST, Ballroom A-C (level 2)
Abstract: While large-scale unsupervised language models (LMs) learn broad world knowledge and some reasoning skills, achieving precise control of their behavior is difficult due to the completely unsupervised nature of their training. Existing methods for gaining such steerability collect human labels of the relative quality of model generations and fine-tune the unsupervised LM to align with these preferences, often with reinforcement learning from human feedback (RLHF). However, RLHF is a complex and often unstable procedure, first fitting a reward model that reflects the human preferences, and then fine-tuning the large unsupervised LM using reinforcement learning to maximize this estimated reward without drifting too far from the original model. In this paper, we leverage a mapping between reward functions and optimal policies to show that this constrained reward maximization problem can be optimized exactly with a single stage of policy training, essentially solving a classification problem on the human preference data. The resulting algorithm, which we call Direct Preference Optimization (DPO), is stable, performant, and computationally lightweight, eliminating the need for fitting a reward model, sampling from the LM during fine-tuning, or performing significant hyperparameter tuning. Our experiments show that DPO can fine-tune LMs to align with human preferences as well as or better than existing methods. Notably, fine-tuning with DPO exceeds RLHF’s ability to control sentiment of generations and improves response quality in summarization and single-turn dialogue while being substantially simpler to implement and train.
In the dataset category :
ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation
Authors: Sungduk Yu · Walter Hannah · Liran Peng · Jerry Lin · Mohamed Aziz Bhouri · Ritwik Gupta · Björn Lütjens · Justus C. Will · Gunnar Behrens · Julius Busecke · Nora Loose · Charles Stern · Tom Beucler · Bryce Harrop · Benjamin Hillman · Andrea Jenney · Savannah L. Ferretti · Nana Liu · Animashree Anandkumar · Noah Brenowitz · Veronika Eyring · Nicholas Geneva · Pierre Gentine · Stephan Mandt · Jaideep Pathak · Akshay Subramaniam · Carl Vondrick · Rose Yu · Laure Zanna · Tian Zheng · Ryan Abernathey · Fiaz Ahmed · David Bader · Pierre Baldi · Elizabeth Barnes · Christopher Bretherton · Peter Caldwell · Wayne Chuang · Yilun Han · YU HUANG · Fernando Iglesias-Suarez · Sanket Jantre · Karthik Kashinath · Marat Khairoutdinov · Thorsten Kurth · Nicholas Lutsko · Po-Lun Ma · Griffin Mooers · J. David Neelin · David Randall · Sara Shamekh · Mark Taylor · Nathan Urban · Janni Yuval · Guang Zhang · Mike Pritchard
Poster session 4: Wed 13 Dec 5:00 p.m. — 7:00 p.m. CST, #105
Oral: Wed 13 Dec 3:45 p.m. — 4:00 p.m. CST, Ballroom A-C (level 2)
Abstract: Modern climate projections lack adequate spatial and temporal resolution due to computational constraints. A consequence is inaccurate and imprecise predictions of critical processes such as storms. Hybrid methods that combine physics with machine learning (ML) have introduced a new generation of higher fidelity climate simulators that can sidestep Moore’s Law by outsourcing compute-hungry, short, high-resolution simulations to ML emulators. However, this hybrid ML-physics simulation approach requires domain-specific treatment and has been inaccessible to ML experts because of lack of training data and relevant, easy-to-use workflows. We present ClimSim, the largest-ever dataset designed for hybrid ML-physics research. It comprises multi-scale climate simulations, developed by a consortium of climate scientists and ML researchers. It consists of 5.7 billion pairs of multivariate input and output vectors that isolate the influence of locally-nested, high-resolution, high-fidelity physics on a host climate simulator’s macro-scale physical state. The dataset is global in coverage, spans multiple years at high sampling frequency, and is designed such that resulting emulators are compatible with downstream coupling into operational climate simulators. We implement a range of deterministic and stochastic regression baselines to highlight the ML challenges and their scoring. The data (https://huggingface.co/datasets/LEAP/ClimSim_high-res) and code (https://leap-stc.github.io/ClimSim) are released openly to support the development of hybrid ML-physics and high-fidelity climate simulations for the benefit of science and society.
In the benchmark category :
DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models
Authors: Boxin Wang · Weixin Chen · Hengzhi Pei · Chulin Xie · Mintong Kang · Chenhui Zhang · Chejian Xu · Zidi Xiong · Ritik Dutta · Rylan Schaeffer · Sang Truong · Simran Arora · Mantas Mazeika · Dan Hendrycks · Zinan Lin · Yu Cheng · Sanmi Koyejo · Dawn Song · Bo Li
Poster session 1: Tue 12 Dec 10:45 a.m. — 12:45 p.m. CST, #1618
Oral: Tue 12 Dec 10:30 a.m. — 10:45 a.m. CST, Ballroom A-C (Level 2)
Abstract: Generative Pre-trained Transformer (GPT) models have exhibited exciting progress in capabilities, capturing the interest of practitioners and the public alike. Yet, while the literature on the trustworthiness of GPT models remains limited, practitioners have proposed employing capable GPT models for sensitive applications to healthcare and finance – where mistakes can be costly. To this end, this work proposes a comprehensive trustworthiness evaluation for large language models with a focus on GPT-4 and GPT-3.5, considering diverse perspectives – including toxicity, stereotype bias, adversarial robustness, out-of-distribution robustness, robustness on adversarial demonstrations, privacy, machine ethics, and fairness. Based on our evaluations, we discover previously unpublished vulnerabilities to trustworthiness threats. For instance, we find that GPT models can be easily misled to generate toxic and biased outputs and leak private information in both training data and conversation history. We also find that although GPT-4 is usually more trustworthy than GPT-3.5 on standard benchmarks, GPT-4 is more vulnerable given jailbreaking system or user prompts, potentially due to the reason that GPT-4 follows the (misleading) instructions more precisely. Our work illustrates a comprehensive trustworthiness evaluation of GPT models and sheds light on the trustworthiness gaps. Our benchmark is publicly available at https://decodingtrust.github.io/.
This year, following the usual practice, we chose a NeurIPS paper from 10 years ago to receive the Test of Time Award, and “ Distributed Representations of Words and Phrases and their Compositionality ” by Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean, won.
Published at NeurIPS 2013 and cited over 40,000 times, the work introduced the seminal word embedding technique word2vec. Demonstrating the power of learning from large amounts of unstructured text, the work catalyzed progress that marked the beginning of a new era in natural language processing.
Greg Corrado and Jeffrey Dean will be giving a talk about this work and related research on Tuesday, 12 Dec at 3:05 – 3:25 pm CST in Hall F.
2023 Conference
Reflections on the neurips 2023 ethics review process, neurips newsletter – november 2023.
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Guest Essay
By Magdalene J. Taylor
Ms. Taylor is a writer covering sex and culture.
“The golden age of dating apps is over,” a friend told me at a bar on Super Bowl Sunday. As we waited for our drinks, she and another friend swiped through Bumble and Hinge, hunting for new faces and likes. Across the bar were two young men: phones out, apps open, clearly doing the exact same thing. Never did the duos meet.
What’s lamentable here isn’t only that dating apps have become the de facto medium through which single people meet. Since 2019, three in 10 U.S. adults have reported using them, with that figure rising to roughly six in 10 for Americans under 50 who have never been married. Not only are people not meeting partners in bars or any of the once normal in-person venues — they’re barely meeting them on the apps, either.
Maybe most of us just aren’t as hot as we used to be. Maybe it’s time our inflated egos got knocked down a notch. Maybe the market of people still willing to put themselves out there in an attempt to date has gotten smaller. Or maybe the apps have functionally, intentionally gotten worse, as have our romantic prospects. The more they fail to help us form relationships, the more we’re forced to keep swiping — and paying.
The internet, where so many of us spend so much of our time, has not been spared from the decline in quality that seems to plague so much of consumer life. This phenomenon was described by the writer Cory Doctorow in a November 2022 blog post and is sometimes called “platform decay”: Tech platforms like Amazon, Reddit and X have declined in quality as they’ve expanded. These sites initially hooked consumers by being almost too good to be true, attempting to become essential one-stop shops within their respective spaces while often charging nothing, thanks to low interest rates and free-flowing venture capital funding . Now that we’re all locked in and that capital has dried up, those initial hooks have been walked back — and there’s nowhere else to go.
This is precisely what is happening with dating apps now, too, with much more urgent consequences. What’s worsening isn’t just the technological experience of online dating but also our ability to form meaningful, lasting connections offline.
The collapse of dating apps’ usability can be blamed on the paid subscription model and the near-monopoly these apps have over the dating world. While dozens of sites exist, most 20-something daters use the big three: Tinder, Hinge and Bumble. (Older people often gravitate toward Match.com or eHarmony.) All three sites offer a “premium” version users must pay for — according to a study conducted by Morgan Stanley , around a quarter of people on dating apps use these services, averaging out at under $20 a month. The purpose, many believe, is to keep them as paid users for as long as possible. Even if we hate it, even if it’s a cycle of diminishing returns, there is no real alternative.
Opinion wants to hear your story.
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IMAGES
VIDEO
COMMENTS
FCE Essays - Sample/model answers and examiner comments. An essay is always written for the teacher. It should answer the question given by addressing both content points and providinga new content point of the writer's own. The essay should be well organised, with an introduction and an appropriate conclusion,and should be written in an appropriate register and tone
• Learn useful techniques for planning your own essay. • Evaluate two examples of a Writing Part 1 essay. • Practise and evaluate your own answer to a Writing Part 1 task. Review: Writing Part 1 . The B2 First for Schools Writing paper has two parts. Part 1 has only one task, which you . must. answer. You will: be given the essay title.
Each paragraph has a clear purpose: Introduction: it introduces the topic in a general way and it leads to the second paragraph (first idea). Paragraph 2: it deals with idea 1. Paragraph 3: it deals with idea 2. Paragraph 4: it deals with idea 3. Conclusion: we express our opinion to conclude and summarise the essay.
In your candidate answer book, you will now write your essay. Let's look at a model answer: There are many factors to consider when deciding if the university should be free or not. Among these are taxes, equality of opportunity, and the economy. ... For B2 level, the Cambridge mark scheme says, 'Occasional errors may be present but do not ...
The first part is the essay; the second part is an article, email, letter, report, or review. You will be given the essay title and two ideas or prompts. It's essential that you include both of these ideas in your essay, as well as another relevant idea that you have to come up with yourself. You have to write 140-190 words in each part and ...
These three paragraphs are called the body of the essay. However, an essay wouldn't be an essay without an introduction at the beginning and a conclusion at the end. All together that's five paragraphs and we could structure it like this: With an introduction, body and conclusion every essay has three main parts.
Test 1 / 25. Answer the question below. Write 140 - 190 words in an appropriate style. Your teacher has asked you to write an essay on the dangers of social media, and how people can protect themselves. Do you think social media can be dangerous? Write your essay using all the notes.
B2 essay structure. A Cambridge B2 First essay has a reasonably set structure. This is because the tasks are always similar. Take a look at the task below: When we analyse the task, the most obvious structure is to write 5 paragraphs. This allows us to keep a clear separation between our three points. It also gives us plenty of opportunities ...
B2, First (FCE) / By John Hayward. Writing is the part of any English exam where you should aim to get a high score and B2 First FCE Writing Part 1, an obligatory essay, is no different. It's also the most trainable part of the exam in a classroom. While other skills often take lots of time, effort and practice, writing can be taught through ...
Cambridge B2 First (FCE) - Writing. The B2 First Writing test has a duration of 1 hour 20 minutes and consists of two parts, and it accounts for 20% of the total score.. The first part has one compulsory question. In the second part, there are three questions, and you must choose one.. Candidates are required to write an essay of about 140-190 words in each part.
Part 1 of the writing test - there are 2 parts total. 140-190 word limit. You have about 40 minutes to plan and write your essay. You must answer a question using two notes and your own idea. The topic requires general knowledge only. The essay is always formal because it is written "for your teacher".
The test has two sections and takes about 80 minutes: Part 1 - write an essay based on prompts. Part 2 - write one from a choice of 3 questions: an article, an essay, a letter, a report, a review, a story. Scoring. Each of the two writing parts are marked out of 20. There are five marks for each of the following: Content, Communicative ...
An appropriate introduction; informs the reader about the essence of the essay; Firstly, (cohesive devices) clearly food is one of the principal reasons affecting people's (imprecise information) health. In their (demonstrative pronouns) daily routine should have appear a great balance diet. Although, (linking words) n owadays having a balanced ...
The essay task at Cambridge B2 First (FCE) level might be one of the first times learners encounter a truly formal writing task. At the previous level, B1 Preliminary (PET), all the writing tasks are relatively casual and informal. This is where the First Certificate writing forces candidates to prove they really know how to organise and ...
😀 Watch me explain a model B2 First Essay. The B2 First (FCE) Paper has two parts. In part one, you have a to write compulsory essay of between 140 and 190 ...
An essay is an academic paper and must be written in formal language. Formal & Informal Style (video) avoid direct and personal language (I, You, We) avoid simple words and common vocabulary use words of a higher level (remarkable, achieve, significant) use more formal expressions (With reference to , Considering the….
The B2 First handbook gives an overview of the exam and its place within Cambridge English examinations. This is followed by a focus on each paper and includes content, advice on preparation, and example papers. B2 First handbook for teachers. B2 First: Handbook for Teachers Listening Audio Files (ZIP, 72MB)
When you write an essay, you need to ensure that it has a clear structure. Paragraph 1: introduction (stating the issue) Paragraph 2: arguments for the statement. Paragraph 3: arguments against the statement. Paragraph 4: summary, your own opinion. 1. Read the Writing Strategy and the task below.
The B2 First Reading and Use of English paper is in seven parts and has a mix of text types and questions. For Parts 1 to 4, you read a range of texts and do grammar and vocabulary tasks. For Parts 5 to 7, you read a series of texts and answer questions that test your reading ability and show that you can deal with a variety of different types ...
We are honored to announce the award-winning papers for NeurIPS 2023! This year's prestigious awards consist of the Test of Time Award plus two Outstanding Paper Awards in each of these three categories: Two Outstanding Main Track Papers. Two Outstanding Main Track Runner-Ups. Two Outstanding Datasets and Benchmark Track Papers.
Yet shares (Bumble's stock price has fallen from about $75 to about $11 since its I.P.O.) and user growth have fallen, so the apps have more aggressively rolled out new premium models. In ...