Source: https://doi.org/10.6084/m9.figshare.22277494.v1
In order to improve the quality of systematic researches, various tools have been developed by well-known scientific institutes sporadically. Dr. Nader Ale Ebrahim has collected these sporadic tools under one roof in a collection named “Research Tool Box”. The toolbox contains over 720 tools so far, classified in 4 main categories: Literature-review, Writing a paper, Targeting suitable journals, as well as Enhancing visibility and impact factor.
Source: https://openai.com/research/gpt-4
A new and powerful tool: GPT-4

Ruby Chen
We’ve
created GPT-4, the latest milestone in OpenAI’s effort in scaling up
deep learning. GPT-4 is a large multimodal model (accepting image and
text inputs, emitting text outputs) that, while less capable than humans
in many real-world scenarios, exhibits human-level performance on
various professional and academic benchmarks.
We’ve created GPT-4, the latest milestone in OpenAI’s effort in scaling up deep learning. GPT-4 is a large multimodal model (accepting image and text inputs, emitting text outputs) that, while less capable than humans in many real-world scenarios, exhibits human-level performance on various professional and academic benchmarks. For example, it passes a simulated bar exam with a score around the top 10% of test takers; in contrast, GPT-3.5’s score was around the bottom 10%. We’ve spent 6 months iteratively aligning GPT-4 using lessons from our adversarial testing program as well as ChatGPT, resulting in our best-ever results (though far from perfect) on factuality, steerability, and refusing to go outside of guardrails.
Over the past two years, we rebuilt our entire deep learning stack and, together with Azure, co-designed a supercomputer from the ground up for our workload. A year ago, we trained GPT-3.5 as a first “test run” of the system. We found and fixed some bugs and improved our theoretical foundations. As a result, our GPT-4 training run was (for us at least!) unprecedentedly stable, becoming our first large model whose training performance we were able to accurately predict ahead of time. As we continue to focus on reliable scaling, we aim to hone our methodology to help us predict and prepare for future capabilities increasingly far in advance—something we view as critical for safety.
We are releasing GPT-4’s text input capability via ChatGPT and the API (with a waitlist). To prepare the image input capability for wider availability, we’re collaborating closely with a single partner to start. We’re also open-sourcing OpenAI Evals,
our framework for automated evaluation of AI model performance, to
allow anyone to report shortcomings in our models to help guide further
improvements.
In a casual conversation, the distinction between GPT-3.5 and GPT-4 can be subtle. The difference comes out when the complexity of the task reaches a sufficient threshold—GPT-4 is more reliable, creative, and able to handle much more nuanced instructions than GPT-3.5.
To understand the difference between the two models, we tested on a variety of benchmarks, including simulating exams that were originally designed for humans. We proceeded by using the most recent publicly-available tests (in the case of the Olympiads and AP free response questions) or by purchasing 2022–2023 editions of practice exams. We did no specific training for these exams. A minority of the problems in the exams were seen by the model during training, but we believe the results to be representative—see our technical report for details.
internal reference 1
| Simulated exams | GPT-4estimated percentile | GPT-4 (no vision)estimated percentile | GPT-3.5estimated percentile |
| Uniform Bar Exam (MBE+MEE+MPT)1 | 298 / 400~90th | 298 / 400~90th | 213 / 400~10th |
| LSAT | 163~88th | 161~83rd | 149~40th |
| SAT Evidence-Based Reading & Writing | 710 / 800~93rd | 710 / 800~93rd | 670 / 800~87th |
| SAT Math | 700 / 800~89th | 690 / 800~89th | 590 / 800~70th |
| Graduate Record Examination (GRE) Quantitative | 163 / 170~80th | 157 / 170~62nd | 147 / 170~25th |
| Graduate Record Examination (GRE) Verbal | 169 / 170~99th | 165 / 170~96th | 154 / 170~63rd |
| Graduate Record Examination (GRE) Writing | 4 / 6~54th | 4 / 6~54th | 4 / 6~54th |
| USABO Semifinal Exam 2020 | 87 / 15099th–100th | 87 / 15099th–100th | 43 / 15031st–33rd |
| USNCO Local Section Exam 2022 | 36 / 60 | 38 / 60 | 24 / 60 |
| Medical Knowledge Self-Assessment Program | 75% | 75% | 53% |
| Codeforces Rating | 392below 5th | 392below 5th | 260below 5th |
| AP Art History | 586th–100th | 586th–100th | 586th–100th |
| AP Biology | 585th–100th | 585th–100th | 462nd–85th |
| AP Calculus BC | 443rd–59th | 443rd–59th | 10th–7th |
We
also evaluated GPT-4 on traditional benchmarks designed for machine
learning models. GPT-4 considerably outperforms existing large language
models, alongside most state-of-the-art (SOTA) models which may include
benchmark-specific crafting or additional training protocols:
| Benchmark | GPT-4 Evaluated few-shot | GPT-3.5 Evaluated few-shot | LM SOTA Best external LM evaluated few-shot | SOTA Best external model (includes benchmark-specific training) |
Multiple-choice questions in 57 subjects (professional & academic) | 86.4% 5-shot | 70.0% 5-shot | 70.7% | 75.2% |
Commonsense reasoning around everyday events | 95.3% 10-shot | 85.5% 10-shot | 84.2% | 85.6% |
Grade-school multiple choice science questions. Challenge-set. | 96.3% 25-shot | 85.2% 25-shot | 84.2% | 85.6% |
Commonsense reasoning around pronoun resolution | 87.5% 5-shot | 81.6% 5-shot | 84.2% | 85.6% |
Python coding tasks | 67.0% 0-shot | 48.1% 0-shot | 26.2% | 65.8% |
DROP (f1 score) Reading comprehension & arithmetic. | 80.9 3-shot | 64.1 3-shot | 70.8 | 88.4 |
Many existing ML benchmarks are written in English. To get an initial sense of capability in other languages, we translated the MMLU benchmark—a suite of 14,000 multiple-choice problems spanning 57 subjects—into a variety of languages using Azure Translate (see Appendix). In the 24 of 26 languages tested, GPT-4 outperforms the English-language performance of GPT-3.5 and other LLMs (Chinchilla, PaLM), including for low-resource languages such as Latvian, Welsh, and Swahili:
We’ve
also been using GPT-4 internally, with great impact on functions like
support, sales, content moderation, and programming. We also are using
it to assist humans in evaluating AI outputs, starting the second phase
in our alignment strategy.
GPT-4 can accept a prompt of text and images, which—parallel to the text-only setting—lets the user specify any vision or language task. Specifically, it generates text outputs (natural language, code, etc.) given inputs consisting of interspersed text and images. Over a range of domains—including documents with text and photographs, diagrams, or screenshots—GPT-4 exhibits similar capabilities as it does on text-only inputs. Furthermore, it can be augmented with test-time techniques that were developed for text-only language models, including few-shot and chain-of-thought prompting. Image inputs are still a research preview and not publicly available.
Source: https://guides.library.unisa.edu.au/c.php?g=941746&p=6818357
This page contains practical ideas and tools to help build your self-confidence, identity and brand, and network successfully.
An interview with Devora Zack, author of Networking for People Who Hate Networking: A Field Guide for Introverts, the Overwhelmed, and the Underconnected (2019), that contains tips for effective networking.
Source: https://www.impactio.com/blog/tips-for-creating-your-own-brand-as-a-researcher

How can you create your own brand as a researcher? It might seem impossible, as this is often a skill that is not taught in a formal institution such as higher education. It is also the case that some researchers are not aware of how much knowledge they possess, and don’t utilize their full potential to create a real audience. They might seek to work for a big environmental or social justice organization, which can be rewarding, but if you don’t get in the door for one of these companies, then what?
In some cases, junior researchers and those looking to make a name for themselves in an industry need to take up branding, and it doesn’t have to be a painful experience. In short, having a brand others know you for is critical for staying competitive and relevant in some fields. That is the case for psychology, for example, and opening up a practice. But it takes that mindset and spark to get started.
Some niche fields are not big enough yet or well established. Some are up and coming such as environmental consultants, or alternative therapists. For example, many people know about conventional therapy but less know about homeopathic remedies or other styles that take a non-traditional approach. These new “fields”, backed by hard research, have only come into existence because the individual behind them was able to create a following for their alternative profession.
Such pioneers often have an entrepreneurial attitude and believe their ideas and angles taken will resonate with a larger audience. They are trend setters who perhaps see a larger than life idea for their research background.
In turn, such a mentality allows professionals to do a few things that they wouldn’t normally be able to do if they stuck with a more traditional career path, such as:
Online platforms and website domains can easily help professionals who want to establish themselves in an alternative field or create a branded angle. We see this most simply when people in our network change their titles on their LinkedIn profiles. It is also the case that more professional outlets, such as Impactio, can showcase publications and citation metrics and give professionals real weight in their industry.
But while Impactio or LinkedIn appeals to professional accomplishments and academic networking, branding also has an ethos component. For example, many consultants engage in some type of branding through creating their website to give users a distinct feel for what working with them will be like.
I recently spoke with a former aid worker who created their own website with a unique branding strategy. Their goal is to help current aid workers deal with re-entry shock and the stress that aid work over long contract periods can create. Browsing on the website, you could see how their new line of work was more than just the research or title they had developed over the years. The visual feeling of the site gave off an incredible tune for self-help, healing, and being in touch with oneself. They had found a gap in the industry and filled it with their own brand.
One of the best ways to bridge this gap between branding and expertise is through thought leadership and taking risks. Thought leaders often emerge in traditional fields when they challenge the status quo of their field and in turn gain a following for doing so. This following is interpreted as a reward for the thought leader, who is then able to capitalize on this process and turn their knowledge base into a real platform.
This strategy is also how some professionals get the idea for writing books. They know that gaining an audience will be beneficial in the long run.
But taking risks doesn’t always pan out so well. Often times it takes many trials and errors for those with an entrepreneurial mindset to break out of their traditional line of work and brand themselves. What is so interesting is that usually professionals with strong research, or academic background, are able to make this transition and make it last.