Tips for Large-Scale Employment and Training in Remote Teams

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Managing large-scale remote employment and training presents a complex challenge. It goes beyond simply recruiting qualified individuals; it involves ensuring their alignment with your organisation’s work culture and possessing the necessary management skills to collaborate effectively with your team.

When executed effectively, large-scale remote employment can yield mutually beneficial outcomes for the organisation and the candidates. However, mishandling can result in wasted time, financial losses, and resource depletion for both parties.

We have assembled a collection of valuable recommendations to facilitate a successful experience in large-scale remote recruitment and training. Keep reading to learn more!

Outsource to experts

Outsourcing staff augmentation services can offer a valuable solution when your business needs additional capacity.

To acquire and oversee extra personnel with the necessary expertise and skills, relying on professional project support services can be highly beneficial. Engaging experts in data migration services is advisable to minimise disruption to your business operations and transactions.

For remote team members, coworking spaces present a superb choice. Incorporating upskilling, social learning, and microlearning into employee training can significantly enhance their performance while boosting engagement, morale, productivity, and satisfaction.

Implementing these suggestions can optimise your remote team’s efficiency and enhance employee engagement, morale, productivity, and satisfaction.

Establish your workflow

A workforce plan is vital to large-scale employment and should be developed from the outset. It will outline a business mission, objectives, and priorities and form the foundation for successful project workflows.

Start with a clear hierarchy of roles and responsibilities for each team member. Develop processes for completing each project step, including planning, design, development, testing, and deployment.

Evaluate your team’s current processes and workflows to determine areas for improvement or additional steps that could be included. Consider using virtual tools to enhance communication and collaboration among team members.

Focus on outputs rather than processes when planning job duties and responsibilities. This will allow employees to perform their tasks as they see fit and contribute to project workflows in the way that best fits their skills and experience.

Set your expectations

When collaborating within a remote team, it is vital to establish realistic expectations. Take the time to define precise job roles and communicate expectations for each position.

Furthermore, invest in well-rounded onboarding and training initiatives to familiarise new hires with the team’s workflow and operational procedures. This process will enable them to integrate seamlessly into the team.

To ensure efficient communication among remote teams, leverage technology to your advantage. Explore email, chat programs, or video conferences to facilitate effective information exchange.

Establishing a feedback loop is crucial as it allows employees to contribute their insights and continue their professional growth, which fosters a sense of connection to the organisation and empowers individuals to embrace additional responsibilities.

Make it communal

Create community by assigning team roles and setting up virtual meetings.
Utilise online collaboration tools to facilitate training sessions.
Provide incentives for the successful completion of training programs.
Develop a way to reward employees for their dedication and hard work.
Ensure regular updates on training programs, deadlines, and targets.
Ensure employees have access to training materials and resources when needed.
Be flexible with scheduling training sessions to accommodate employee work and family life.

Ensure employees are well-equipped with the skills they need for the job. Also, regularly update employees on their progress and provide training tailored to their job role.

Use video

Utilise video conferencing and webinars for large-scale training sessions. They’re easy to set up and can be used for internal and external training. Leverage online tools for interactive activities such as polls, quizzes, and surveys.

Create short videos to explain complex topics in an easy-to-understand way. Video creation can be a great way to convey complicated ideas or concepts to employees quickly.

Follow up with individual employees to ensure the training was effective and successful. It’s important to show employees how the training has positively impacted the organisation and the employee’s job responsibilities.

Keep in touch with all candidates

A remote work environment allows employees to work from anywhere, making it easier to focus on their work and be more productive. It has been found to increase employee engagement, with employees reporting greater satisfaction with their work-life balance, creativity, and ability to be more flexible.

Additionally, remote work has been found to help companies save money on office space and other expenses. Not only do remote employees spend less time commuting and working in an office, but they also spend less time filing documents and spending time in the office.

As a result, companies can save on costly office infrastructure such as electricity bills and other operating costs. With globalisation and technology allowing recruiters to pull from a broader and more diverse candidate pool, remote work is increasingly viewed as a viable work option by professionals of all ages and backgrounds.

If you’re interested in exploring remote work as an option for your organisation, consider the benefits and drawbacks of this new way of working before leaping.

Equip teams with the right technology and tools

Organisations should assess the technology and tools available to facilitate seamless connectivity and effective collaboration among remote employees. This evaluation examines remote employees’ technological requirements to establish connections, such as email, chat platforms, video calls, and similar applications.

Additionally, companies should implement comprehensive training initiatives on a large scale to ensure that all members of their remote teams are proficient in utilising the technology and tools provided. These initiatives involve offering employees tutorials, workshops, and seminars to familiarise them with the functionality of the technology and tools.

Finally, employers need to guarantee that their remote teams have access to the latest technology and tools relevant to their respective roles and responsibilities. This may entail providing the necessary hardware, software, and training resources.

By undertaking these measures, organisations can equip their remote teams with the appropriate technology and tools, fostering an environment where they can work efficiently and productively.

Trust your employees

Employ online communication and collaboration platforms to foster alignment among all team members. This approach ensures remote employees stay connected and well-informed, even during demanding work periods.

Craft training programs that cater to the specific needs of remote employees. Tailor these programs to align with each employee’s unique skill set and preferred learning style. This tailored approach fosters employee motivation and engagement, making absorbing and applying new skills easier.

Ensure that all employees comprehensively understand the company’s culture and values. This understanding helps remote employees embrace fresh ideas and cultivates enthusiasm for their work. It also fosters a sense of belonging and nurtures team chemistry.

Emphasise building trust within remote teams through regular check-ins and feedback mechanisms. Create opportunities for remote workers to voice their opinions, seek clarification, and provide consistent feedback on their work. This establishes trust and accountability, resulting in improved performance and engagement.

Lastly, encourage self-paced learning opportunities that allow employees to refine their skills and expand their knowledge. This approach empowers remote workers to develop the necessary job skills without being constrained by rigid training schedules or deadlines.

Image: Pexels

Addressing challenges in managing remote teams

Establishing clear communication channels and protocols is crucial for effectively managing remote teams. You can track progress and deadlines using project management tools, ensuring everyone is on the same page. Regular feedback and recognition of remote team members help maintain engagement and motivation.

Virtual team-building activities can be organised to foster a sense of community and teamwork. Offering training and professional development opportunities to remote employees enables them to acquire new skills and stay productive. Setting realistic expectations and goals for remote teams is important, considering their unique challenges. By addressing these challenges head-on, organisations can effectively manage their remote workforce and ensure success in the long term.

Overcoming isolation and building team cohesion

To overcome isolation and build team cohesion in remote teams, fostering regular communication and collaboration among team members is crucial. This can be achieved by utilising various digital communication channels like email, chat, and video conferencing. Encouraging open and transparent communication makes everyone feel heard and included in discussions and decision-making processes.

Additionally, virtual team-building activities and social events can be organised to strengthen bonds and create a sense of community. It is also important to provide opportunities for team members to share their personal interests and hobbies, creating connections beyond work tasks. Regular check-ins and feedback sessions should be implemented to address any challenges and ensure team cohesion is maintained. By implementing these strategies, remote teams can overcome isolation and build strong team cohesion.

Ensuring accountability and performance in remote settings

To ensure accountability and high performance in remote settings, it is crucial to implement clear performance metrics and expectations for remote team members. Regularly tracking progress and providing feedback helps maintain accountability and ensures everyone meets their goals. Utilising project management tools and software allows for monitoring tasks, deadlines, and overall team productivity.

Additionally, fostering open communication channels is essential to address any challenges or roadblocks that may hinder performance. Supporting remote team members with resources, training, and development opportunities can enhance their skills and motivation, leading to improved performance. Remote work brings unique challenges, but with the right systems and support in place, teams can thrive and deliver excellent results.

The future of remote work and its impact on employment

The rise of remote work has significantly impacted job opportunities, allowing companies to tap into a larger talent pool without geographical limitations. Effective management and training are crucial for remote teams to thrive. Strategies such as clear communication through email, chat, video conferencing, and project management tools help bridge the distance.

Creating a positive remote work culture fosters collaboration and maintains employee engagement. While remote work offers benefits like flexibility and work-life balance, it also presents challenges such as potential feelings of isolation and the need for strong collaboration tools.

Technology facilitates remote training and development, providing new skills and industry expertise opportunities. As companies adapt to the future of work, remote teams will continue to play a significant role, requiring human resources and team leaders to make important decisions and implement best practices.

Adapting HR practices for a remote workforce

Adapting HR practices for a remote workforce is crucial in today’s flexible work environment. With remote work becoming the norm, employers have access to a broader talent pool and the opportunity to recruit team members from different locations. To ensure effective communication, collaboration, and productivity in remote teams, it is essential to adapt HR practices accordingly. Virtual onboarding and training programs are vital in remote work environments, helping new employees integrate seamlessly into their roles.

Building a strong company culture and fostering employee engagement are vital in remote teams. Implementing remote work policies and providing the necessary tools and resources are key to successful remote employment. Organisations can create a thriving and productive remote workforce by adapting HR practices to the unique needs of remote workers.

The role of leadership in remote team management

Effective leadership is pivotal in managing remote teams, ensuring clear communication, and aligning goals. Remote leaders must possess strong interpersonal skills to build trust and motivate team members from a distance. Regular check-ins, feedback, and recognition are paramount to maintaining engagement and productivity. Setting clear expectations and providing necessary resources and support are essential for the success of remote teams.

Additionally, remote leaders should foster a positive team culture, promoting collaboration and creating opportunities for professional development. By incorporating these leadership strategies, remote teams can thrive and overcome challenges posed by the pandemic and remote working.

Leaders must adapt their management style to meet the unique needs of remote employees, leveraging digital tools such as video conferencing and collaboration tools. Leadership in remote team management has become increasingly important in the past year as organisations navigate the new normal of remote work.

In Summary

Undoubtedly, employee training can be demanding, and the challenges amplify within remote teams scattered across various locations.

Operating in geographically dispersed areas and different time zones can give rise to communication gaps and hinder productivity.

Thus, it becomes crucial to engage remote employees actively. Regular communication and ample support emerge as highly effective strategies to foster satisfaction and productivity among remote team members.

About the Author

Tom Siani is an online marketing expert with more than 7 years of experience in this digital industry. He is collaborating with some well-known brands to generate traffic, create sales funnels, and increase online sales.

He has also written many articles about social media marketing, brand marketing, blogging, search visibility, etc.

The post Tips for Large-Scale Employment and Training in Remote Teams appeared first on The 6Q Blog.

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Distilling step-by-step: Outperforming larger language models with less training data and smaller model sizes

Posted by Cheng-Yu Hsieh, Student Researcher, and Chen-Yu Lee, Research Scientist, Cloud AI Team

Large language models (LLMs) have enabled a new data-efficient learning paradigm wherein they can be used to solve unseen new tasks via zero-shot or few-shot prompting. However, LLMs are challenging to deploy for real-world applications due to their sheer size. For instance, serving a single 175 billion LLM requires at least 350GB of GPU memory using specialized infrastructure, not to mention that today’s state-of-the-art LLMs are composed of over 500 billion parameters. Such computational requirements are inaccessible for many research teams, especially for applications that require low latency performance.

To circumvent these deployment challenges, practitioners often choose to deploy smaller specialized models instead. These smaller models are trained using one of two common paradigms: fine-tuning or distillation. Fine-tuning updates a pre-trained smaller model (e.g., BERT or T5) using downstream manually-annotated data. Distillation trains the same smaller models with labels generated by a larger LLM. Unfortunately, to achieve comparable performance to LLMs, fine-tuning methods require human-generated labels, which are expensive and tedious to obtain, while distillation requires large amounts of unlabeled data, which can also be hard to collect.

In “Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes”, presented at ACL2023, we set out to tackle this trade-off between model size and training data collection cost. We introduce distilling step-by-step, a new simple mechanism that allows us to train smaller task-specific models with much less training data than required by standard fine-tuning or distillation approaches that outperform few-shot prompted LLMs’ performance. We demonstrate that the distilling step-by-step mechanism enables a 770M parameter T5 model to outperform the few-shot prompted 540B PaLM model using only 80% of examples in a benchmark dataset, which demonstrates a more than 700x model size reduction with much less training data required by standard approaches.

While LLMs offer strong zero and few-shot performance, they are challenging to serve in practice. On the other hand, traditional ways of training small task-specific models require a large amount of training data. Distilling step-by-step provides a new paradigm that reduces both the deployed model size as well as the number of data required for training.

Distilling step-by-step

The key idea of distilling step-by-step is to extract informative natural language rationales (i.e., intermediate reasoning steps) from LLMs, which can in turn be used to train small models in a more data-efficient way. Specifically, natural language rationales explain the connections between the input questions and their corresponding outputs. For example, when asked, “Jesse’s room is 11 feet long and 15 feet wide. If she already has 16 square feet of carpet, how much more carpet does she need to cover the whole floor?”, an LLM can be prompted by the few-shot chain-of-thought (CoT) prompting technique to provide intermediate rationales, such as, “Area = length * width. Jesse’s room has 11 * 15 square feet.” That better explains the connection from the input to the final answer, “(11 * 15 ) – 16”. These rationales can contain relevant task knowledge, such as “Area = length * width”, that may originally require many data for small models to learn. We utilize these extracted rationales as additional, richer supervision to train small models, in addition to the standard task labels.

Overview on distilling step-by-step: First, we utilize CoT prompting to extract rationales from an LLM. We then use the generated rationales to train small task-specific models within a multi-task learning framework, where we prepend task prefixes to the input examples and train the model to output differently based on the given task prefix.

Distilling step-by-step consists of two main stages. In the first stage, we leverage few-shot CoT prompting to extract rationales from LLMs. Specifically, given a task, we prepare few-shot exemplars in the LLM input prompt where each example is composed of a triplet containing: (1) input, (2) rationale, and (3) output. Given the prompt, an LLM is able to mimic the triplet demonstration to generate the rationale for any new input. For instance, in a commonsense question answering task, given the input question “Sammy wanted to go to where the people are. Where might he go? Answer Choices: (a) populated areas, (b) race track, (c) desert, (d) apartment, (e) roadblock”, distilling step-by-step provides the correct answer to the question, “(a) populated areas”, paired with the rationale that provides better connection from the question to the answer, “The answer must be a place with a lot of people. Of the above choices, only populated areas have a lot of people.” By providing CoT examples paired with rationales in the prompt, the in-context learning ability allows LLMs to output corresponding rationales for future unseen inputs.

We use the few-shot CoT prompting, which contains both an example rationale (highlighted in green) and a label (highlighted in blue), to elicit rationales from an LLM on new input examples. The example is from a commonsense question answering task.

After the rationales are extracted, in the second stage, we incorporate the rationales in training small models by framing the training process as a multi-task problem. Specifically, we train the small model with a novel rationale generation task in addition to the standard label prediction task. The rationale generation task enables the model to learn to generate the intermediate reasoning steps for the prediction, and guides the model to better predict the resultant label. We prepend task prefixes (i.e., [label] and [rationale] for label prediction and rationale generation, respectively) to the input examples for the model to differentiate the two tasks.

Experimental setup

In the experiments, we consider a 540B PaLM model as the LLM. For task-specific downstream models, we use T5 models. For CoT prompting, we use the original CoT prompts when available and curate our own examples for new datasets. We conduct the experiments on four benchmark datasets across three different NLP tasks: e-SNLI and ANLI for natural language inference; CQA for commonsense question answering; and SVAMP for arithmetic math word problems. We include two sets of baseline methods. For comparison to few-shot prompted LLMs, we compare to few-shot CoT prompting with a 540B PaLM model. In the paper, we also compare standard task-specific model training to both standard fine-tuning and standard distillation. In this blogpost, we will focus on the comparisons to standard fine-tuning for illustration purposes.

Less training data

Compared to standard fine-tuning, the distilling step-by-step method achieves better performance using much less training data. For instance, on the e-SNLI dataset, we achieve better performance than standard fine-tuning when using only 12.5% of the full dataset (shown in the upper left quadrant below). Similarly, we achieve a dataset size reduction of 75%, 25% and 20% on ANLI, CQA, and SVAMP.

Distilling step-by-step compared to standard fine-tuning using 220M T5 models on varying sizes of human-labeled datasets. On all datasets, distilling step-by-step is able to outperform standard fine-tuning, trained on the full dataset, by using much less training examples.

Smaller deployed model size

Compared to few-shot CoT prompted LLMs, distilling step-by-step achieves better performance using much smaller model sizes. For instance, on the e-SNLI dataset, we achieve better performance than 540B PaLM by using a 220M T5 model. On ANLI, we achieve better performance than 540B PaLM by using a 770M T5 model, which is over 700X smaller. Note that on ANLI, the same 770M T5 model struggles to match PaLM’s performance using standard fine-tuning.

We perform distilling step-by-step and standard fine-tuning on varying sizes of T5 models and compare their performance to LLM baselines, i.e., Few-shot CoT and PINTO Tuning. Distilling step-by-step is able to outperform LLM baselines by using much smaller models, e.g., over 700× smaller models on ANLI. Standard fine-tuning fails to match LLM’s performance using the same model size.

Distilling step-by-step outperforms few-shot LLMs with smaller models using less data

Finally, we explore the smallest model sizes and the least amount of data for distilling step-by-step to outperform PaLM’s few-shot performance. For instance, on ANLI, we surpass the performance of the 540B PaLM using a 770M T5 model. This smaller model only uses 80% of the full dataset. Meanwhile, we observe that standard fine-tuning cannot catch up with PaLM’s performance even using 100% of the full dataset. This suggests that distilling step-by-step simultaneously reduces the model size as well as the amount of data required to outperform LLMs.

We show the minimum size of T5 models and the least amount of human-labeled examples required for distilling step-by-step to outperform LLM’s few-shot CoT by a coarse-grained search. Distilling step-by-step is able to outperform few-shot CoT using not only much smaller models, but it also achieves so with much less training examples compared to standard fine-tuning.

Conclusion

We propose distilling step-by-step, a novel mechanism that extracts rationales from LLMs as informative supervision in training small, task-specific models. We show that distilling step-by-step reduces both the training dataset required to curate task-specific smaller models and the model size required to achieve, and even surpass, a few-shot prompted LLM’s performance. Overall, distilling step-by-step presents a resource-efficient paradigm that tackles the trade-off between model size and training data required.

Availability on Google Cloud Platform

Distilling step-by-step is available for private preview on Vertex AI. If you are interested in trying it out, please contact vertex-llm-tuning-preview@google.com with your Google Cloud Project number and a summary of your use case.

Acknowledgements

This research was conducted by Cheng-Yu Hsieh, Chun-Liang Li, Chih-Kuan Yeh, Hootan Nakhost, Yasuhisa Fujii, Alexander Ratner, Ranjay Krishna, Chen-Yu Lee, and Tomas Pfister. Thanks to Xiang Zhang and Sergey Ioffe for their valuable feedback.