Revealed: Key Components of Employee Engagement

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Loyal employees are the core of every successful company. Depending on their role, they ensure the processes are running smoothly and responsibly on a daily basis. However, many employers fail to understand how they directly affect employee morale and engagement.

Top-level businesses already have pre-defined processes resulting in higher employee loyalty and workplace satisfaction. However, employee engagement is something that digital start-ups and small businesses have been struggling with in the past few years.

Lucky for both employers and employees, this is an issue that doesn’t require a huge financial investment to solve. Instead, business owners and managers need to invest their time and energy into better understanding their employees and showing them proper appreciation.

Before we go into that, it’s crucial to mention how engaged employees are more innovative, productive, and loyal. They boost customer satisfaction and help their organisations outperform competitors. Yet, the concept isn’t simple as it seems. Increasing salaries or giving more vacation days doesn’t necessarily improve employee engagement.

Instead, employee engagement consists of multiple impactful components. Throughout the article, we will help you understand the ways in which certain components matter for employees and how they impact their overall engagement.

Key elements of employee engagement

Employee engagement is more than simply being present on the job. It is a concept that encapsulates the relationship between an employee and their work environment. Engaged employees are emotionally committed to their work and the goals of their organisation. In this context, they believe the work they perform makes a real difference. And it does.

Engagement isn’t simply emotional. Employees should also be intellectually committed to the role. However, you shouldn’t expect your employees to be engaged on your own. Employee engagement is a two-way street, and if it’s not recognised adequately, you can expect a drop in morale among your workforce.

The responsibility of maximising employee engagement falls on both the individual and the organisation.

This list of key components helps you understand the most important aspects of employee engagement. However, you shouldn’t start implementing and improving all of the factors at once. Decide which of them are the most crucial for your business, and then tackle the rest of them one by one.


One of the most important aspects of any relationship between individuals is communication. The major source of stress for many employees is their inability to understand their role and their responsibilities. The reason behind this is usually higher-ups who haven’t been able to communicate this properly.

This is why it’s important to embrace open and transparent communication in the workplace. Employees shouldn’t be afraid of asking additional questions about their job, and the managers should be there to help them solve their problems.

To improve communication in the workplace, you should encourage Two-way communication, which encourages regular feedback and open dialogue. When working from home, communicating is even harder. Text messages simply can’t convey the emotions that real-life discussion can.

To facilitate communication in a remote environment, you can utilise multiple communication channels. Video meetings should be encouraged and scheduled on a regular basis, and messages need to be concise and clear of jargon.

One example of effective implementation of transparent communication is Google’s weekly “TGIF” (Thank God It’s Friday). These meetings that are scheduled every Friday help employees catch up with company news, product updates, and strategic initiatives.

Recognition and reward

Every manager can rank their subordinates according to their performance and behaviour at work. If you ask them who their best worker is, they will give you an answer in the blink of an eye. However, without a positive company culture that encourages such behaviour, they probably never give them the recognition they deserve.

Employees should feel valued for their contributions, and that is achieved mainly through recognition and rewards. Together, recognition and rewards boost employee morale and motivation.

To effectively recognise your employees, you would need to do that in a timely manner. Assuring employees that their hard work is appreciated should happen as soon as it’s done. Furthermore, it would make a much bigger impact if you disclosed their achievements publicly, at a company meeting, or using the company’s social media.

In addition, you can reward your employees with financial and non-financial benefits. Personalising these awards can have an even better impact as it will show the company cares about its employees on an individual level.

Another interesting way of increasing employee engagement through reward and recognition is by promoting peer-to-peer recognition. Starbucks and Zappos, for example, encourage their employees to recognise coworkers that are exceptional.

Career development opportunities

Throughout my career as a director, I’ve had opportunities to work with hundreds of employees. What I’ve noticed is that some of the best employees would tell me they preferred roles and companies where they have a chance to improve their skills.

Career development opportunities play a crucial role in fostering employee engagement. When employees see a clear path for advancement and growth within an organisation, they are more likely to stay engaged and committed to their work. Employees that don’t see the way in which they can advance in the company are likely to be less motivated.

These opportunities can range from providing training and development programs, mentorship, cross-functional projects, and opportunities for promotion. As an employer, these opportunities are ideal for you to see which employees are already highly-engaged with your company. Shortlisting these people for the nearest available promotion or a salary increase will not only reward them properly but also show the rest of the team that hard work doesn’t go unnoticed.

Once you’ve established ways in which your employees can improve their job-related skills, you can also proceed with creating workshops for personal or soft skills development. These workshops could focus on areas such as communication, leadership, or time management, all of which can contribute to a more harmonious, productive, and engaged workplace.

Work-life balance

In the era of remote work, work-life balance has become one of the most important aspects for employees looking for a job. Some experts are looking exclusively for jobs that offer them flexible work hours and the opportunity to work remotely.

When employees have a good work-life balance, they are to be more satisfied, less prone to burnout, and more productive at work, leading to increased engagement. Poor work-life balance will bring the opposite of the mentioned benefits.

Remote work and flexible work hours are some of the more impactful changes you can bring to your company. However, paid time-off and well-being programs can be just as effective and help employees lower their stress levels and improve their work-life balance.

The ROI of employee well-being programs like fitness initiatives and stress management workshops is immense. Happier employees mean higher productivity at no additional cost. There are also companies that offer mental health services, which also promote well-being.

However, none of these methods will work if you’re setting unrealistic expectations for your employees or if they’re working too many hours. Avoid overloading employees with work and ensure they are not consistently working overtime.

Leadership and management

Leadership and management play a crucial role in driving employee engagement. Leaders set the tone for the organisational culture and work environment, and their behaviour and attitudes can significantly influence employees’ engagement levels.

An unmotivated leader will certainly leave a negative impact on the people around him. It can not only lower employee engagement, but also push talent away from the company and incur additional expenses.

On the other hand, strong leaders inspire their teams, foster open communication, and model the behaviours they expect from their teams. A manager who preaches values that they themselves do not apply will not leave a positive impression on the workers.

Similarly, managers have a direct impact on their teams’ engagement levels. Managers who provide regular feedback and support their teams in career development can significantly boost employee engagement.

Not all leaders are made equal. There are multiple types of leaders who can equally help increase employee engagement. The Berkeley Leadership Matrix defines leader types as:

  • Transformational Leadership,
  • Servant Leadership,
  • Authentic Leadership,
  • Participative Leadership.

Some leaders are natural at inspiring others with their vision, while others enjoy putting their workers first and leading with empathy. You need to help your managers recognise their leadership style.

Organisational culture and environment

Implementing a single change or a policy as a part of your company’s process of increasing employee engagement won’t be of much help. On an organisational level, your business needs to embrace a positive culture that values employee well-being and fosters collaboration.

Every company needs to cultivate inclusivity and diversity. Having a team of international experts with a diverse set of skills will help the business move forward while giving the advantage of different perspectives and innovative ideas.

If your employees work from the office, provide them with a safe, comfortable, and well-equipped work environment. Google’s innovative and inclusive culture is characterised by a flat organisational structure. Workers have the freedom to spend time on personal projects and focus on their well-being. This type of culture encourages employees to develop themselves personally and professionally.

One of the common stressors for employees is the feeling their managers are too involved with their work. By fostering a culture of employee autonomy, you can increase employee engagement.

Meaningful work

Last but not least is helping your employees find meaning in their work.

It’s easy for an employee to start feeling detached from their colleagues when performing a role that doesn’t result in immediate revenue or client satisfaction. More often than not, these employees are both irreplaceable and directly responsible for company stability. But, that’s only visible if you can peek behind the company curtains.

It’s crucial for them to have a manager that can recognise and value their work regardless of whether their work assignments are menial or not. It’s just as important for the HR department to be aware of this potential issue and pay special attention to these brilliant individuals. Giving them regular shout-outs and helping them connect with their teams on a personal level is just as important as patting your highest-earner on their back.

In fact, the “behind-the-scenes” employee might need it more, as the high-earner already feels valued and is always in the spotlight.

In Summary

Improving the key components on the employee engagement list can be done immediately. On the other hand, some employees are simply not interested in trying out whatever methods you decide to go for.

As a manager or an executive, you should do what’s within your reach to provide as many benefits as you’re able for your employees. Top talents will always recognise these values, which will attract and retain them.

For many industry experts, a high salary isn’t as important as working in a stress-free environment where they are appreciated and have the opportunity to improve. Furthermore, the ways in which you can apply some of these strategies differ from industry to industry.

Successful application of these key components of employee engagement will make your company future-proof. Don’t limit yourself to the six components mentioned in the article, survey your employees and see whether there are components more important for them.

About the Author

Alex Popovic is a digital marketing specialist with a decade of experience spearheading teams in primarily tech and finance sectors. These days he’s focused both on SEO consultancy and Balkan Based, a recruitment organisation he co-founded to help ambitious individuals from the region find work online.

The post Revealed: Key Components of Employee Engagement appeared first on The 6Q Blog.

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Best of both worlds: Achieving scalability and quality in text clustering

Posted by Sara Ahmadian and Mehran Kazemi, Research Scientists, Google Research

Clustering is a fundamental, ubiquitous problem in data mining and unsupervised machine learning, where the goal is to group together similar items. The standard forms of clustering are metric clustering and graph clustering. In metric clustering, a given metric space defines distances between data points, which are grouped together based on their separation. In graph clustering, a given graph connects similar data points through edges, and the clustering process groups data points together based on the connections between them. Both clustering forms are particularly useful for large corpora where class labels can’t be defined. Examples of such corpora are the ever-growing digital text collections of various internet platforms, with applications including organizing and searching documents, identifying patterns in text, and recommending relevant documents to users (see more examples in the following posts: clustering related queries based on user intent and practical differentially private clustering).

The choice of text clustering method often presents a dilemma. One approach is to use embedding models, such as BERT or RoBERTa, to define a metric clustering problem. Another is to utilize cross-attention (CA) models, such as PaLM or GPT, to define a graph clustering problem. CA models can provide highly accurate similarity scores, but constructing the input graph may require a prohibitive quadratic number of inference calls to the model. On the other hand, a metric space can efficiently be defined by distances of embeddings produced by embedding models. However, these similarity distances are typically of substantial lower-quality compared to the similarity signals of CA models, and hence the produced clustering can be of much lower-quality.

An overview of the embedding-based and cross-attention–based similarity scoring functions and their scalability vs. quality dilemma.

Motivated by this, in “KwikBucks: Correlation Clustering with Cheap-Weak and Expensive-Strong Signals”, presented at ICLR 2023, we describe a novel clustering algorithm that effectively combines the scalability benefits from embedding models and the quality from CA models. This graph clustering algorithm has query access to both the CA model and the embedding model, however, we apply a budget on the number of queries made to the CA model. This algorithm uses the CA model to answer edge queries, and benefits from unlimited access to similarity scores from the embedding model. We describe how this proposed setting bridges algorithm design and practical considerations, and can be applied to other clustering problems with similar available scoring functions, such as clustering problems on images and media. We demonstrate how this algorithm yields high-quality clusters with almost a linear number of query calls to the CA model. We have also open-sourced the data used in our experiments.

The clustering algorithm

The KwikBucks algorithm is an extension of the well-known KwikCluster algorithm (Pivot algorithm). The high-level idea is to first select a set of documents (i.e., centers) with no similarity edge between them, and then form clusters around these centers. To obtain the quality from CA models and the runtime efficiency from embedding models, we introduce the novel combo similarity oracle mechanism. In this approach, we utilize the embedding model to guide the selection of queries to be sent to the CA model. When given a set of center documents and a target document, the combo similarity oracle mechanism outputs a center from the set that is similar to the target document, if present. The combo similarity oracle enables us to save on budget by limiting the number of query calls to the CA model when selecting centers and forming clusters. It does this by first ranking centers based on their embedding similarity to the target document, and then querying the CA model for the pair (i.e., target document and ranked center), as shown below.

A combo similarity oracle that for a set of documents and a target document, returns a similar document from the set, if present.

We then perform a post processing step to merge clusters if there is a strong connection between two of them, i.e., when the number of connecting edges is higher than the number of missing edges between two clusters. Additionally, we apply the following steps for further computational savings on queries made to the CA model, and to improve performance at runtime:

We leverage query-efficient correlation clustering to form a set of centers from a set of randomly selected documents instead of selecting these centers from all the documents (in the illustration below, the center nodes are red).

We apply the combo similarity oracle mechanism to perform the cluster assignment step in parallel for all non-center documents and leave documents with no similar center as singletons. In the illustration below, the assignments are depicted by blue arrows and initially two (non-center) nodes are left as singletons due to no assignment.

In the post-processing step, to ensure scalability, we use the embedding similarity scores to filter down the potential mergers (in the illustration below, the green dashed boundaries show these merged clusters).

Illustration of progress of the clustering algorithm on a given graph instance.


We evaluate the novel clustering algorithm on various datasets with different properties using different embedding-based and cross-attention–based models. We compare the clustering algorithm’s performance with the two best performing baselines (see the paper for more details):

To evaluate the quality of clustering, we use precision and recall. Precision is used to calculate the percentage of similar pairs out of all co-clustered pairs and recall is the percentage of co-clustered similar pairs out of all similar pairs. To measure the quality of the obtained solutions from our experiments, we use the F1-score, which is the harmonic mean of the precision and recall, where 1.0 is the highest possible value that indicates perfect precision and recall, and 0 is the lowest possible value that indicates if either precision or recall are zero. The table below reports the F1-score for Kwikbucks and various baselines in the case that we allow only a linear number of queries to the CA model. We show that Kwikbucks offers a substantial boost in performance with a 45% relative improvement compared to the best baseline when averaging across all datasets.

The figure below compares the clustering algorithm’s performance with baselines using different query budgets. We observe that KwikBucks consistently outperforms other baselines at various budgets.

A comparison of KwikBucks with top-2 baselines when allowed different budgets for querying the cross-attention model.


Text clustering often presents a dilemma in the choice of similarity function: embedding models are scalable but lack quality, while cross-attention models offer quality but substantially hurt scalability. We present a clustering algorithm that offers the best of both worlds: the scalability of embedding models and the quality of cross-attention models. KwikBucks can also be applied to other clustering problems with multiple similarity oracles of varying accuracy levels. This is validated with an exhaustive set of experiments on various datasets with diverse properties. See the paper for more details.


This project was initiated during Sandeep Silwal’s summer internship at Google in 2022. We would like to express our gratitude to our co-authors, Andrew McCallum, Andrew Nystrom, Deepak Ramachandran, and Sandeep Silwal, for their valuable contributions to this work. We also thank Ravi Kumar and John Guilyard for assistance with this blog post.


Zero-shot adaptive prompting of large language models

Posted by Xingchen Wan, Student Researcher, and Ruoxi Sun, Research Scientist, Cloud AI Team

Recent advances in large language models (LLMs) are very promising as reflected in their capability for general problem-solving in few-shot and zero-shot setups, even without explicit training on these tasks. This is impressive because in the few-shot setup, LLMs are presented with only a few question-answer demonstrations prior to being given a test question. Even more challenging is the zero-shot setup, where the LLM is directly prompted with the test question only.

Even though the few-shot setup has dramatically reduced the amount of data required to adapt a model for a specific use-case, there are still cases where generating sample prompts can be challenging. For example, handcrafting even a small number of demos for the broad range of tasks covered by general-purpose models can be difficult or, for unseen tasks, impossible. For example, for tasks like summarization of long articles or those that require domain knowledge (e.g., medical question answering), it can be challenging to generate sample answers. In such situations, models with high zero-shot performance are useful since no manual prompt generation is required. However, zero-shot performance is typically weaker as the LLM is not presented with guidance and thus is prone to spurious output.

In “Better Zero-shot Reasoning with Self-Adaptive Prompting”, published at ACL 2023, we propose Consistency-Based Self-Adaptive Prompting (COSP) to address this dilemma. COSP is a zero-shot automatic prompting method for reasoning problems that carefully selects and constructs pseudo-demonstrations for LLMs using only unlabeled samples (that are typically easy to obtain) and the models’ own predictions. With COSP, we largely close the performance gap between zero-shot and few-shot while retaining the desirable generality of zero-shot prompting. We follow this with “Universal Self-Adaptive Prompting“ (USP), accepted at EMNLP 2023, in which we extend the idea to a wide range of general natural language understanding (NLU) and natural language generation (NLG) tasks and demonstrate its effectiveness.

Prompting LLMs with their own outputs

Knowing that LLMs benefit from demonstrations and have at least some zero-shot abilities, we wondered whether the model’s zero-shot outputs could serve as demonstrations for the model to prompt itself. The challenge is that zero-shot solutions are imperfect, and we risk giving LLMs poor quality demonstrations, which could be worse than no demonstrations at all. Indeed, the figure below shows that adding a correct demonstration to a question can lead to a correct solution of the test question (Demo1 with question), whereas adding an incorrect demonstration (Demo 2 + questions, Demo 3 with questions) leads to incorrect answers. Therefore, we need to select reliable self-generated demonstrations.

Example inputs & outputs for reasoning tasks, which illustrates the need for carefully designed selection procedure for in-context demonstrations (MultiArith dataset & PaLM-62B model): (1) zero-shot chain-of-thought with no demo: correct logic but wrong answer; (2) correct demo (Demo1) and correct answer; (3) correct but repetitive demo (Demo2) leads to repetitive outputs; (4) erroneous demo (Demo3) leads to a wrong answer; but (5) combining Demo3 and Demo1 again leads to a correct answer.

COSP leverages a key observation of LLMs: that confident and consistent predictions are more likely correct. This observation, of course, depends on how good the uncertainty estimate of the LLM is. Luckily, in large models, previous works suggest that the uncertainty estimates are robust. Since measuring confidence requires only model predictions, not labels, we propose to use this as a zero-shot proxy of correctness. The high-confidence outputs and their inputs are then used as pseudo-demonstrations.

With this as our starting premise, we estimate the model’s confidence in its output based on its self-consistency and use this measure to select robust self-generated demonstrations. We ask LLMs the same question multiple times with zero-shot chain-of-thought (CoT) prompting. To guide the model to generate a range of possible rationales and final answers, we include randomness controlled by a “temperature” hyperparameter. In an extreme case, if the model is 100% certain, it should output identical final answers each time. We then compute the entropy of the answers to gauge the uncertainty — the answers that have high self-consistency and for which the LLM is more certain, are likely to be correct and will be selected.

Assuming that we are presented with a collection of unlabeled questions, the COSP method is:

Input each unlabeled question into an LLM, obtaining multiple rationales and answers by sampling the model multiple times. The most frequent answers are highlighted, followed by a score that measures consistency of answers across multiple sampled outputs (higher is better). In addition to favoring more consistent answers, we also penalize repetition within a response (i.e., with repeated words or phrases) and encourage diversity of selected demonstrations. We encode the preference towards consistent, un-repetitive and diverse outputs in the form of a scoring function that consists of a weighted sum of the three scores for selection of the self-generated pseudo-demonstrations.
We concatenate the pseudo-demonstrations into test questions, feed them to the LLM, and obtain a final predicted answer.

Illustration of COSP: In Stage 1 (left), we run zero-shot CoT multiple times to generate a pool of demonstrations (each consisting of the question, generated rationale and prediction) and assign a score. In Stage 2 (right), we augment the current test question with pseudo-demos (blue boxes) and query the LLM again. A majority vote over outputs from both stages forms the final prediction.

COSP focuses on question-answering tasks with CoT prompting for which it is easy to measure self-consistency since the questions have unique correct answers. But this can be difficult for other tasks, such as open-ended question-answering or generative tasks that don’t have unique answers (e.g., text summarization). To address this limitation, we introduce USP in which we generalize our approach to other general NLP tasks:

Classification (CLS): Problems where we can compute the probability of each class using the neural network output logits of each class. In this way, we can measure the uncertainty without multiple sampling by computing the entropy of the logit distribution.
Short-form generation (SFG): Problems like question answering where we can use the same procedure mentioned above for COSP, but, if necessary, without the rationale-generating step.
Long-form generation (LFG): Problems like summarization and translation, where the questions are often open-ended and the outputs are unlikely to be identical, even if the LLM is certain. In this case, we use an overlap metric in which we compute the average of the pairwise ROUGE score between the different outputs to the same query.

Illustration of USP in exemplary tasks (classification, QA and text summarization). Similar to COSP, the LLM first generates predictions on an unlabeled dataset whose outputs are scored with logit entropy, consistency or alignment, depending on the task type, and pseudo-demonstrations are selected from these input-output pairs. In Stage 2, the test instances are augmented with pseudo-demos for prediction.

We compute the relevant confidence scores depending on the type of task on the aforementioned set of unlabeled test samples. After scoring, similar to COSP, we pick the confident, diverse and less repetitive answers to form a model-generated pseudo-demonstration set. We finally query the LLM again in a few-shot format with these pseudo-demonstrations to obtain the final predictions on the entire test set.

Key Results

For COSP, we focus on a set of six arithmetic and commonsense reasoning problems, and we compare against 0-shot-CoT (i.e., “Let’s think step by step“ only). We use self-consistency in all baselines so that they use roughly the same amount of computational resources as COSP. Compared across three LLMs, we see that zero-shot COSP significantly outperforms the standard zero-shot baseline.

USP improves significantly on 0-shot performance. “CLS” is an average of 15 classification tasks; “SFG” is the average of five short-form generation tasks; “LFG” is the average of two summarization tasks. “SFG (BBH)” is an average of all BIG-Bench Hard tasks, where each question is in SFG format.

For USP, we expand our analysis to a much wider range of tasks, including more than 25 classifications, short-form generation, and long-form generation tasks. Using the state-of-the-art PaLM 2 models, we also test against the BIG-Bench Hard suite of tasks where LLMs have previously underperformed compared to people. We show that in all cases, USP again outperforms the baselines and is competitive to prompting with golden examples.

Accuracy on BIG-Bench Hard tasks with PaLM 2-M (each line represents a task of the suite). The gain/loss of USP (green stars) over standard 0-shot (green triangles) is shown in percentages. “Human” refers to average human performance; “AutoCoT” and “Random demo” are baselines we compared against in the paper; and “3-shot” is the few-shot performance for three handcrafted demos in CoT format.

We also analyze the working mechanism of USP by validating the key observation above on the relation between confidence and correctness, and we found that in an overwhelming majority of the cases, USP picks confident predictions that are more likely better in all task types considered, as shown in the figure below.

USP picks confident predictions that are more likely better. Ground-truth performance metrics against USP confidence scores in selected tasks in various task types (blue: CLS, orange: SFG, green: LFG) with PaLM-540B.

Zero-shot inference is a highly sought-after capability of modern LLMs, yet the success in which poses unique challenges. We propose COSP and USP, a family of versatile, zero-shot automatic prompting techniques applicable to a wide range of tasks. We show large improvement over the state-of-the-art baselines over numerous task and model combinations.


This work was conducted by Xingchen Wan, Ruoxi Sun, Hootan Nakhost, Hanjun Dai, Julian Martin Eisenschlos, Sercan Ö. Arık, and Tomas Pfister. We would like to thank Jinsung Yoon Xuezhi Wang for providing helpful reviews, and other colleagues at Google Cloud AI Research for their discussion and feedback.

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