Agent Spotlight: Edda Bertamini, a Master of Social Work And Cancer Survivor From Germany
February 23, 2023
The final quarter of the year is typically a time when businesses assess their performances and make decisions about the coming year. As we approach Q1 2023, business leaders are increasingly being faced with tough decisions to deal with unpredictable market conditions. Companies are choosing whether to freeze hiring, cut projects, lay off staff, or some combination of all belt-tightening options.
Is there an alternative? How might business leaders stay the course amid rising concerns that their business goals are in jeopardy?
Here's what you need to know.
Economic Volatility’s Impacts are Worsening
According to a recent Invisible survey, two out of three business leaders say their concern over an economic downturn has increased in the last three months. As you survey the landscape, you can imagine why; even the biggest names in Tech like Apple and Amazon are freezing hiring, and the unicorns of yesteryear are having to lay off staff.
The survey found that it’s having a significant impact on the bottom line. 70% of business leaders reported that their company has been negatively impacted by economic volatility, either through slowing or decreasing revenue and sales or that they are unable to meet KPIs.
In addition, 62% said that hiring is on hold at their company. Most expect the hiring freeze to remain for at least 3 months.
Beyond Freezing Hiring and Cutting Costs, What Else are Businesses Doing?
In spite of the defensive posture many companies are taking, the same survey respondents report an aggressive approach to meeting goals at their organization. Over half said that their company has adopted more automation technology in response to economic volatility.
Even more business leaders reported that they plan on adding more automation tech to their businesses before the end of this year. When we ask them about the status of their business goals, we find out why.
52% reported that the inability to grow the headcount on their team is holding them back from achieving their business goals. 69% say if they could outsource and/or automate some of their team’s work, their goals would be achievable.
Why Automation?
There are several reasons why business leaders are turning to automation technology. First, automation can help businesses improve efficiencies and optimize processes.
When done correctly, automation can help businesses save time and money. In addition, automation can help businesses liberate staff from repetitive tasks so they can focus on higher-level work that requires human ingenuity and creativity.
Outsourcing your work, to either people, technology, or both, can be just the right amount of aggressiveness to help you come out on top post-economic downturn.
What Does This Mean for You?
If you are looking to add more automation to your arsenal as we close out Q4, don’t do it alone. Invisible is an expert in helping teams just like yours navigate uncertainty.
We enable clients across any industry to program business processes that we execute with the combination of a flexible workforce and automation expertise.
Ready to achieve your business goals? Let us know!
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The final quarter of the year is typically a time when businesses assess their performances and make decisions about the coming year. As we approach Q1 2023, business leaders are increasingly being faced with tough decisions to deal with unpredictable market conditions. Companies are choosing whether to freeze hiring, cut projects, lay off staff, or some combination of all belt-tightening options.
Is there an alternative? How might business leaders stay the course amid rising concerns that their business goals are in jeopardy?
Here's what you need to know.
Economic Volatility’s Impacts are Worsening
According to a recent Invisible survey, two out of three business leaders say their concern over an economic downturn has increased in the last three months. As you survey the landscape, you can imagine why; even the biggest names in Tech like Apple and Amazon are freezing hiring, and the unicorns of yesteryear are having to lay off staff.
The survey found that it’s having a significant impact on the bottom line. 70% of business leaders reported that their company has been negatively impacted by economic volatility, either through slowing or decreasing revenue and sales or that they are unable to meet KPIs.
In addition, 62% said that hiring is on hold at their company. Most expect the hiring freeze to remain for at least 3 months.
Beyond Freezing Hiring and Cutting Costs, What Else are Businesses Doing?
In spite of the defensive posture many companies are taking, the same survey respondents report an aggressive approach to meeting goals at their organization. Over half said that their company has adopted more automation technology in response to economic volatility.
Even more business leaders reported that they plan on adding more automation tech to their businesses before the end of this year. When we ask them about the status of their business goals, we find out why.
52% reported that the inability to grow the headcount on their team is holding them back from achieving their business goals. 69% say if they could outsource and/or automate some of their team’s work, their goals would be achievable.
Why Automation?
There are several reasons why business leaders are turning to automation technology. First, automation can help businesses improve efficiencies and optimize processes.
When done correctly, automation can help businesses save time and money. In addition, automation can help businesses liberate staff from repetitive tasks so they can focus on higher-level work that requires human ingenuity and creativity.
Outsourcing your work, to either people, technology, or both, can be just the right amount of aggressiveness to help you come out on top post-economic downturn.
What Does This Mean for You?
If you are looking to add more automation to your arsenal as we close out Q4, don’t do it alone. Invisible is an expert in helping teams just like yours navigate uncertainty.
We enable clients across any industry to program business processes that we execute with the combination of a flexible workforce and automation expertise.
Ready to achieve your business goals? Let us know!
LLM Task
Benchmark Dataset/Corpus
Common Metric
Dataset available at
Sentiment Analysis
SST-1/SST-2
Accuracy
https://huggingface
.co/datasets/sst2
Natural Language Inference / Recognizing Textual Entailment
Stanford Natural Language Inference Corpus (SNLI)
Accuracy
https://nlp.stanford.edu
projects/snli/
Named Entity Recognition
conll-2003
F1 Score
https://huggingface.co/
datasets/conll2003
Question Answering
SQuAD
F1 Score, Exact Match, ROUGE
https://rajpurkar.github.i
o/SQuAD-explorer/
Machine Translation
WMT
BLEU, METEOR
https://machinetranslate
.org/wmt
Text Summarization
CNN/Daily Mail Dataset
ROUGE
https://www.tensorflow
.org/datasets/catalog/
cnn_dailymail
Text Generation
WikiText
BLEU, ROUGE
Paraphrasing
MRPC
ROUGE, BLEU
https://www.microsoft.
com/en-us/download/details.a
spx?id=52398
Language Modelling
Penn Tree Bank
Perplexity
https://zenodo.org/recor
d/3910021#.ZB3qdHbP
23A
Bias Detection
StereoSet
Bias Score, Differential Performance
Table 1 - Example of some LLM tasks with common benchmark datasets and their respective metrics. Please note for many of these tasks, there are multiple benchmark datasets, some of which have not been mentioned here.
Metric
Usage
Pros
Cons
Accuracy
Measures the proportion of correct predictions made by the model compared to the total number of predictions.
Simple interpretability. Provides an overall measure of model performance.
Sensitive to dataset imbalances, which can make it not informative. Does not take into account false positives and false negatives.
Precision
Measures the proportion of true positives out of all positive predictions.
Useful when the cost of false positives is high. Measures the accuracy of positive predictions.
Does not take into account false negatives.Depends on other metrics to be informative (cannot be used alone).Sensitive to dataset imbalances.
Recall
Measures the proportion of true positives out of all actual positive instances.
Useful when the cost of false negatives is high.
Does not take into account false negatives.Depends on other metrics to be informative (cannot be used alone)and Sensitive to dataset imbalances.
F1 Score
Measures the harmonic mean of precision and recall.
Robust to imbalanced datasets.
Assumes equal importance of precision and recall.May not be suitable for multi-class classification problems with different class distributions.
Perplexity
Measures the model's uncertainty in predicting the next token (common in text generation tasks).
Interpretable as it provides a single value for model performance.
May not directly correlate with human judgment.
BLEU
Measures the similarity between machine-generated text and reference text.
Correlates well with human judgment.Easily interpretable for measuring translation quality.
Does not directly explain the performance on certain tasks (but correlates with human judgment).Lacks sensitivity to word order and semantic meaning.
ROUGE
Measures the similarity between machine-generated and human-generated text.
Has multiple variants to capture different aspects of similarity.
May not capture semantic similarity beyond n-grams or LCS.Limited to measuring surface-level overlap.
METEOR
Measures the similarity between machine-generated translations and reference translations.
Addresses some limitations of BLEU, such as recall and synonyms.
May have higher computational complexity compared to BLEU or ROUGE.Requires linguistic resources for matching, which may not be available for all languages.
Table 2 - Common LLM metrics, their usage as a measurement tool, and their pros and cons. Note that for some of these metrics there exist different versions. For example, some of the versions of ROUGE include ROUGE-N, ROUGE-L, and ROUGE-W. For context, ROUGE-N measures the overlap of sequences of n-length-words between the text reference and the model-generated text. ROUGE-L measures the overlap between the longest common subsequence of tokens in the reference text and generated text, regardless of order. ROUGE-W on the other hand, assigns weights (relative importances) to longer common sub-sequences of common tokens (similar to ROUGE-L but with added weights). A combination of the most relevant variants of a metric, like ROUGE is selected for comprehensive evaluation.
