Invisible Spotlight

Agent Spotlight: Sanjay Sharma, an Experienced And Multilingual Agent from India

Sanjay Sharma

Experienced And Multilingual Agent from India

Invisible Spotlight

"Every weekend we would go to slums and distribute free water purifiers to those in need."

Meet Sanjay, an Invisible agent of more than a year who can speak no less than 6 languages.

We reached out to learn more about him!

Where are you located?

I’m located in Siliguri, India.

What languages do you speak?

I can speak English, Hindi, Nepali, and other Indian regional languages such as Mizo, Manipuri, and Kuki.

How would you describe yourself?

I’m an introverted individual who loves nature & technology. Always willing to lend a helping hand to others!

What are your hobbies?

I love photography, watching movies, and casual gaming.

What is your favorite quote?

‘’The greatness of a person is not in how much wealth they acquire, but in their integrity and their ability to affect those around them positively”. Bob Marley

What did you do before Invisible?

Before I joined Invisible, I worked as a Billing Support Specialist for a major telecommunications group from the UK. It was not an easy job, but as they say: Nothing good comes easy!

What are Your Top 3 moments at Invisible?

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02|

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Why did you decide to join Invisible?

I decided to join invisible as it provided better work-life balance, and eliminated the daily commute. It offered more flexible working hours.

Can you name something you are proud of? An achievement, or anything else in your life!

I’m most proud of my volunteering contributions at Waves for Water (W4W).  Every weekend we would go to slums and distribute free water purifiers to those in need. Its a chance to connect with different communities in a new way and to make a difference in people’s lives. I think everyone should volunteer to experience the joy that we receive by helping others.

How would you describe Invisible to someone who wants to join our team?

I'd describe Invisible as flexible, exciting, rewarding and supportive - an organization that takes feedbacks seriously and listens to its agents.

What is it that keeps you at Invisible?

Flexibility and transparency are two major factors. The leaders are also kind and the, hierarchy seems to be in a flat line. All team members are friendly!

I'd describe Invisible as flexible, exciting, rewarding and supportive.

Thank you for being a helpful team member, Sanjay!

Want to join an elite team of global professionals?

Andrew Hull

Meet Sanjay, an Invisible agent of more than a year who can speak no less than 6 languages.

We reached out to learn more about him!

Where are you located?

I’m located in Siliguri, India.

What languages do you speak?

I can speak English, Hindi, Nepali, and other Indian regional languages such as Mizo, Manipuri, and Kuki.

How would you describe yourself?

I’m an introverted individual who loves nature & technology. Always willing to lend a helping hand to others!

What are your hobbies?

I love photography, watching movies, and casual gaming.

What is your favorite quote?

‘’The greatness of a person is not in how much wealth they acquire, but in their integrity and their ability to affect those around them positively”. Bob Marley

What did you do before Invisible?

Before I joined Invisible, I worked as a Billing Support Specialist for a major telecommunications group from the UK. It was not an easy job, but as they say: Nothing good comes easy!

Overview

LLM Task

Benchmark Dataset/Corpus

Sentiment Analysis

SST-1/SST-2

Natural Language Inference /  Recognizing Textual Entailment

Stanford Natural Language Inference Corpus (SNLI)

Named Entity Recognition

conll-2003

Question Answering

SQuAD

Machine Translation

WMT

Text Summarization

CNN/Daily Mail Dataset

Text Generation

WikiText

Paraphrasing

MRPC

Language Modelling

Penn Tree Bank

Bias Detection

StereoSet

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

https://www.salesforce.
com/products/einstein/
ai-research/the-wikitext-dependency-language-modeling-dataset/

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

https://huggingface.co/
datasets/stereoset

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 Selection

Metric

Usage

Accuracy

Measures the proportion of correct predictions made by the model compared to the total number of predictions.

Precision

Measures the proportion of true positives out of all positive predictions.

Recall

Measures the proportion of true positives out of all actual positive instances.

F1 Score

Measures the harmonic mean of precision and recall.

Perplexity

Measures the model's uncertainty in predicting the next token (common in text generation tasks).

BLEU

Measures the similarity between machine-generated text and reference text.

ROUGE

Measures the similarity between machine-generated and human-generated text.

METEOR

May have higher computational complexity compared to BLEU or ROUGE.Requires linguistic resources for matching, which may not be available for all languages.

Pros

Cons

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.

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.

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.

Robust to imbalanced datasets.

Assumes equal importance of precision and recall.May not be suitable for multi-class classification problems with different class distributions.

Interpretable as it provides a single value for model performance.

May not directly correlate with human judgment.

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.

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.

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.

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.

Andrew Hull

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