Vinija's Notes • Models • Bard

Introduction

Google’s conversational AI, Bard is designed to help its customers with information retrieval by the use of NLP.
Bard, which is a lightweight version of LaMDA, will be updating Google’s search to display a synthesized version of the answer you’re searching for.
LaMDA (Language Model for Dialogue Applications), which is built on Transformers, was trained on dialogue in order to better understand the nuances of language.
The model can engage in more open-ended, dynamic conversations with users and respond to more complex queries.
Google envisions that LaMDA could be used in a variety of applications, including customer service, language translation, and voice assistants.
As the blog states, LaMDA was built on Towards a Human-like Open-Domain Chatbot.
In this article, we will dissect the technicalities of Meena, the predecessor to Bard, until more research is made publicly available for Bard.

Meena is a 2.6 billion parameter neural conversational model that has been trained end-to-end by Google Research’s Brain Team.
The Chatbot Meena is able to hold sensible conversations and this is because it was evaluated through a human metric called Sensibleness and Specificity Average (SSA).

SSA is designed to measure how sensible and specific Meena’s responses are in the context of a given conversation.
SSA measures the extent to which the language model produces sensible and specific responses to prompts.
To compute SSA, a set of prompts is given to the language model, and the generated responses are evaluated by human judges.
Each response is given a score between 0 and 1, representing its sensibleness and specificity.
Sensibleness refers to the degree to which the response is coherent and grammatically correct, while specificity refers to the degree to which the response is relevant and informative.
The average of the sensibleness and specificity scores for all responses is then calculated to obtain the SSA score.
A higher SSA score indicates that the language model produces more sensible and specific responses.
A quick note here about perplexity, which is a commonly used metric to evaluate language models,while perplexity is a useful metric for evaluating the quality of language models for tasks such as machine translation or text generation, it is not always a reliable measure of overall language model performance.
SSA is a metric that is specifically designed to evaluate the quality of generated text.
It provides a more direct measure of the language model’s ability to produce coherent and relevant responses to prompts.

“The training objective is to minimize perplexity, the uncertainty of predicting the next token (in this case, the next word in a conversation). At its heart lies the Evolved Transformer seq2seq architecture, a Transformer architecture discovered by evolutionary neural architecture search to improve perplexity.” Google AI
The data Meena used to train on was mined and filtered from public domain social media conversations.
The conversations with multiple speakers are stored in a tree structure where the root of the tree is the first message and the replies are the child nodes.
“The Meena model has 2.6 billion parameters and is trained on 341 GB of text, filtered from public domain social media conversations.
Compared to an existing state-of-the-art generative model, OpenAI GPT-2, Meena has 1.7x greater model capacity and was trained on 8.5x more data.” Google AI

Meena’s architecture includes: an Evolved Transformer (ET) seq2seq model with 2.6B parameters with 1 ET encoder block and 13 ET decoder blocks as shown below.

The “Evolved Transformer” (ET) is a neural architecture that combines evolutionary algorithms and the Transformer architecture for automatic neural architecture search.
The encoder, per usual, encodes the contextual meaning of the conversation while the decoder works on a sensible response.
“Through tuning the hyper-parameters, we discovered that a more powerful decoder was the key to higher conversational quality.” Google AI
For decoding in particular, the paper found that a model with sufficiently low perplexity can achieve diverse and high-quality responses using a simple sample-and-rank decoding strategy.
In this strategy, they sampled N independent candidate responses using random sampling with a temperature T, and selected the candidate response with the highest probability as the final output.
The temperature T is a hyper-parameter that regulates the probability distribution of the next token during decoding.

Meena was able to achieve a perplexity of 10.2 (smaller is better) and that translates to an SSA score of 72% compared to 86% SSA achieved by the average person.
The full version of Meena a filtering mechanism and tuned decoding had further advances the SSA score to 79%.