Wordle Today
Most people are familiar with Wordle by now. It’s that simple yet addictive game where players try to guess a five-letter word within six attempts. Introducing WordMap: Guess the Word of the Day A few weeks ago, while using semantic search in a Retrieval-Augmented Generation (RAG) system (for those curious about RAG, there’s more information in a previous insight), an idea emerged. What if there were a game like Wordle, but instead of guessing a word based on its letter positions, players guessed the word of the day by how close their guesses were in meaning? Players would input various words, and the game would score each one based on its semantic similarity to the target word, evaluating how related the guesses are in terms of meaning or context. The goal would be to guess the word in as few tries as possible, though without a limit on the number of attempts. This concept led to the creation of ☀️ WordMap! To develop the game, it was necessary to embed both the user’s input word and the word of the day, then calculate how semantically similar they were. The game would normalize the score between 0 and 100, displaying it in a clean, intuitive user interface. Diagram of the Workflow The Embedding ChallengeRAGs are frequently used for searching relevant data based on an input. The challenge in this case was dealing with individual words instead of full paragraphs, making the context limited. There are two types of embeddings: word-level and sentence-level. While word-level embeddings might seem like the logical choice, sentence-level embeddings were chosen for simplicity. Word-Level Embeddings Word-level embeddings represent individual words as vectors in a vector space, with the premise that words with similar meanings tend to appear in similar contexts. Key Features However, word embeddings treat words in isolation, which is a limitation. For instance, the word “bank” could refer to either a financial institution or the side of a river, depending on the context. Sentence-Level Embeddings Sentence embeddings represent entire sentences (or paragraphs) as vectors, capturing the meaning by considering the order and relationships between words. Key Features The downside is that sentence embeddings require more computational resources, and longer sentences may lose some granularity. Why Sentence Embeddings Were Chosen The answer lies in simplicity. Most embedding models readily available today are sentence-based, such as OpenAI’s text-embedding-3-large. While Word2Vec could have been an option, it would have required loading a large pre-trained model. Moreover, models like Word2Vec need vast amounts of training data to be precise. Using sentence embeddings isn’t entirely inaccurate, but it does come with certain limitations. Challenges and SolutionsOne limitation was accuracy, as the model wasn’t specifically trained to embed single words. To improve precision, the input word was paired with its dictionary definition, although this method has its own drawbacks, especially when a word has multiple meanings. Another challenge was that semantic similarity scores were relatively low. For instance, semantically close guesses often didn’t exceed a cosine similarity score of 0.45. To avoid discouraging users, the scores were normalized to provide more realistic feedback. The Final Result 🎉The game is available at WordMap, and it’s ready for players to enjoy! Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more
