Bridging the Gap Between Intent and Vector Similarity

Vector space models like word embeddings and sentence embeddings are incredibly useful tools in natural language processing. By representing linguistic items as vectors in a high-dimensional space, these models can capture semantic similarity — words or sentences with similar meanings tend to be closer together in the vector space.

However, there is an important distinction between semantic similarity and intent that, if overlooked, can lead to inaccurate or unhelpful system outputs.

The core idea behind retrieval augmented generation (RAG) systems is leveraging external information to enhance the capabilities of a large language model (LLM).

Typically, this involves an initial retriever component which finds relevant context passages from a database.

These passages are encoded into vector representations and compared via cosine similarity to the user’s query, also encoded as a vector. The most similar passages are retrieved and fed into the LLM to inform its response generation.

While powerful, this vector similarity-based retrieval risks retrieving passages that have high semantic similarity but contain inaccurate or outdated information about the intent. This undermines the goal of grounding the LLM’s responses in factual knowledge.

In this article, I’ll explain the mathematical underpinnings of vector similarity, discuss the core differences between semantic similarity and intent, and suggest strategies for developing models that can better understand users’ intended meaning.

Vector Similarity — A Mathematical Refresher

The key to vector space models is representing words or sentences as numeric vectors. The dimensions of these vectors encode meaningful attributes about the item in question. While we may not know exactly what each dimension represents, items with similar meanings tend to cluster together in the vector space.

We can quantitatively assess the similarity of two vectors using metrics like cosine similarity and Euclidean distance.

Cosine Similarity measures the cosine of the angle between two vectors. It is calculated as:

python

cosine_similarity = (A ⋅ B) / (||A|| ||B||)

Where A and B are the two vectors, (A ⋅ B) is their dot product, and ||A|| is the L2 norm (length) of A. This yields similarity scores between -1 and 1, with 1 indicating identical vectors.

Euclidean Distance is simply the straight-line distance between two points (vectors) in multi-dimensional space. It is computed as:

python

distance = √Σ(A - B)2

Where the summation occurs over all dimensions of the vectors. Smaller distances denote greater similarity.

While these mathematical notions of similarity are indispensable tools, they fail to capture a much more complex type of similarity — similarity in intent.

The Crucial Difference Between Semantic Similarity and Intent

Imagine we are building a virtual assistant that can respond to customers’ questions and concerns. The user submits the query:

“I do not want to purchase shoes today.”

Our model embeds this text into a vector A.

Now suppose we have two possible responses embedded as vectors B and C:

B: “Please take a look at our new selections of running shoes and boots in the catalog.”

C: “No problem, we can help you find shoes another time when you are ready.”

Vector A will likely have high semantic similarity with vector B, and lower similarity with C.

But sentence C clearly shows much greater understanding of the user’s intent than B.

This example illustrates that semantic similarity — captured by vector embeddings — does not equate to similarity in meaning and intent. Other factors like context, tone, entity recognition, and external knowledge come into play. Potential strategies to address this include:

Hierarchical classification — categorize queries by general intent before semantics
Attention mechanisms — focus on key words that modify meaning
External knowledge — leverage real-world facts and relationships
User feedback loops — iteratively improve model understanding
Clarification — ask for confirmation when unsure of intent

Well-designed conversational systems combine mathematical representations like vector embeddings with reasoning, world knowledge, and interaction dynamics to fully understand natural language.

Bridging the Intent Gap in Retrieval Augmented LLMs

In recent years, some of the largest advances in natural language AI have come through retrieval augmented large language models (LLMs) such as RAG. These systems pair a large neural language model with a retriever module that provides relevant background knowledge to inform the LLM’s predictions.

The retriever and LLM modules typically leverage vector similarity metrics like cosine similarity between embedded passages and the input text. However, applying the principles we’ve discussed around intent and semantics could improve performance:

The retriever could prioritize retrieving passages about named entities and relationships detected in the query, ensuring highly pertinent facts.
An intent classifier could label queries with intents to allow the retriever to focus on appropriate contexts. For example, a question classified as having an “advice seeking” intent could retrieve advice-oriented passages.
The retrieved passages could be filtered for consistency and accuracy by a factuality classifier before feeding into the LLM.
An interactive retrieval loop could allow the system to ask clarifying questions when the intent is unclear, refining the context documents.
A grounding mechanism could encode key facts about the query into the input representation, priming the LLM towards correct information.
Failures in intent and contradictions could trigger re-ranking or re-retrieval of passages.

By augmenting vector similarity-based retrieval with intent modeling, interactive clarification, grounding, and factuality filtering, we can enhance LLMs’ context-driven reasoning — moving closer to true understanding. Combining strengths of large neural networks and search-based knowledge access will pave the way for more capable and aligned AI systems.

Moving Toward More Human-Centered Responses

Bridging the gap between intentionality and semantics is an active area of NLP research. But while we work to enhance model capabilities, there are also ways to make systems transparent and user-friendly.

Admitting uncertainty and providing alternative actions rather than irrelevant information enhances the user experience.

Instead of potentially inaccurate responses, we can reply:

“I’m sorry, I’m not totally sure I understand your intent. Could you please rephrase or provide more details about what you need help with today?”

Though lacking capabilities, this honest reply establishes realistic expectations and provides a path forward. Being upfront about limitations while guiding users through achievable next steps can lead to more productive human-machine interactions.

The ideal system will fuse state-of-the-art ML with philosophical principles like humility and transparency. Building our models around human needs rather than technological showmanship remains imperative.

While vector embeddings provide a useful mathematical workspace to represent and relate meanings, intent requires a deeper understanding of thoughts, goals, and communication.

Building systems that complement vector similarities with real-world reasoning, iterative learning, and honest conversation moves us toward more helpful and human-centric AI.

In Plain English

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