Tackling Explainability and Interpretability in Language Models

RAG

Although RAG doesn’t necessarily supply explainability so much as it increases the accuracy of models by expanding the context from which they produce answers, it still makes their outputs easier to understand. Or, more specifically, RAG “can measure the groundedness or the faithfulness of a response,” Sammons commented.

Here are some of the ways organizations employ RAG to increase the transparency of model outputs:

• Attribution: One way RAG allows models to generate more dependable outputs is by presenting specific sources, passages, and sentences from which a model’s response was culled. Such citations provide improved “visibility to users in the source of the responses and traceability of the source content,” Lowe indicated.
• AI Evaluation: With this technique, organizations compile the documentation that will serve to augment prompts into a knowledgebase, then have humans create sample questions and responses about it. Those answers, which have already been approved by organizations, can be used to assess the response of RAG systems before they’re put in production. “Now, you can ask a question of your RAG and first call your knowledgebase, do a search, and then call the LLM and [see] what response you get for the exact same question,” Mishra said. Organizations can also employ language models to devise the questions asked of the knowledgebase, which conserves time and effort on the part of humans, before following the foresaid process to evaluate their RAG systems.
• Data Quality: Although data quality is foundational to any data-centric practice in general, it’s integral for getting reliable results from RAG or from language models altogether. “If your data is conflicting, unstructured, and wrong, AI is only going to interpret what it’s suggesting,” Lowe pointed out. “To eliminate hallucinations and false returns, you need to be in good shape at the fundamental data level.”

HUMAN INTERPRETABLE

There is no shortage of measures for enlarging the transparency into how language model outputs are derived from specific inputs. Users can avail themselves of chain of thought techniques, reasoning or thinking models (with chain of thought embedded in them), multi-agent frameworks, RAG, or context engineering. Each of these constructs makes the inner workings of models more readily apparent. It may not do so at the interpretability and explainability level of the deep neural network heyday (before language models), but it helps prevent the proverbial black box effect.

According to Stephenson, there’s one final consideration. “All of it needs to be human interpretable,” Stephenson revealed. “This is a very important piece. These inner monologues have to happen in human language: the way we talk, read, speak, and understand it. If it was just bytes and numbers, we wouldn’t be able to read and understand it. So, a very important piece of explainability is having a way to express what the model is doing in a way that a human can understand.”

Even smaller organizations can tackle explainability and interpretability in language models by looking at reasoning models, context engineering, and RAG. These offer practical pathways to transparency and accountability. Critical to determining success is whether outputs from models remain interpretable, auditable, trustworthy, and aligned with human understanding.