Why Excel files with mostly numbers are usually left out of training data

When you’re sorting through a mountain of documents, some things stand out more than others. Some pages hum with narrative energy; others march in with cold, hard numbers. For teams that work in document-heavy environments—like Relativity’s world of eDiscovery and project workflows—it's helpful to think about what kinds of content actually teach a language-model system to understand human text. Here’s the core idea, wrapped in a story that stays practical and human.

Numbers that don’t speak the language

Let me explain a simple truth: not all data is equally useful for teaching a model to understand words, context, and meaning. Excel files filled with numbers contain heaps of precise data—hidden in rows and columns, formulas flickering behind the scenes, charts that summarize trends. But that’s mostly a numerical story. It’s a language of counts, averages, and percentages rather than a narrative you can follow with a reader’s eye.

Think about what a language model needs to learn: how sentences flow, how ideas connect, how tone shifts, and how arguments are built. It’s not just about mapping symbols to values; it’s about understanding nuance, implication, and the way people express themselves. Purely numeric files rarely provide those cues. They’re great for analytics in a dashboard or a data lake, but they’re not the best teachers for language comprehension or contextual reasoning.

Excel files with mostly numbers: the likely exclusion

The question is straightforward, and the answer is telling: Excel files that are mostly numbers are typically excluded from training data meant for language tasks. They’re excellent for numbers, but they don’t offer the kind of prose, descriptions, and narrative scaffolding that help a model grasp how humans write, argue, and explain.

To be clear, there might be some text embedded in spreadsheets—cell comments, tab names, maybe a few narrative labels. But those bits are optional, often sparse, and not the core driver of meaning. When you strip away the context, the rest is a language that’s more about data structure than storytelling.

Other formats bring more to the table for language-focused learning

If we compare formats, Word documents, PDFs, and PowerPoint presentations tend to carry richer linguistic content. They’re filled with headings that guide you, bullets that sketch points, and paragraphs that carry a point of view. They also reflect real-world communication: a memo outlining a plan, a briefing deck with talking points, or a case summary that stitches facts into a narrative.

• Word documents: Mostly prose, with occasional lists and headings. They’re the bread-and-butter of written communication—explanations, justifications, and descriptions that reveal how people structure arguments and present context.

• PDFs: These can be scanned images or text layers, but even when they’re modern, they carry a blend of narrative and design. You’ll find captions, footnotes, and the way information is organized on a page. That spatial, reader-facing arrangement adds nuance to how meaning is conveyed.

• PowerPoint presentations: Slides are often concise and visually driven. They force you to distill ideas into digestible chunks, which is a different kind of language skill—one that’s about coherence, flow, and clarity, often with speaker notes that reveal intent.

From a Relativity PM perspective, these formats are especially relevant. Relativity workflows handle documents, review annotations, and communication trails that are inherently textual. Training data drawn from these sources can teach models how people summarize risk, outline milestones, and explain decisions in a way that aligns with real-world practice.

Why context matters in a project-management ecosystem

In a project setting, words carry weight. A note in a Word document about a timeline isn’t just words; it’s a claim about schedule, dependencies, and accountability. A PDF briefing may stitch together policy, risk, and impact in a way that helps someone decide how to allocate resources. A slide deck often distills complex information into a narrative arc and highlights what matters most.

For teams relying on document-centric workflows, language understanding isn’t a nice-to-have—it’s a productivity multiplier. It helps automate tagging, summarization, and routing of documents; it supports smarter search; it makes it easier to surface relevant context during reviews or when discussing next steps with stakeholders. The more language-rich the training data, the better the model can grasp human intent, phrasing, and nuance.

A practical guide to building text-rich datasets

If you’re curating materials for language-focused training in a Relativity-heavy environment, here are some practical angles to consider:

• Prioritize narrative content: Include Word docs and PDFs where authors explain processes, decisions, and risk factors. Look for materials that tell a story—problem, analysis, conclusion—rather than mere data dumps.

• Value structure as a signal: Documents with clear headings, bullet points, and well-organized sections help the model learn how information is typically laid out and how tone shifts across sections.

• Include context, not just content: Annotations, comments, and metadata can reveal how people think about documents. Where possible, pair raw text with notes that explain why a decision was made or what a slide intends to convey.

• Be mindful of redaction and privacy: In a legal-tech setting, sensitive information is a real constraint. Build datasets with proper masking and governance so the training material remains usable and compliant.

• Balance variety with relevance: A diverse mix of formats helps the model generalize, but keep the content aligned with the kinds of language you’ll encounter in real-world workflows.

A caveat about numeric data in a language model

That said, there’s room for numeric data when the goal expands beyond pure language learning. If you’re training a model to reason about data-driven narratives—like a report that blends numbers with a story about performance, risk, and impact—you can include tables or charts as part of a broader context. The trick is to ensure the text around the numbers carries the continuity and meaning a reader would expect, rather than making the model learn to parse raw tables in isolation.

In the end, the goal isn’t to shun numbers altogether. It’s to recognize when numbers serve a story and when they stand apart from the story. For language understanding, the former is gold; the latter is noise you might want to exclude or treat separately.

Relativity PM realities: how this connects to real-world work

The Relativity ecosystem thrives on documents—how they’re created, shared, reviewed, and governed. Teams juggle timelines, compliance requirements, and stakeholder communications, all of which live in prose as much as in data. When you’re thinking about datasets for language-focused learning, you’re not just training a model; you’re shaping how teams search, summarize, and interpret information.

That connection is the bridge between theory and practice. A well-constructed corpus rooted in Word docs, PDFs, and slide decks mirrors the way people in PM roles communicate decisions. It helps a model learn how to extract essence from long briefs, how to recognize the logic of a plan, and how to distinguish opinion from fact. All of this matters when you want tools that assist teams in navigating complex projects with clarity and speed.

A gentle, human-centered takeaway

If you’re organizing content for language-oriented training, remember this simple rule of thumb: prioritize text-rich formats, and treat numbers as a separate stream that can feed analytics or numerical reasoning tasks, not the main channel for language learning. Excel’s strength is precision and macro-level crunching; language models thrive on narrative, context, and the subtleties of human expression found in prose and slide notes.

The choice matters because it shapes how well a model will assist teams in a Relativity-based workflow. When a user searches a repository, or asks a system to summarize a briefing, the goal is to deliver something that reads naturally, makes sense, and points to the right next step. That’s the kind of capability you get when the training data leans into language-rich materials rather than plain numbers.

Final reflection: what to carry forward

So, what’s the bottom line? In the realm of language-focused learning for a Relativity project environment, Excel files with mostly numbers are typically excluded from the core training corpus. They simply don’t carry the narrative texture that helps a model understand human language and context as it’s used in real work. Word documents, PDFs, and PowerPoint decks, on the other hand, bring the stories, the arguments, and the structures that readers actually follow.

If you’re shaping a training approach for a PM-centered, document-heavy setting, aim for a balanced library: solid textual content that mirrors day-to-day communications, complemented by numeric data where it enriches the story rather than dominates it. The result is a more fluent, more useful assistant—one that helps teams move with clarity, not just speed. And that, after all, is what good project leadership looks like in practice.