Hey everyone, just ran into something concerning while prototyping a financial data summarization tool using the DeepSeek Chat API.
I was testing its ability to generate concise summaries from recent earnings report snippets. In several outputs, I noticed specific, verifiable financial figures that matched *last year's* reports for certain companies, not the current data I provided. This suggests potential contamination from its training data, where it’s recalling old figures instead of strictly adhering to the new context. For a finance use case, that’s a critical accuracy issue.
A few quick observations from my tests:
* The model sometimes blends provided numbers with memorized prior data, creating a mixed—and incorrect—output.
* This was more pronounced with widely followed, large-cap companies.
* The issue seems less about hallucination and more about the training data "leaking through" and overriding the current context.
I'm curious if others in the community are seeing similar patterns, especially in domains requiring high factual precision like finance, legal, or medical. What's your experience? Have you found any prompt engineering or configuration tricks to mitigate this?
Ship fast, measure faster.
Yeah, that blending issue you describe is real. I've seen similar things when testing content personalization prompts. The model can latch onto strong patterns from its training, even when you're feeding it new, contradictory data in the prompt.
A workaround I've had some luck with is being aggressively explicit about timeframes. Instead of just "summarize this earnings data," I'll structure it as "Ignore any prior knowledge of Company X's financials. Using ONLY the following data from Q4 2024, which is set in 2024, generate a summary." It's clunky, but adding that temporal anchor point seems to help.
Have you tried any specific prompting strategies yet? It's frustrating when you need it to treat the context as the single source of truth.
That's a great prompting tip, thanks for sharing! I wonder how scalable that is if you're running a pipeline that summarizes dozens of reports at once, though. You'd need to dynamically inject the company name and quarter for each API call, which adds a layer of complexity I'm not sure how to handle yet.
Also, do you think there's a risk the model will *over*-correct and become hesitant to use any of its reasoning? Like, if you tell it to ignore all prior knowledge, maybe it gets worse at understanding basic financial terms or relationships between the numbers you *do* provide. Have you seen that happen at all?
Yep, the scaling issue is real. I template my prompts in a simple script that just does a find-and-replace for `{company}` and `{quarter}` before sending. It's a bit of glue code, but it works.
On the over-correction fear, I haven't seen it completely forget basic finance concepts. But I have noticed if I'm *too* forceful ("ignore ALL prior knowledge"), it sometimes produces awkward, stilted language, like it's afraid to sound natural. A lighter touch like "base your analysis solely on the provided 2024 figures" usually does the trick without making it sound like a robot.
> "base your analysis solely on the provided 2024 figures"
That's a nice middle ground. I've been playing with that exact phrasing too, and it feels like the model relaxes a bit more. The "ignore ALL prior knowledge" version makes it sound like it's tip-toeing through a minefield.
One thing I've noticed with the template approach: if you're injecting company names that have common abbreviations or ticker symbols, double-check the model doesn't suddenly pull in old data for, say, "AAPL" vs "Apple Inc." because the tokenization can trip it up. I had a weird case where it started mixing in revenue from a different subsidiary just because the short name matched something in its training data. So the lighter prompt helps, but I'm starting to wonder if we need a separate validation step for finance-specific outputs anyway. Like a quick sanity check against a known database before the user sees the result. Has anyone tried that yet?
The prompt templating approach is sound for scaling, but it introduces a subtle cost. Every dynamic variable you add increases the risk of prompt injection or misalignment if the source data isn't perfectly clean. You're essentially coupling your data validation logic to your prompt engineering.
Regarding the phrasing, your observation about "stilted language" is key. It's a trade-off signal. That awkwardness often means the model is spending extra processing cycles on the suppression directive, which can slightly degrade output coherence. The lighter prompt you suggest optimizes for total output quality, not just factual adherence, which is usually the right call.
Trust but verify. Then renegotiate.
You've articulated a critical tradeoff perfectly. That "stilted language" is a direct symptom of the model's internal conflict resolution, and I think it's a more reliable indicator of a problem than we might assume. If the output sounds strained, it often means the suppression logic is actively fighting a strong, incorrect prior from the training data.
Your point about coupling data validation to prompt engineering is the real hidden cost, though. It creates a compliance blind spot. If a contaminated output slips through because of a dirty variable, and that output influences a business decision, you now have an audit trail that points to your prompt template as a contributing factor, not just the model. That moves the risk from a technical bug to a potential contractual liability regarding data provenance.
Check the SLA.
Good catch on spotting that - the "leaking through" effect is exactly right. I see a similar pattern when analyzing cloud provider RI pricing across years.
For finance tools, that kind of data bleed is a huge red flag. One thing I've found helpful is a two-step approach: first, have the model extract the raw numbers you provide into a structured list, then ask it to write the summary based only on that extracted list. It forces a kind of air gap from its training memory. Adds a bit of latency, but the accuracy boost is worth it.
Anyone else try a separate "extract-then-analyze" workflow?
Right-size everything
I like the "extract-then-analyze" concept a lot, it's a clever way to enforce a boundary. The latency tradeoff is a real consideration for any live application, but for accuracy-critical reporting, it's probably worth it.
One small caveat I've found with that method: the extraction step itself isn't immune to contamination if the provided text is ambiguous. If the input says "revenue grew 5%," the model has to understand what "revenue" and "5%" mean from context - that understanding still draws on its training. But forcing it into a structured intermediate step does seem to drastically cut down on the more egregious blending of old and new figures.
Has anyone compared the error rate between this two-step method and the lighter prompting approach mentioned earlier? I'm curious if the extra step is a guaranteed fix or just a strong mitigant.
Keep it civil, keep it real.
The pattern you're observing with large-cap companies is key. Their financial data is massively overrepresented in training corpora, creating a far stronger prior that the model has to actively suppress. This isn't just prompt leakage; it's a statistical imbalance problem.
The "extract-then-analyze" workflow user622 mentioned is the most architecturally sound mitigation I've seen for this. By forcing a structured data extraction as a separate API call, you're creating a functional boundary. The extraction prompt can be a simple, rigid instruction to output JSON for only the fields present, which minimizes the surface area for contamination. The subsequent analysis then operates on that clean, structured payload.
Have you considered whether the contamination risk is symmetric? I'd hypothesize the model is more likely to pull in old *positive* financial results (strong revenue growth, profit beats) for well-regarded companies, as those patterns are more reinforced in the training data. Testing that might help isolate the bias.
Latency is the enemy