Dynamic Uncertainty Ranking: Enhancing retrieval augmented in-context learning for long tail knowledge in LLMs

Imagine a doctor searching for treatment guidance on a rare disease, only to receive confident but incorrect advice from an AI model. In fields like healthcare,  mistakes are not just inconvenient; they can carry life or death consequences. As large language models (LLMs) are increasingly getting involved in decision making, ensuring their accuracy on rare, domain specific knowledge becomes vital.

The challenge

LLMs can excel at answering general knowledge questions but tend to falter when it comes to long tail knowledge, which refers to rare, specialized information critical to high stakes domains. These failures are particularly dangerous when clinicians must diagnose uncommon conditions. In these cases, even a confident error from an LLM could mislead experts and cause serious repercussions.

Retrieval augmented in context learning (ICL) offers a potential remedy. It works by appending carefully selected examples to the query, similar to how a student might be given specific, highly relevant study notes to improve performance on a difficult test. This makes retrieval quality critical. If the examples added to the prompt are poorly chosen, they can mislead the model, resulting in unstable or incorrect outputs and ultimately undermining the user’s trust—precisely the kind of breakdown that is unacceptable in high stakes environments like medicine.

Solutions through retrieval augmented in-context learning

Retrieval augmented in context learning improves an LLM’s ability to answer complex questions by enriching the prompt with examples drawn from external data. Baseline retrieval systems often use methods like BM25, a ranking function based on the frequency of search terms, to identify relevant documents. While useful, these methods prioritize surface similarity, which may not correlate with whether an example actually improves the model’s response. When irrelevant or noisy examples are included, the model can get confused. Instead of amplifying relevant context, poor retrieval injects distraction and degrades performance precisely when precision matters most1.

Introducing Dynamic Uncertainty Ranking

To solve this, we introduce Dynamic Uncertainty Ranking (DUR)2, a new reinforcement learning based method that transforms retrieval into an intelligent, feedback driven process.

Instead of relying purely on superficial matching, DUR evaluates how each candidate example affects the model’s final answer. If adding an example leads the model to a more accurate response, that example is rewarded and ranked higher. If it derails the model’s prediction, it is penalized. This reinforcement learning loop enables DUR to curate examples that genuinely help the LLM produce better answers.

DUR operates in several steps:

  • A baseline retriever such as BM25 first collects a pool of candidate examples.
  • DUR then re ranks these examples using a retriever trained via reinforcement learning, where the system learns which examples to prioritize by rewarding those that improve the model’s performance and penalizing those that degrade it, all guided by the LLM’s own performance feedback.
  • A learnable threshold, a dynamic control point, determines how many examples to include. DUR employs dynamic thresholding, an adaptive strategy that adjusts the number of examples used based on the model’s output quality. If additional examples cause the model’s performance to deteriorate, the threshold rises to prevent further degradation, reduce unnecessary computational load, and strike a balance between efficiency and accuracy.

By filtering for examples that are not just relevant but reliably beneficial, DUR creates a more stable and efficient prompting environment.

Experimental results

We evaluated DUR across five Question Answer datasets drawn from diverse domains: PubMedQA (biomedical), Ethos national (social media), Eval climate (climate discourse), NaturalQuestions (open domain), and T REx (factual knowledge). In every case, DUR outperformed strong retrieval augmented baselines:

  • 2.76 percent average accuracy improvement across datasets
  • Up to 5.96 percent accuracy gain on hard, long tail queries
  • 33 to 66 percent fewer queries compared to PromptPG, thanks to efficient example selection

By reducing the number of retrieved examples through its dynamic thresholding mechanism, DUR avoids overloading the model with distracting or misleading information. This means faster inference, lower compute cost, and improved performance, all with fewer calls to expensive LLM APIs. These benefits are especially impactful in high stakes domains where latency, cost, and reliability matter.

Implications and real-world impact

These numbers can eventually translate into real world advantages in clinical scenarios, such as answering questions about rare diseases or complex comorbidities, where DUR can surface only the most relevant biomedical literature. In PubMedQA evaluations, DUR helped the LLM find correct answers by filtering out misleading references.

Consider a physician trying to determine treatment options for a rare pregnancy related comorbidity. Without strong retrieval support, an LLM might provide generic or misleading advice. With DUR, the LLM selectively draws from relevant, peer reviewed biomedical literature and enables a safer and more accurate recommendation. In our experiments with PubMedQA, DUR consistently surfaced better supporting material and made correct answers more likely, even on complex questions where other retrieval methods failed.3

Cross domain generalization is another important application. For example, a retriever trained using social media discourse data from the Ethos national dataset was able to outperform traditional baseline methods even when applied to entirely different domains, such as biomedical research questions from PubMedQA and factual knowledge from the T REx dataset. This demonstrates that DUR learns generalizable retrieval strategies that extend beyond the domain it was originally trained on. This shows that DUR does not merely memorize patterns specific to one dataset. It learns a robust and transferable retrieval strategy.


Conclusion: Developed for high-stakes applications

As language models are increasingly involved in decision making processes in fields such as medicine, the need for consistent, interpretable, and reliable outputs becomes increasingly clear. In medicine, errors carry real consequences. A misdiagnosis can have significant implications. In such settings, it is not enough for a model to generate plausible answers. It must generate the right ones, and do so for the right reasons.

Dynamic Uncertainty Ranking can offer a practical advancement. By using the LLM’s own responses as feedback, DUR intelligently selects and filters examples to maximize accuracy, stability, and efficiency. It does not just enhance model performance. It could help establish a level of trustworthiness essential for high stakes applications.

Looking forward, further research could explore sequence aware ranking as a way to unlock even greater performance, given that the order of examples can also influence the LLM’s behavior. Improving the sequencing of retrieved examples may unlock even greater performance gains.

Ultimately, methods like DUR represent a future where foundation models are not only powerful but dependable. They support better outcomes in the moments that matter most.

  1. For example: https://arxiv.org/html/2505.06914v1#:~:text=A%20well%2Dknown%20issue%20with,and%20utilizing%20hard%20distracting%20passages. ↩︎
  2. Concept only. This work is in concept phase and may never become a product. Not for Sale. Any reported results are preliminary and subject to change.  Not cleared or approved by the U.S. FDA or any other global regulator for commercial availability. ↩︎
  3. https://arxiv.org/abs/2410.23605 ↩︎

Share