Keeping LLMs Updated Without Full Retraining: Approaches & Trade-Offs

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Table of Contents

• Keeping LLMs Updated Without Full Retraining: Approaches & Trade-Offs

• Approaches to Updating LLMs

  • Periodic Fine-Tuning on New Data

  • Retrieval-Augmented Generation (RAG)

  • Parameter-Efficient Updates (LoRA and Adapters)

• Implementation in Frameworks

• Trade-Off Analysis: Cost, Latency, and Efficiency

Keeping LLMs Updated Without Full Retraining: Approaches & Trade-Offs

Large Language Models (LLMs) trained once on a static corpus can become outdated as new information emerges. Researchers and industry practitioners are exploring methods to update LLMs’ knowledge without retraining from scratch. We review three major approaches – periodic fine-tuning, retrieval-augmented generation (RAG), and parameter-efficient updates (e.g. LoRA adapters) – comparing their mechanisms, implementations in popular frameworks, and trade-offs in compute cost, latency, and efficiency.

Approaches to Updating LLMs

Periodic Fine-Tuning on New Data

Fine-tuning involves continuing an LLM’s training on fresh domain data or recent knowledge. By exposing the model to a new knowledge base (e.g. recent text corpora), the model’s weights adapt to incorporate that information (HERE). This method directly infuses new facts into the model and can improve its domain-specific performance or update its knowledge. For example, an LLM pre-trained in 2022 could be fine-tuned on 2024 news articles to learn about current events.

However, fine-tuning a large model is resource-intensive. Modern LLMs have billions of parameters, and full fine-tuning requires substantial GPU memory and time (Rank Also Matters: Hierarchical Configuration for Mixture of Adapter Experts in LLM Fine-Tuning). Continually repeating this process for each data update is often impractical as model sizes and new information volumes grow (Towards Understanding Systems Trade-offs in Retrieval-Augmented Generation Model Inference). Moreover, continual fine-tuning can cause catastrophic forgetting – the model may overwrite or lose previously learned knowledge when learning new facts (HERE). Research confirms that LLMs struggle to fully absorb brand-new factual information through limited fine-tuning, often needing many repeated examples of a fact to truly learn it . Even when new facts are learned, fine-tuning on them can inadvertently increase the model’s tendency to hallucinate (generate incorrect information) ( Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations?). This highlights the risk that naïvely injecting new knowledge via fine-tuning may degrade an LLM’s reliability .

Despite these challenges, periodic fine-tuning is used in practice for domain adaptation and alignment. In frameworks like PyTorch and TensorFlow, fine-tuning is implemented by continuing the training loop on new data with a small learning rate. Libraries such as Hugging Face Transformers provide high-level APIs (e.g. Trainer) to load a pre-trained model and fine-tune on a new dataset with minimal code. For instance, OpenAI’s API allows fine-tuning GPT-3.5 on custom data (though such fine-tuning updates mainly task behavior, not general knowledge). Engineers often employ techniques like rehearsal (mixing some original training data or synthetic data to retain old knowledge) to mitigate forgetting in continual fine-tuning . Nonetheless, the compute demands remain significant for large models – hence the motivation for alternative update methods.

Retrieval-Augmented Generation (RAG)

Retrieval-Augmented Generation (RAG) keeps an LLM’s knowledge up-to-date by retrieving relevant information from an external knowledge source at query time, instead of baking all knowledge into the model’s weights (HERE). In a RAG pipeline, when the LLM receives a query, it first uses a search module (e.g. a vector database or search engine) to fetch documents or facts related to the query. The retrieved text is then provided to the LLM as additional context (prepended to the prompt) for generation (Towards Understanding Systems Trade-offs in Retrieval-Augmented Generation Model Inference). This way, the LLM can leverage fresh, authoritative information stored in an external database or index, even if that information wasn’t in its original training data.

RAG effectively decouples knowledge updating from model training. The model itself remains fixed, but the external knowledge base can be continuously expanded or edited. This addresses the problem of static knowledge: new data can be ingested into the database in real-time, so the system’s outputs reflect current information without needing to retrain the model weights ( Retrieval-Augmented Generation for Large Language Models: A Survey). Studies have shown that RAG often yields better performance on knowledge-intensive tasks than fine-tuning on new data, because the model can draw on a larger, up-to-date fact repository . In particular, RAG consistently outperforms unsupervised fine-tuning for both previously seen and entirely new knowledge, providing more accurate factual responses . It also reduces hallucinations by grounding the model’s output in retrieved evidence.

Implementing RAG typically involves combining an LLM with a retriever. In practice, frameworks like Hugging Face Transformers include RAG-specific models (e.g. a BART decoder with a built-in retriever) and pipelines to streamline this process. Engineers can also use libraries like LangChain or LlamaIndex to connect any LLM (e.g. GPT-4, Llama-2) with a vector store (FAISS, ElasticSearch, etc.) for document retrieval. For example, a RAG system might use a SentenceTransformer (for embeddings) to index a company’s internal documents; at query time, relevant documents are retrieved and fed into a GPT-3-style model to answer user questions with up-to-date reference text. This approach has been adopted in industry for applications like customer support chatbots and search engines (e.g. Bing Chat) to provide current, source-backed answers.

Pros: RAG avoids the heavy cost of frequent retraining – only the retrieval index needs updating, which is much cheaper. It “incorporates knowledge from external databases, allowing continuous knowledge updates and integration of domain-specific information” ( Retrieval-Augmented Generation for Large Language Models: A Survey). It effectively expands the model’s accessible knowledge to potentially millions of documents without increasing model size (HERE).

Cons: RAG introduces additional system complexity and latency. Each query now requires a retrieval step (embedding the query, searching the database) before generation. This can slow down response time: real-time retrieval can add tens to hundreds of milliseconds depending on the index size and infrastructure (Towards Understanding Systems Trade-offs in Retrieval-Augmented Generation Model Inference). A recent systems study notes that while RAG avoids continuous retraining, it incurs “slower model inference times” as the trade-off . The retrieval step can comprise a significant portion of end-to-end latency (in one analysis, ~41% of total latency) and requires maintaining a fast, scalable search infrastructure. Another consideration is that RAG’s accuracy hinges on the retriever – if relevant documents are missed or if the knowledge base is incomplete, the model’s answer may still be incorrect. In practice, caching frequent queries and using efficient vector indices or rerankers can help optimize RAG latency and reliability. Overall, RAG offers a highly flexible, cost-effective way to keep LLM outputs up-to-date, offloading knowledge maintenance to an external database rather than the model itself .

Parameter-Efficient Updates (LoRA and Adapters)

Rather than fine-tuning all billions of parameters of an LLM, parameter-efficient fine-tuning (PEFT) updates only a small subset of parameters or adds small new modules to incorporate new knowledge or skills. Techniques under this umbrella include LoRA (Low-Rank Adaptation), adapter layers, prefix tuning, and prompt tuning. These approaches keep the original model weights mostly frozen and train a much smaller number of new parameters that can be “attached” to the model (Rank Also Matters: Hierarchical Configuration for Mixture of Adapter Experts in LLM Fine-Tuning). This drastically reduces the computational burden of updates.

LoRA is a popular PEFT method that injects trainable low-rank matrices into each layer of the model instead of modifying the full weight matrix . During fine-tuning, the base weights WW stay fixed and two small matrices AA and BB are learned such that W+α⋅ABW+α⋅AB approximates the adaptation for the new data. These low-rank adapters typically constitute only a fraction of a percent of the model’s parameters. For example, a LoRA adapter often adds roughly 1% (or less) to the model size while achieving performance close to a full fine-tune (TGI Multi-LoRA: Deploy Once, Serve 30 Models). Hugging Face reports that LoRA adapters “typically only add about 1% of storage and memory overhead compared to the base LLM while maintaining quality comparable to fully fine-tuned models” . In other words, LoRA can infuse new knowledge or task behavior with minimal resource cost. Another benefit of freezing the original weights is that it preserves the model’s original knowledge, mitigating catastrophic forgetting – the new small matrices adjust the model for new data without overwriting everything it previously knew .

Adapters (Houlsby et al., 2019) follow a similar idea by inserting small neural layers (with far fewer parameters than the full model) into each transformer block. Only these adapter layers are trained on the new data. Other PEFT techniques like prefix tuning or prompt tuning keep the model weights unchanged and instead learn a small set of new “virtual tokens” or embeddings that steer the model when prepended to the input. All these methods significantly reduce the number of trainable parameters required to update or personalize an LLM (Rank Also Matters: Hierarchical Configuration for Mixture of Adapter Experts in LLM Fine-Tuning), making frequent updates more feasible. A recent study notes that “LoRA has gained considerable attention for injecting low-rank matrices (adapters) into the model’s weight matrices, enabling efficient fine-tuning for downstream tasks without substantially increasing the number of trainable parameters” .

In practice, parameter-efficient tuning is widely supported. On PyTorch/Hugging Face, the 🤗 PEFT library allows users to apply LoRA or other adapters to any Transformer model with just a few lines of code. You load the base model (e.g. a 7B parameter LLaMA) and wrap it with a LoRA config specifying the rank (size) of the adaptation matrices, then train normally; only the LoRA layers will update. This avoids needing multi-GPU setups – e.g. using LoRA with 4-bit quantization (QLoRA), researchers finetuned a 65B model on a single 48GB GPU while retaining full 16-bit fine-tune performance ( QLoRA: Efficient Finetuning of Quantized LLMs). Google’s TensorFlow Keras has integrated support as well: for instance, KerasNLP’s Gemma LLM can be fine-tuned with LoRA and QLoRA, which “significantly reduces the number of trainable parameters, decreasing training time and GPU memory usage, while maintaining output quality.” (Parameter-efficient fine-tuning of Gemma with LoRA and QLoRA). These frameworks handle merging the base model with adapter weights at inference time seamlessly.

One compelling industry application is multi-adapter serving. Since LoRA adapters are so small, one can train multiple different LoRA modules (e.g. one per task or per data update) and swap them in and out of the base model on the fly. Hugging Face’s Text Generation Inference (TGI) server recently introduced “Multi-LoRA” support, allowing a single deployed model to host dozens of LoRA adapters simultaneously (TGI Multi-LoRA: Deploy Once, Serve 30 Models). In this setup, the system loads the large base model once, then for each incoming request it can apply the appropriate small adapter weights to handle a specific domain or updated dataset. The overhead of loading many adapters is negligible (e.g. 30 LoRA adapters could be loaded with only ~3% extra VRAM . This demonstrates an efficient strategy to maintain multiple fine-tuned variations of an LLM (e.g. monthly knowledge updates or client-specific versions) without deploying many full copies of the model. Only the tiny delta weights differ, which can be updated or replaced independently.

Pros: Parameter-efficient tuning dramatically lowers update compute cost and memory requirements. By training only ~1% of the parameters, it “makes fine-tuning a lot cheaper and faster, often requiring only a single GPU. It also tends to preserve the original model’s capabilities, since the base weights aren’t overwritten (reducing forgetting of prior knowledge . Fine-tuning with LoRA on a small fresh dataset can be done frequently or on-demand, enabling quick iterations. At inference, using adapters adds minimal latency – it’s essentially the same forward pass with a slightly augmented model.

Cons: The need to still load the full base model means the inference cost per request is similar to using the original LLM (you don’t escape the big model’s runtime cost, whereas RAG can use a smaller model plus retrieval). If many different LoRA versions are needed simultaneously, serving can become complex (though multi-adapter solutions mitigate this). Additionally, while PEFT methods excel for task adaptation, injecting a large amount of factual knowledge purely via LoRA fine-tuning could still require substantial training data. If the domain shift is huge, multiple adapter layers or higher-rank LoRA might be needed to capture all the new information. In summary, parameter-efficient updates are a practical compromise, retaining most of the model’s knowledge and performance while cheaply incorporating new data.

Implementation in Frameworks

Modern ML frameworks and libraries provide extensive support for these update methods:

• PyTorch & Hugging Face: PyTorch’s flexible autograd makes it straightforward to fine-tune any model – simply load the pre-trained weights and continue training on new data. Hugging Face’s Transformers library abstracts this by providing Trainer and Accelerate utilities for distributed fine-tuning of transformers. For RAG, Hugging Face offers ready-made classes like RagRetriever and RagSequenceForGeneration that combine a question encoder, document index, and generator model, so developers can plug in their own knowledge corpus and use a RAG pipeline out-of-the-box. The LangChain framework (Python) is also commonly used to orchestrate LLM + retriever workflows, with integration for Hugging Face models and various vector databases. For parameter-efficient tuning, the 🤗 PEFT library supports methods like LoRA, prefix tuning, and adapters on top of any Transformer model. For example, one can apply LoRA to a transformers model by just calling peft.get_peft_model(model, lora_config) and then train; saving the LoRA adapter produces a lightweight file (often just a few MBs) that can be merged or loaded with the base model later. Tools like DeepSpeed and bitsandbytes further optimize memory use (e.g. 4-bit quantization as in QLoRA) to enable fine-tuning 30B+ models on a single GPU ( QLoRA: Efficient Finetuning of Quantized LLMs).

• TensorFlow & Keras: While PyTorch currently dominates large-scale LLM fine-tuning, TensorFlow has kept pace. Keras 3 (with KerasNLP) provides high-level interfaces to load large models (like PaLM or GPT-style architectures) and fine-tune or use adapters. The example of Keras Gemma LLM’s LoRA fine-tuning (Parameter-efficient fine-tuning of Gemma with LoRA and QLoRA) shows that only minimal code changes are needed to enable LoRA – under the hood, Keras inserts low-rank trainable weights and freezes the rest. TensorFlow’s ecosystem also offers the TensorFlow Extended (TFX) pipeline for automating tasks like periodic model fine-tuning on new data, which can be useful in production MLops. RAG in TensorFlow can be implemented by using a retrieval component (either in Python or as a TensorFlow Retrieval API if available) to supply context to a text generation model. Although TF doesn’t have a built-in RAG class, one can combine a TF Recommenders or Annoy index for retrieval with a T5 or GPT-2 model for generation.

• Industry Tools: Many companies leverage a combination of these approaches. For instance, some deploy a RAG-based system with a moderate-sized language model to handle knowledge queries, avoiding retraining unless the base model itself needs an upgrade. Others fine-tune models on a schedule – e.g. updating a product Q&A bot’s model monthly with new Q&A pairs via LoRA, and using RAG in between for any out-of-scope queries. Cloud platforms are also offering services: Google’s Vertex AI and AWS Bedrock provide fine-tuning APIs (often using parameter-efficient methods under the hood to reduce customer costs), and managed retrieval services for building RAG pipelines. In open-source, projects like Haystack and LlamaIndex provide end-to-end pipelines for RAG. Meanwhile, tools like Hugging Face Hub allow hosting multiple adapter variants of a model (so different users can fetch domain-specific adapters). The net result is that keeping an LLM updated no longer means re-training a 100B model from scratch; practitioners mix and match these techniques to balance freshness, cost, and performance.

Trade-Off Analysis: Cost, Latency, and Efficiency

Each approach comes with advantages and trade-offs in terms of computational cost, inference latency, and overall efficiency:

• Periodic Fine-Tuning: This method has the highest upfront compute cost. Full fine-tuning of a large LLM (tens of billions of parameters) requires powerful hardware and can take hours or days. Incremental fine-tuning every time new data arrives is often prohibitively expensive (Towards Understanding Systems Trade-offs in Retrieval-Augmented Generation Model Inference). Once fine-tuned, though, the inference latency is just the model’s normal runtime – no extra lookup needed – so answers can be generated as fast as any standalone model. Fine-tuning can achieve high accuracy on the new data it sees, but unsupervised fine-tuning on factual corpora yields only modest gains in knowledge and may still miss many facts (HERE). There’s also the risk of overfitting or forgetting: the model might become very good at the recent data but degrade on older knowledge or unrelated queries (mitigation strategies like mixing data help, at the cost of more complexity). In sum, pure fine-tuning can be accurate if done well, but it’s computationally expensive and difficult to sustain frequently.
  

• Retrieval-Augmented Generation (RAG): RAG largely avoids retraining costs – incorporating new knowledge is as simple as updating the external database or index, which is much cheaper than model training 0. This makes it extremely cost-efficient to keep knowledge current. The trade-off comes at inference time: each query triggers a retrieval, which adds some latency and system overhead. If the knowledge store is local and optimized, this might only add a fraction of a second, but for very large or remote indexes it could be slower. RAG pipelines also consume memory/storage for the index (potentially terabytes for massive corpora) 7. Despite the latency overhead, RAG is often worth it for knowledge-intensive applications – it can leverage far more data than could ever be packed into the model itself, and it can be updated continuously. In terms of accuracy, RAG excels at factual and up-to-date queries (assuming the retrieval is good) 0. The model’s responses are grounded in retrieved evidence, which improves correctness and makes answers verifiable. RAG may be less ideal for queries requiring deep reasoning on long contexts if the retrieval fails to provide all needed pieces (there, very long-context models or fine-tuning might work better). Overall, RAG offers low ongoing cost and high accuracy for dynamic knowledge, at the price of slightly higher latency and system complexity.

• LoRA/Adapter Updates: Parameter-efficient fine-tuning strikes a middle ground. The compute cost to update an LLM with LoRA is much lower than full fine-tuning – often an order of magnitude less data to train and parameters to adjust (Rank Also Matters: Hierarchical Configuration for Mixture of Adapter Experts in LLM Fine-Tuning). Training can be done on modest hardware (one high-end GPU) in a short time, even for large models, especially with optimizations like QLoRA (quantization) that *“reduce memory usage enough to finetune a 65B model on a single 48GB GPU ( QLoRA: Efficient Finetuning of Quantized LLMs). This makes it feasible to update the model more frequently (e.g. fine-tune a weekly news adapter). The inference latency using an updated LoRA model is nearly the same as the original model’s latency – just a bit of extra matrix multiplication for the adapter, which is negligible. There’s no external call or search, so responses are fast and self-contained. The efficiency in terms of cost-vs-accuracy is quite high: LoRA updates can achieve performance close to full fine-tuning (TGI Multi-LoRA: Deploy Once, Serve 30 Models), especially for targeted tasks or incremental knowledge additions, while using a fraction of the compute and allowing the base model’s general abilities to survive 2. However, unlike RAG, LoRA does not inherently provide real-time knowledge updates – you still need to retrain (even if cheaply) to add new information. If the knowledge to add is constantly changing (e.g. live sports scores), RAG might be preferable; but if updates are periodic and can be scheduled (e.g. a nightly fine-tune on the day’s data), LoRA is a very efficient solution. One can also combine approaches: for instance, use a base model that was pre-trained and occasionally fine-tuned with LoRA on core knowledge, and use retrieval augmentation for any truly on-the-fly information needs.

In summary, there is no one-size-fits-all solution – it’s about balancing freshness, cost, and complexity. Fine-tuning (full or via adapters) bakes in the knowledge, giving a self-sufficient model that answers quickly but requires investment to update. RAG leaves the model static but augments it with a flexible, updatable memory, trading a bit of speed for a big gain in knowledge currency and coverage. Recent research and industry practice often favor RAG for continuously changing information due to its low computational cost and strong factual accuracy (HERE), while using LoRA and adapters for efficient periodic updates that keep models competent on specific datasets or tasks with minimal overhead 2. Many production systems layer these techniques – e.g. a base model fine-tuned with LoRA on domain data, plus RAG for any query beyond the scope of that data – to get the best of both worlds.

Going forward, we expect continued advances in hybrid approaches: techniques to quickly edit model weights for new facts (model editing), smarter retrieval that works with the model’s internal knowledge, and improved PEFT methods that further reduce training cost. The 2024–2025 literature shows active exploration of all these, moving us closer to LLMs that can learn incrementally as the world changes, without the massive expense of re-training from scratch. The choice of method will depend on the specific application requirements, but understanding these trade-offs helps in designing an update strategy that keeps an LLM both up-to-date and efficient.