I'm a machine learning engineer working on a project to fine-tune a large language model for a specific domain-specific question-answering task. My team has access to substantial computational resources, and we're debating which underlying transformer architecture to use as our base. We're considering the trade-offs between encoder-only models like BERT, which seem great for understanding, versus decoder-only models like GPT, which excel at generation, or encoder-decoder models like T5. For a task that requires both deep comprehension of technical documents and generating concise, accurate answers, which family of transformer architectures has proven most effective in your experience? I'm particularly interested in real-world pitfalls, like the handling of long-context inputs or the fine-tuning stability of these different designs.
Two quick takeaways: for a task that needs both deep comprehension and generation, encoder-decoder models or a retrieval-augmented setup tend to perform best. If you can only pick one family to start with, go with an encoder-decoder like T5/BART and consider a retrieval step to fetch relevant documents. For really long inputs, use a long-context variant (Longformer/BigBird) or chunking with a separate retriever so you don’t lose important context.
From experience, an RAG-style pipeline with a dense retriever and a generator works well for domain-specific QA. We used a T5-based encoder-decoder, fed it document chunks (roughly 4–5k tokens) and tuned with adapters instead of full fine-tuning. It stabilized training, and the produced answers were solid, though you still need lightweight fact-checking post-generation.
Pitfalls I’ve seen: (1) hallucinations and misalignment between the training domain and your data; (2) retrieval quality setting off the whole answer; (3) long-context memory and latency constraints; (4) fine-tuning stability with very large models. Plan for evaluation that includes factual checks and a retriever with a ranking step to surface the right docs.
Long-context options to consider: Longformer, BigBird, or other sparse-attention variants can handle tens of thousands of tokens with the right config. In many pipelines, a practical approach is to keep a robust retriever and chunk documents into 8k–16k total context, then aggregate across chunks with a crossover cross-attention or a reranker.
Fine-tuning tricks that help in production: use adapters (LoRA/prefix-tuning) to avoid full fine-tuning, freeze most layers, and use a low LR with warmup. Enable gradient checkpointing and mixed precision to fit memory. Monitor for drift and run regular calibration with a held-out validation set that reflects your domain.
If you’re game, tell me a bit about your domain, data size, latency targets, and whether you want offline or online inference, and I’ll sketch a concrete baseline pipeline (architecture, data flow, and a rough training plan).