| Area | Representative Works | Key Takeaways | |------|----------------------|---------------| | | Houlsby et al. [7]; Pfeiffer et al. [8] | Small bottleneck modules enable efficient domain adaptation without updating the backbone. | | Meta‑Learning for NLP | Li et al. [9]; Vu et al. [10] | Model‑agnostic meta‑learning (MAML) and reinforcement‑learning curricula accelerate few‑shot learning. | | Efficient Inference | Shazeer et al. [11] (Switch Transformers); Liu et al. [12] (DistilBERT) | Sparsely‑activated experts and knowledge distillation reduce latency. | | Interpretability | Jain & Ng [13]; Vig et al. [14] | Probing and attribution methods expose hidden representations. | | Multidomain LLMs | Liu et al. [15] (UnifiedQA); Karpukhin et al. [16] (RAG) | Unified models can handle heterogeneous tasks but often require massive fine‑tuning. |
To address these challenges, we propose , a novel adaptive‑learning framework that augments a frozen base transformer with lightweight adapters and a meta‑learning controller that dynamically selects and configures adapters at inference time. The name “Ellie Nova” evokes the idea of a new star (nova) that illuminates every corner of the linguistic sky while remaining compact (elliptical) enough to be deployed everywhere. ellie nova new
| Domain | Baseline (Full FT) | Ellie Nova | Δ Performance | Δ Data Req. | Δ Latency | |--------|-------------------|-----------|---------------|-------------|-----------| | PubMedQA | 84.2 % | | +1.4 pp | –23 % | –14 % | | NER‑Bio | 78.5 % | 80.1 % | +1.6 pp | –21 % | –16 % | | ContractNLI | 81.9 % | 82.7 % | +0.8 pp | –24 % | –15 % | | CaseHOLD | 74.3 % | 75.9 % | +1.6 pp | –22 % | –13 % | | Swahili‑NLI | 68.4 % | 70.2 % | +1.8 pp | –25 % | –17 % | | Yoruba‑POS | 71.0 % | 72.5 % | +1.5 pp | –23 % | –14 % | | Average | – | – | +1.8 pp | –23 % | –15 % | | Area | Representative Works | Key Takeaways
We employ Proximal Policy Optimisation (PPO) [17] with a curriculum that gradually introduces more challenging domains. | | Meta‑Learning for NLP | Li et al
