Active Learning (AL) has been a powerful paradigm for improving model efficiency and performance by selecting the most informative data points for labeling and training. In recent active learning frameworks, Large Language Models (LLMs) have been employed not only for selection but also for generating entirely new data instances and providing more cost-effective annotations. Motivated by the increasing importance of high-quality data and efficient model training in the era of LLMs, we present a comprehensive survey on LLM-based Active Learning. We introduce an intuitive taxonomy that categorizes these techniques and discuss the transformative roles LLMs can play in the active learning loop. We further examine the impact of AL on LLM learning paradigms and its applications across various domains. Finally, we identify open challenges and propose future research directions. This survey aims to serve as an up-to-date resource for researchers and practitioners seeking to gain an intuitive understanding of LLM-based AL techniques and deploy them to new applications.

Natural language interfaces (NLIs) democratize data analytics by enabling non-technical users to query relational databases via Text-to-SQL systems. While large language models (LLMs) have achieved state-of-the-art accuracy on benchmarks like Spider and BIRD, two critical challenges persist for real-time deployment: (1) inference latency due to sequential autoregressive decoding (e.g., average inference latency on BIRD (Minidev) is 14.3 seconds per query for Qwen2.5-Coder32B and 22.86 seconds for Llama-70B.), and (2) schema hallucinations (e.g., invalid column references like customer_ids instead of cust_id). (2) schema hallucinations (e.g., Qwen2.5-Coder-32B Instruct generated ... COUNT(users.UserId) ... = users.Id ..., using users.Id correctly in JOIN but hallucinating users.UserId in COUNT). To address these, we propose Tree-Guided Token Decoding (TTD-SQL), a lightweight framework that integrates SQL grammar and database schema constraints into the decoding process without modifying the underlying LLM. TTD precomputes token-level decision trees over SQL keywords, table names, and column identifiers, enabling deterministic “auto-fill” transitions for uniquely determined tokens (e.g., “Song_” → “ID”) while retaining flexibility for unconstrained reasoning. Across five LLMs (CodeLlama, Phi-4, Qwen2.5, Granite, Llama70B), TTD achieves up to 19.96% token-rate speedups by eliminating redundant forward passes (e.g., CodeLlama: 8.97→10.76 tokens/s on Spider) and reduces schema hallucinations by +17.7% in executable-SQL rates (e.g., CodeLlama on BIRD). By bridging rigid parser based methods and flexible LLM generation, TTD offers a practical path toward reliable, high-performance SQL generation in both public benchmarks and enterprise settings.

Auto-regressive Large Language Models (LLMs) demonstrate remarkable performance across different domains such as vision and language tasks. However, due to sequential processing through multiple transformer layers, autoregressive decoding faces significant computational challenges, particularly in resource-constrained environments like mobile and edge devices. Existing approaches in literature that aim to improve latency via skipping layers have two distinct flavors: (1) early exit, and (2) input-agnostic heuristics where tokens exit at pre-determined layers irrespective of input sequence. Both the above strategies have limitations, the former cannot be applied in the presence of KV caching, which is essential for speed-ups in modern inference frameworks, and the latter fails to capture variation in layer importance across tasks or, more generally, across input sequences. To address these limitations, we propose FiRST, a model-agnostic framework that reduces inference latency by using layer-specific routers to adaptively skip transformer layers during decoding, based on routing decisions made from the input prompt in the prefill stage. FiRST remains fully compatible with KV caching, enabling faster decoding while maintaining quality. Our method reveals that input adaptivity is essential: Different tasks rely on different subsets of layers to evolve meaningful representations. Extensive experiments show that FiRST significantly reduces latency while outperforming existing layer selection strategies in quality. It retains performance comparable to the base model without skipping. FiRST is thus a promising and efficient solution for LLM deployment in low-resource environments.

Large Language Models (LLMs) have recently demonstrated impressive few-shot learning capabilities through in-context learning (ICL). However, ICL performance is highly dependent on the choice of few-shot demonstrations, making the selection of the most optimal examples a persistent research challenge. This issue is further amplified in low-resource Indic languages, where the scarcity of ground-truth data complicates the selection process. In this work, we propose PromptRefine, a novel Alternating Minimization approach for example selection that improves ICL performance on low-resource Indic languages. PromptRefine leverages auxiliary example banks from related high-resource Indic languages and employs multi-task learning techniques to align language-specific retrievers, enabling effective cross-language retrieval. Additionally, we incorporate diversity in the selected examples to enhance generalization and reduce bias. Through comprehensive evaluations on four text generation tasks—Cross-Lingual Question Answering, Multilingual Question Answering, Machine Translation, and Cross-Lingual Summarization using state-of-the-art LLMs such as LLAMA-3.1-8B, LLAMA-2-7B, Qwen-2-7B, and Qwen-2.5-7B, we demonstrate that PromptRefine significantly outperforms existing frameworks for retrieving examples.