Matching users to interest categories at scale is central to personalized shopping, but the task is challenging in large e-commerce platforms, where label spaces continually evolve and user-interest signals are sparse and long-tailed. Autoregressive language models are appealing because their world knowledge and semantic priors over descriptors generalize across extreme label spaces and accommodate multiple valid label assignments. Yet under teacher-forced fine-tuning, inference-time predictions become part of the conditioning context: early errors steer later outputs toward co-occurring labels, over-generating near-correlates and missing unrelated true interests. We present MERIT, a framework for user-interest propensity modeling that mitigates this exposure bias through a self-correction objective. A permutation-invariant multi-target loss over shuffled mixtures of gold and mined hard-negative labels exposes the generator to erroneous prefixes while preserving the efficiency of teacher-forced training. This training objective concentrates supervision at classification positions, yielding propensity-aligned hidden states powering a lightweight scorer for bidirectional retrieval (interests for users and users for interests). On a proprietary e-commerce dataset with 250k+ interest categories, MERIT improves global recall by at least 11.9% and average Hit@k by 6.1%. In production A/B tests, it achieves +0.26% gain in user conversion.

Text-to-SQL simplifies database interactions by enabling non-experts to convert their natural language (NL) questions to Structured Query Language (SQL) queries. With advancements in Large Language Models (LLM), in-context learning (ICL) has emerged as a popular choice for building Text-to-SQL systems. Real world, industry-scale databases, often comprise thousands of tables and hundreds of columns, and makes passing the entire schema as context to an LLM infeasibly expensive. This requisites access to the correct database and the set of tables. Recently Retrieval Augmented Generation (RAG) based methods have been proposed for retrieving relevant subset of databases and tables for a given query. However, we observe that the existing methods of synthetic query generation can generate predominantly simple queries which might not be sufficiently representative of complex, real world queries, thus, negatively affecting the quality of the generated SQL. To address this, we propose an innovative in-context reinforcement learning (ICRL) based framework which refines the question generation process by enhancing the model’s ability to produce intricate queries that practitioners may pose during inference. In contrast to the existing approaches, our framework ensures the generation of synthetic SQL queries which are diverse and complex. We demonstrate the effectiveness of our approach via multiple experiments comparing against the representative state-of-the-art models on public benchmark datasets and observe substantial improvements in performance and scalability. Our method achieves 15-20% higher recall in database/table retrieval task compared to the existing state-of-the-art models for schema identification and upto 2% higher execution accuracy for SQL generation.