Recent large language models support inputs of up to 10 million tokens, yet they perform poorly on long-context tasks that require complex reasoning.Such tasks can be solved using only a subset of the input -a proxy contextrather than the full sequence.Despite sharing the same underlying reasoning process, models exhibit a significant performance disparity between proxy and full contexts.To improve long-context reasoning, we propose ProxyCoT, a novel training framework that transfers reasoning capabilities from short proxy contexts to full long contexts.Specifically, we first obtain high-quality chain-of-thought reasoning traces on proxy contexts through reinforcement learning or distillation from a larger teacher model, and then ground the generated traces in full long contexts with supervised fine-tuning.Experiments across different datasets demonstrate that ProxyCoT consistently outperforms strong baselines with reduced computational overhead.Furthermore, models trained with ProxyCoT generalize their long-context reasoning capabilities to out-of-domain tasks. 1

Text-to-SQL semantic parsing has made significant progress in recent years, with various models demonstrating impressive performance on the challenging Spider benchmark.However, it has also been shown that these models often struggle to generalize even when faced with small perturbations of previously (accurately) parsed expressions.This is mainly due to the linguistic form of questions in Spider which are overly specific, unnatural, and display limited variation.In this work, we use data augmentation to enhance the robustness of text-to-SQL parsers against natural language variations.Existing approaches generate question reformulations either via models trained on Spider or only introduce local changes.In contrast, we leverage the capabilities of large language models to generate more realistic and diverse questions.Using only a few prompts, we achieve a two-fold increase in the number of questions in Spider.Training on this augmented dataset yields substantial improvements on a range of evaluation sets, including robustness benchmarks and out-of-domain data. 1

Practical semantic parsers are expected to understand user utterances and map them to executable programs, even when these are ambiguous. We introduce a new benchmark, AMBROSIA, which we hope will inform and inspire the development of text-to-SQL parsers capable of recognizing and interpreting ambiguous requests. Our dataset contains questions showcasing three different types of ambiguity (scope ambiguity, attachment ambiguity, and vagueness), their interpretations, and corresponding SQL queries. In each case, the ambiguity persists even when the database context is provided. This is achieved through a novel approach that involves controlled generation of databases from scratch. We benchmark various LLMs on AMBROSIA, revealing that even the most advanced models struggle to identify and interpret ambiguity in questions.

To translate natural language questions into executable database queries, most approaches rely on a fully annotated training set.Annotating a large dataset with queries is difficult as it requires query-language expertise.We reduce this burden using grounded in databases intermediate question representations.These representations are simpler to collect and were originally crowdsourced within the Break dataset (Wolfson et al., 2020).Our pipeline consists of two parts: a neural semantic parser that converts natural language questions into the intermediate representations and a non-trainable transpiler to the SPARQL query language (a standard language for accessing knowledge graphs and semantic web).We chose SPARQL because its queries are structurally closer to our intermediate representations (compared to SQL).We observe that the execution accuracy of queries constructed by our model on the challenging Spider dataset is comparable with the state-of-the-art text-to-SQL methods trained with annotated SQL queries.Our code and data are publicly available.1