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Language models are often used for tasks involving structured data like tables and graphs, but there is no principled approach for choosing the best format to represent such data for fine-tuning. We address this in three steps. First, we show that format choice remains important even after fine-tuning; models learn more efficiently with specific formats rather than adapting to any format. Second, we show that a pre-trained model can suggest its own candidate formats by auto-completing partial prompts, reducing reliance on developer intuition. Third, and most importantly, we demonstrate that base model performance across formats reliably predicts post-fine-tuning performance: the format that performs best before fine-tuning remains among the top candidates after fine-tuning in 16 out of 18 settings across three data structure types, three models, and six tasks. This finding allows format selection to be done via inference alone, avoiding costly trial-and-error fine-tuning runs.

ACL 2026

Asking language models how to represent data for fine-tuning

### Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

Welcome to the 2025 meeting of the Nations of the Americas Chapter of the Association for Computational Linguistics! I am proud to help organize the first NAACL conference to carry the new name of our organization, one that emphasizes inclusion for all of the Americas. I am also pleased to welcome you to Albuquerque, New Mexico, a state whose unique blend of cultural influences will make for an excellent backdrop for NAACL 2025, especially with this year’s special theme on NLP in a Multicultural World. 
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NAACL 2025

Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

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It is my great pleasure and honor to welcome you to the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), held in San Diego, California, USA, July 2–7, 2026. ACL 2026 continues our fields tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.

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Table reasoning remains challenging for Large Language Models (LLMs) as it requires integrating structured tabular information with natural language questions. Previous SQL-based approaches rely on surface-level alignment between question keywords and column headers, often generating queries with spurious or missing column mappings. 
We introduce TabBridge, a framework that incorporates both structural and contextual information for accurate table reasoning. TabBridge first generates a unified textual representation called Table Specification (TabSpec), preserving the structural information through row and column analysis. In order to ensure accuracy and consistency, we also employ a reconstruction-based evaluation mechanism to verify and refine the generated TabSpec. TabSpec is subsequently used to generate SQL aligned with the contextual intent of the question, enabling accurate interpretation of column semantics that are often overlooked by previous approaches.
Across three public benchmarks, TabBridge shows consistent improvements over previous SQL-based methods, achieving 73.94\% accuracy on WikiTableQuestions (+5.3 pp over the previous state of the art). TabBridge also demonstrates robust performance across diverse LLM backbones, confirming its generalizability across model architectures. Our code is available at https://github.com/raylee0519/TabBridge.

TabBridge: Bridging Structure and Context for Accurate Table Reasoning

We address the task of routing natural language queries in multi-database enterprise environments. We construct realistic benchmarks by extending existing NL-to-SQL datasets. Our study shows that routing becomes increasingly challenging with larger, domain-overlapping DB repositories and ambiguous queries, motivating the need for more structured and robust reasoning-based solutions. By explicitly modelling schema coverage, structural connectivity, and fine-grained semantic alignment, the proposed modular, reasoning-driven re-ranking strategy consistently outperforms embedding-only and direct LLM-prompting baselines across all the metrics.

Routing End User Queries to Enterprise Databases

The ability to understand and reason over tabular data is critical for the enablement of Large Language Models (LLMs) in enterprise settings, where domain-specific information is often stored in relational databases. While LLMs have made progress in understanding tabular data in recent years, they still struggle to retrieve the right evidence, reason across multiple tables, and find correct answers. A promising direction for improving LLMs in this setting is training models on multi-table question-answer (Q&A) pairs with associated reasoning traces. However, there is a lack of existing resources with reasoning traces annotated for multi-table Q&A. In this work, we address this gap by generating synthetic reasoning traces for multi-table Q&A and show that they lead to better performance than using Q&A pairs alone. Through analyses and ablations, we show that performance can be further improved by adding contrastive negative traces and training with contrastive preference optimization.

Synthetic Contrastive Reasoning for Multi-Table Q&A

Improving the reliability of large language models (LLMs) at inference time is a central challenge in structured reasoning tasks such as Text-to-SQL. Common test-time inference strategies, including Best-of-$N$ sampling and Majority Voting, rely on heuristic signals such as execution success or output frequency, which provide limited semantic discrimination across candidate outputs.
In this work, we study Outcome Reward Models (ORMs) as learned semantic scoring functions for test-time verification in Text-to-SQL. While ORMs have been previously explored for test-time scaling and alignment, their application to structured query generation remains underexplored. We introduce GradeSQL, a scalable framework for training task-specific ORMs via automated candidate generation and execution-based labeling, enabling verifier training without manual annotation.
We integrate ORMs into a verification-driven Best-of-$N$ pipeline and evaluate our approach on the BIRD and Spider benchmarks across multiple open-source LLM families. ORM-based selection consistently outperforms execution-based Best-of-$N$ and Majority Voting, with gains of up to +4.33% on BIRD and +2.10% on Spider. We further show that ORMs scale effectively with larger candidate sets and yield stronger improvements on complex queries.
Overall, our results demonstrate that ORM-based verification provides a simple, effective, and scalable alternative to heuristic test-time selection strategies for Text-to-SQL. Code, datasets, and models are publicly available.

Test-Time Verification for Text-to-SQL via Outcome Reward Models

For large language models (LLMs), reasoning over graphs can help solve many problems. Prior work has tried to improve LLM graph reasoning through different training methods, but the merits of such approaches remain unclear and the limitations of each approach with respect to generalizability of reasoning are often not thoroughly explored. In this paper we systematically compare the ability of LLMs to learn fundamental graph tasks across a variety of training methods and their ability to generalize out of distribution across various dimensions. We highlight key tradeoffs between training methods, e.g., training specialized graph encoders and fusing their embeddings with LLMs consistently collapses in terms of generalizability; however, no single method shows clear superiority across all dimensions of generalizability, regardless of the size of the model.

Generalization in Graph Reasoning: A Systematic Comparison of LLM Training Approaches

Self-correction—the ability of LLMs to detect and fix their own errors—has been studied extensively for mathematical and code reasoning, with limited prior work on table reasoning (primarily multi-agent pipelines such as Table-Critic, ACL 2025, rather than single-model structured prompting). Tables present unique challenges: errors arise from wrong cell retrieval, incorrect computation, flawed logic, and hallucination of values not present in the data. We conduct the first cross-provider single-model self-correction analysis for table reasoning across five providers (Google, Moonshot AI, Zhipu, Alibaba, MiniMax), testing five models (Gemini 3.1 Pro, Kimi K2.5, GLM 5, Qwen 3.5+, MiniMax M2.5) on WikiTableQuestions and TabFact with a multi-seed paired protocol. We propose Structured Self-Correction (SSC), a table-specific verification chain that guides models through cell verification, computation checking, logic validation, and completeness assessment. We confirm that the Accuracy-Correction Paradox (terminology from Li 2025) previously observed in math extends to tables: models with base accuracy in the mid-60s–mid-70s region benefit modestly from self-correction (multi-seed mean SCG up to +1.3% with within-seed point estimates as high as +3.4%), while stronger models above this region are systematically harmed by over-correction (multi-seed mean SCG down to -1.3%, with 95% bootstrap CIs significantly below zero). SSC reduces over-correction rates in 9 of 10 conditions, with reductions of 38–69% on TabFact. An inference-mode-controlled probe shows that SSC's qualitative direction is robust for Qwen 3.5+ across reasoning-ON and reasoning-OFF settings, while GLM 5 exhibits a substantial mode-dependent shift, indicating that mode robustness itself is model-dependent. Stronger baselines (self-consistency, self-critic, tool-augmented arithmetic verification, majority voting, and a same-family scaling probe) further characterize where SSC helps. Ablation studies reveal that answer-aware review is essential, reasoning traces aid error detection, and iterative correction shows diminishing returns. A FinQA domain transfer probe confirms a capability floor: self-correction fails when base task competence is very low (21.5% accuracy). Our primary contribution is empirical: we characterize the conditions under which self-correction helps or harms table reasoning, providing actionable guidance for practitioners.

Can LLMs Self-Correct Table Reasoning Errors?

Recent advances in text-to-SQL have shown that methods such as Group Relative Policy Optimization (GRPO) can substantially improve reasoning performance, but these approaches remain inherently on-policy, limiting their ability to incorporate novel reasoning patterns. In this work, we address this limitation by leveraging existing datasets to generate high-quality off-policy rollouts, enabling mixed-policy training that exposes models to diverse and informative reasoning trajectories. We present the first application of mixed-policy GRPO to the text-to-SQL domain and introduce a systematic study of off-policy data generation strategies for this setting, including a novel method, Iterative Error Correction (IEC), which iteratively refines model outputs through targeted feedback. Our experiments show that mixed-policy GRPO outperforms both base models and on-policy GRPO, yielding average improvements of +4.7% over base models and +4.1% over on-policy GRPO across the Spider and BIRD benchmarks. Gains are particularly strong on BIRD, reaching up to +7.3% over base models and +4.5% over on-policy GRPO.

Mixed-Policy GRPO for Text-to-SQL with Off-Policy Data Generation

Large language models (LLMs) are increasingly evaluated on structured data tasks—table question answering, chart comprehension, graph reasoning, and time series analysis—yet these benchmarks operate in isolation. We ask: does competence in one structured data format predict competence in another when the underlying data and questions are identical? We construct a controlled benchmark of 1,724 programmatically generated questions over 250 sub-tables drawn from 10 datasets spanning 7 domains. Each sub-table is rendered in five formats: Markdown table, chart image, chart text-description, entity-relationship graph, and unlabeled time series. We evaluate six frontier March 2026 LLMs and measure cross-format transfer via tetrachoric correlation on binary (correct/incorrect) outcomes. Our key findings: (1) structured data comprehension skills transfer strongly across text-based formats, with mean tetrachoric r=0.84 between table, graph, and time series representations (r=0.85 overall; r=0.84 for non-ceiling models, confirming the result is not an artifact of high accuracy); (2) chart text-descriptions show consistently lower transfer (r=0.65) despite being length-matched to tables; (3) chart image comprehension is largely siloed, with a 20-50% accuracy gap compared to equivalent text descriptions; and (4) enabling chain-of-thought reasoning improves accuracy by 24-38% for DeepSeek V3.2 but only 0-0.5% for Gemini 3.1 Pro, suggesting reasoning benefits may vary substantially across models. On hard questions (multi-hop and conditional aggregation), transfer remains substantial at r=0.78.

Do Structured Data Comprehension Skills Transfer Across Representation Types? A Systematic Study with Frontier LLMs

Deployed LLM agents rely on textual policies that must evolve from operational experience, yet over long horizons flat-text representations make verification increasingly difficult to sustain as policies grow. We propose Graph-Regularized Agentic Context Evolution (GRACE), which represents policy content as a typed semantic graph and performs scoped structural validation in the local neighborhood of modified nodes, rather than through document-wide consistency analysis. We evaluate GRACE within a fixed operational harness on the telecom domain of $\tau^2$-bench under a controlled distribution-shift protocol. GRACE improves strict reliability (pass^3) from 0.227 to 0.833 and is the only method that sustains improvement over the full trajectory. The three baselines each exhibit a different failure mode, revealing that contradiction avoidance alone is insufficient. Our results suggest that sustained policy evolution may require both a structural substrate for scoped verification and an active mechanism that consolidates accumulated knowledge.

Graph-Regularized Agentic Context Evolution

Large Language Models (LLMs) are increasingly used to generate structured outputs—JSON objects, SQL queries, and structured records—from formal schemas. While recent advances in constrained decoding and schema-aware prompting have improved syntactic compliance, the semantic reliability of these outputs remains poorly characterized. We investigate this gap through the lens of schema drift—the inevitable evolution of database schemas in production environments through column renamings, type changes, and constraint modifications.

We introduce StructHallu-Drift, a benchmark and evaluation framework for studying structured hallucinations under schema evolution. We contribute: (1) a six-category hallucination taxonomy that disentangles syntactic validity from semantic fidelity; (2) a controlled evaluation suite applying realistic schema mutations at three severity levels to established NL-to-structure datasets; and (3) a systematic evaluation of four LLMs spanning 7B to 70B parameters across three structured output tasks.

Experiments on 1,200 schema–model evaluation instances reveal four key findings: (i) 39–54% of structured outputs contain at least one semantic hallucination; (ii) schema drift severity has surprisingly minimal effect on hallucination rates (∼44% across all levels, p = 0.59), suggesting imperfect schema conditioning under our prompting setup; (iii) output format is the dominant factor in generation reliability, with SQL achieving ∼85% semantic validity while schema-grounded record generation drops to 7–24%; (iv) each model exhibits a distinct hallucination fingerprint, implying that mitigation strategies must be model-specific rather than universal. We publicly release our benchmark and evaluation toolkit.

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TabBridge: Bridging Structure and Context for Accurate Table Reasoning

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LLMs Are Biased Towards Output Formats! Systematically Evaluating and Mitigating Output Format Bias of LLMs

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