United States

Tabular anomaly detection is challenging because real-world tables contain heterogeneous columns, ranging from structured identifiers to free-form text. Existing methods face a fundamental trilemma: rule-based systems require extensive manual configuration and fail on novel schemas; statistical methods scale efficiently but miss semantic errors; and LLM-based approaches understand semantics but incur prohibitive per-cell inference costs. No prior method simultaneously addresses semantic heterogeneity, domain-specific validation rules, and enterprise-scale processing.

We introduce TabGuard, an agentic framework that resolves this trilemma through semantic routing. Using LLM function calling, the system analyzes a small sample of each column and dynamically selects the most effective validation strategy, routing to a regex-based validator for syntactic patterns, a code-generation validator for domain-specific rules (such as Luhn checksums for credit cards), or an embedding-based validator for distributional outliers. This architecture decouples expensive cognitive reasoning ($O(m)$ LLM calls for $m$ columns) from scalable programmatic execution, enabling deployment on enterprise datasets without per-cell inference.

ACL 2026

TabGuard: Agentic LLM Orchestration for Adaptive Tabular Anomaly Detection via Dynamic Validator Selection and Generation

spider beaver dataset

fetaqa

tabard benchmark

interpretable ai systems

human-in-the-loop anomaly detection

scalable data validation

o(m) complexity

sampling-based inference

two-sigma rule

cosine similarity thresholding

centroid-based outlier detection

modular pipeline

minilm embeddings

deterministic execution

validator selection

farthest point sampling

embedding-based anomaly detection

regex synthesis

llm function calling

column-wise validation

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.

It is exciting to see the many developments in the field reflected at our conference, such as the advancement of large language models (born out of our community) into many application directions. This is not without challenges, partly due to unsolved problems, and partly due to misuse of the technology. All this is reflected well at ACL 2026. The conference also reflects that the field is not only about advancing technology, but also the science of fundamentally understanding language and how it is use by many different speaker communities. <br>
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The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) will take place in San Diego, California, from July 2 - 7, 2026.

workshop paper

Large language models have shown impressive capabilities in code generation, yet they often produce functionally incorrect code. Uncertainty quantification (UQ) methods have emerged as a promising approach for detecting hallucinations in natural language generation, but their effectiveness for code generation tasks remains underexplored. We systematically evaluate how UQ techniques transfer to code generation across three programming languages, five LLMs, and over 1,700 problems. We find that some token-probability-based methods generalize effectively without modification, while sampling-based methods relying on natural language inference (NLI) fail because NLI models cannot distinguish functionally different code, causing most responses to collapse into a single semantic cluster. To address this, we introduce \emph{functional equivalence methods}, a family of code-specific methods that replace NLI-based semantic equivalence with an LLM-based functional equivalence assessment, including functional entropy, a code-specific analog of semantic entropy. Functional equivalence methods achieve top AUROC in 11 out of 15 model-benchmark combinations and the best calibration across most settings, consistently outperforming both NLI-based counterparts and all other methods evaluated.

Functional Entropy: Predicting Functional Correctness in LLM-Generated Code with Uncertainty Quantification

The rapid growth of scientific publications makes it increasingly difficult to track and synthesize research progress. While Large Language Models (LLMs) can support automated survey generation, existing methods retrieve unstructured data and require models to infer conceptual, methodological, and taxonomic relations from raw text at generation time. We introduce STRUCTSURVEY, a hierarchical multiagent framework that shifts structural reasoning from generation to retrieval by dynamically constructing graph-based representations of entities, relations, and topical taxonomies. We evaluate STRUCTSURVEY on a new referencegrounded benchmark of ACL survey papers for reproducible long-form scientific summarization. Compared with embedding-only retrieval baselines, STRUCTSURVEY improves ROUGE1 recall by +2.9 and ROUGE-2 recall by +1.0 on average, without reducing precision. It also improves LLM-as-a-Judge ratings for logical structure, depth, and synthesis, showing that explicit structural retrieval yields surveys closer to human-written organization and reasoning.

StructSurvey: Structured Agentic Retrieval for Automated Survey Paper Generation

In this work, we explore the Large Language Model (LLM) agent reviewer dynamics in an Elo-ranked review system using real-world conference paper submissions. Multiple LLM agent reviewers with different personas engage in multi round review interactions moderated by an Area Chair. We compare a baseline setting with conditions that incorporate Elo ratings and reviewer memory. Our simulation results showcase several interesting findings, including how incorporating Elo improves Area Chair decision accuracy, as well as reviewers' adaptive review strategies that exploits our Elo system without improving review effort. These findings show how the Elo system affects peer review and offer insights for improving AI conference evaluation. Our code is available at https://github.com/hsiangwei0903/EloReview.

Modeling LLM Agent Reviewer Dynamics in Elo-Ranked Review System

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.

Asking language models how to represent data for fine-tuning

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.

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