United States

Recent advances in large language models (LLMs) have significantly enhanced automated legal reasoning. Yet, it remains unclear whether their performance reflects genuine legal reasoning ability or artifacts of data contamination. We present a comprehensive empirical study of tax law reasoning approaches and implement a contamination detection protocol to rigorously assess LLM reliability. We show that performance can be inflated by contamination. Building on this analysis, we conduct a systematic evaluation, comparing monolithic LLMs with hybrid systems that translate statutory text into formal representations and delegate inference to symbolic solvers. We build a novel test suite designed to probe generalization to unseen documents via case and rule variations. Our findings indicate that legal reasoning is inherently compositional and that neuro-symbolic frameworks offer a more reliable and robust foundation for legal AI, as well as improved generalization to unobserved situations.

ACL 2026

Reasoners or Translators? Contamination-aware Evaluation and Neuro-Symbolic Robustness on Tax Law

tax law

legal reasoning

neurosymbolic

llms

evaluation

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ACL 2024

The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. More information will be announced soon.

workshop paper

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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The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) will take place in San Diego, California, from July 2 - 7, 2026.

Dense retrievers powered by pretrained embeddings are widely used for document retrieval but struggle in specialized domains due to the mismatches between the training and target domain distributions. Domain adaptation typically requires costly annotation and retraining of query-document pairs. In this work, we revisit an overlooked alternative: applying PCA to domain embeddings to derive lower-dimensional representations that preserve domain-relevant features while discarding non-discriminative components. Though traditionally used for efficiency, we demonstrate that this simple embedding compression can effectively improve retrieval performance. Evaluated across 9 retrievers and 14 MTEB datasets, PCA applied solely to query embeddings improves NDCG@10 in 75.4\% of model-dataset pairs, offering a simple and lightweight method for domain adaptation.

More Than Efficiency: Embedding Compression Improves Domain Adaptation in Dense Retrieval

Recent advances in large language models (LLMs) have greatly improved Text-to-SQL performance for single-table queries. But, it remains challenging in multi-table databases due to complex schema and relational operations. Existing methods often struggle with retrieving the right tables and columns, generating accurate JOINs and UNIONs, and generalizing across diverse schemas. To address these issues, we introduce UNJOIN, a two-stage framework that decouples the retrieval of schema elements from SQL logic generation. 
In the first stage, we merge the column names of all tables in the database into a single-table representation by prefixing each column with its table name. This allows the model to focus purely on accurate retrieval without being distracted by the need to write complex SQL logic. In the second stage, the SQL query is generated on this simplified schema and mapped back to the original schema by reconstructing JOINs, UNIONs, and relational logic. Evaluations on SPIDER and BIRD datasets show that UNJOIN matches or exceeds the state-of-the-art baselines. UNJOIN uses only schema information, which does not require data access or fine-tuning, making it scalable and adaptable across databases. Our code is available at: https://github.com/coral-lab-asu/unjoin

UNJOIN: Enhancing Multi-Table Text-to-SQL Generation via Schema Simplification

Large Language Models (LLMs) often underperform in domain adaptation for industrial settings, where available corpora are limited and structurally diverse. These corpora frequently include non-natural formats such as tables, entity lists, or bullet-point instructions that hinder effective learning. To understand and improve domain adaptive pretraining on such data, we introduce SParK-Eval (Structure-aware Parametric Knowledge Evaluation), a framework that constructs question–answer pairs from pretraining data and annotates each with its input structure (e.g., natural sentence, table, list). This enables fine-grained analysis of how input structure affects parametric knowledge acquisition during DAPT. Additionally, we propose a prompt-based input normalization method that converts diverse inputs into coherent natural sentences, providing a reference for isolating structural effects. Our experiments show that LLMs acquire substantially more knowledge from natural sentences than from their structurally non-standard counterparts. These findings underscore the importance of structure-aware evaluation in diagnosing learning challenges and guiding effective domain adaptation strategies.

SParK-Eval: Evaluating Structure-Aware Knowledge Acquisition in LLMs for Domain Adaptation to Industrial Records

We introduce SciTaRC, an expert-authored benchmark for question answering over scientific tables that targets composite, multi-step reasoning. To enable fine-grained diagnostic analysis beyond end-task accuracy, SciTaRC pairs each question with a manually constructed reasoning plan and explicit complexity metrics. State-of-the-art models fail on at least 23\% of these questions, while highly capable open-weight models like Llama-3.3-70B collapse on 65.5\% of the benchmark. Error analysis shows that, in zero-shot settings, failures are driven primarily by question comprehension, where models misinterpret the scientific query and derive the wrong reasoning objective. To determine whether overcoming this gap is sufficient, we use structured plans to decouple strategy formulation from execution. Surprisingly, providing step-by-step oracle plans yields only limited gains and fails to eliminate the performance gap. This reveals a substantial execution bottleneck: both natural language and code-based methods struggle to reliably carry out long-horizon computational chains over structured data. Ultimately, SciTaRC serves as a rigorous diagnostic testbed for studying both planning and execution in scientific table reasoning.

SciTaRC: Benchmarking QA on Scientific Tabular Data that Requires Language Reasoning and Complex Computation

Despite its real-world significance, model performance on tabular data remains underexplored, leaving uncertainty about which model to rely on and which prompt configuration to adopt. To address this gap, we create ToRR, a benchmark for Table Reasoning and Robustness, measuring model performance and robustness on table-related tasks. The benchmark includes 10 datasets that cover different types of table reasoning capabilities across varied domains. ToRR goes beyond model performance rankings, and is designed to reflect whether models can handle tabular data consistently and robustly, across a variety of common table representation formats. We present a leaderboard as well as comprehensive analyses of the results of leading models over ToRR. Our results reveal a striking pattern of brittle model behavior, where even strong models are unable to perform robustly on tabular data tasks. We further find that no single table format consistently yields superior performance. However, evaluating models across multiple formats is essential for a reliable assessment of their capabilities. Moreover, we show that the reliability boost from testing multiple prompts can be equivalent to adding more test examples. Overall, our findings show that reasoning over table tasks remains a significant challenge. The leaderboard, data and code are publicly available.

The Mighty ToRR: A Benchmark for Table Reasoning and Robustness in LLMs

Knowledge Graph-to-Text (G2T) generation involves verbalizing structured knowledge graphs into natural language text. Recent advancements in Pretrained Language Models (PLMs) have improved G2T performance, but their effectiveness relies on datasets with precise graph-text alignment. However, the scarcity of high-quality, general-domain G2T generation datasets restricts progress in the general-domain G2T generation research. To address this issue, we introduce Wikipedia Ontology-Free Graph-text dataset (WikiOFGraph), a new large-scale G2T dataset generated using a novel method that leverages Large Language Models (LLMs) and Data-QuestEval. Our dataset, which contains 5.85M general-domain graph-text pairs, offers high graph-text consistency without reliance on external ontologies. Experimental results demonstrate that PLM fine-tuned on WikiOFGraph outperforms those trained on other datasets across various evaluation metrics. Our method proves to be a scalable and effective solution for generating high-quality G2T data, significantly advancing the field of G2T generation.

Ontology-Free General-Domain Knowledge Graph-to-Text Generation Dataset Synthesis using Large Language Model

We introduce a finance & accounting benchmark (Finch) for evaluating AI agents on real-world, enterprise-grade professional workflows -- interleaving data entry, structuring, formatting, web search, cross-file retrieval, calculation, modeling, validation, translation, visualization, and reporting. Finch is sourced from authentic enterprise workspaces at Enron (15,000 spreadsheets and 500,000 emails from 150 employees) and other financial institutions, preserving in-the-wild messiness across multimodal artifacts (text, tables, formulas, charts, code, and images) and spanning diverse domains such as budgeting, trading, and asset management. We propose a workflow construction process that combines LLM-assisted discovery with expert annotation: (1) LLM-assisted, expert-verified derivation of workflows from real-world email threads and version histories of spreadsheet files, and (2) meticulous expert annotation for workflows, requiring over 700 hours of domain-expert effort. This yields 172 composite workflows with 384 tasks, involving 1,710 spreadsheets with 27 million cells, along with PDFs and other artifacts, capturing the intrinsically messy, long-horizon, knowledge-intensive, and collaborative nature of real-world enterprise work. We conduct both human and automated evaluations of frontier AI systems including GPT 5.1, Claude Sonnet 4.5, Gemini 3 Pro, Grok 4, and Qwen 3 Max, and GPT 5.1 Pro spends 16.8 minutes per workflow yet passes only 38.4% of workflows, while Claude Sonnet 4.5 passes just 25.0%. Comprehensive case studies further surface the challenges that real-world enterprise workflows pose for AI agents.

Finch: Benchmarking Finance & Accounting across Spreadsheet-Centric Enterprise Workflows

In modern industry systems like multi-turn chat agents, Text-to-SQL technology bridges natural language (NL) questions and database (DB) querying. The conversion of tabular DB results into NL representations (NLRs) enables the chat-based interaction. Currently, NLR generation is typically handled by large language models (LLMs), but information loss or errors in presenting tabular results in NL remains largely unexplored. This paper introduces a novel evaluation method - Combo-Eval - for judgment of LLM-generated NLRs that combines the benefits of multiple existing methods, optimizing evaluation fidelity and achieving a significant reduction in LLM calls by 25-61%. Accompanying our method is NLR-BIRD, the first dedicated dataset for NLR benchmarking. Through human evaluations, we demonstrate the superior alignment of Combo-Eval with human judgments, applicable across scenarios with and without ground truth references.

Can LLMs Narrate Tabular Data? An Evaluation Framework for Natural Language Representations of Text-to-SQL System Outputs

Human reasoning is inherently multimodal: when problems become difficult, we rarely think in words alone. We often externalize our reasoning by sketching diagrams or drawing grids to better understand the underlying conceptual structure and avoid mistakes. Building upon this, our research investigates a) whether grounding multi-hop textual–spatial stories into other modalities that capture the geometry of the scene, like visualization or grids, improves reasoning abilities compared to natural language text-based inference; and b) whether the model is able to decide effectively when it should rely on natural language text-based reasoning and when it should switch between modalities. To assess this, we introduce a switching metric based on trustworthiness and complexity signals, which estimates when grounding a spatial story into structure is likely to improve performance, taking a first step towards principled modality selection in large language models' reasoning.

Spatial Reasoning via Modality Switching Between Language and Symbolic Visualization

Mental health is an incredibly pressing issue in terms of societal, economic, and personal impact.  Although it's natural to conceive of it as a clinical domain, it's also very much a social science domain; moreover, language is one of the most important carriers of signal about individuals' lived experiences and mental state. In this talk I'll discuss the evolving landscape and needs of computational mental health research, what folks interested in NLP+CSS can contribute, and why mental health should be attractive to NLP+CSS researchers as a space to work in. 

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Next from ACL 2026

More Than Efficiency: Embedding Compression Improves Domain Adaptation in Dense Retrieval

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Generalization or Memorization: Data Contamination and Trustworthy Evaluation for Large Language Models

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