United States

Legal practitioners in Thailand must navigate fragmented government websites to research over 3,800 active laws and 87,000 Supreme Court decisions, with no unified tool for cross-referencing, version tracking, or structural navigation. We present FourCorners, a deployed platform that addresses five practitioner pain points through three modules built on a temporal legal knowledge graph covering 552K nodes and 6.3M edges: (1) an AI legal assistant that performs grounded generation via structured graph retrieval, streaming verified source content inline with responses; (2) an interactive law reader that translates graph structure into navigation and comparison features; and (3) a court decision explorer with version-aware citations produced by temporal entity resolution across 87,394 rulings. The system discovers implicit cross-corpus relationships through co-citation analysis of Supreme Court decisions. Interviews with 20 legal professionals reveal that inline source verification fundamentally changes how practitioners interact with AI-generated legal content, and that cross-corpus enrichment surfaces legal relationships that existing tools leave invisible.

ACL 2026

FourCorners: Grounded Thai Legal Research over a Temporal Knowledge Graph

**Welcome to EMNLP 2023!**

On behalf of the EMNLP 2023 Organizing Committee, I extend a warm and heartfelt welcome to all of you to the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP). It is with immense pleasure and excitement that we gather here in Singapore, a vibrant hub of innovation and technological advancement.

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EMNLP 2023

EMNLP 2023 took place in Singapore from Dec 6th to Dec 10th, 2023.

technical 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 development of audio foundation models has accelerated rapidly since the emergence of GPT-4o. However, the lack of comprehensive evaluation has become a critical bottleneck for further progress in the field, particularly in audio generation. Current audio evaluation faces three major challenges: 
(1) audio evaluation lacks a unified framework, with datasets and code scattered across various sources;
(2) audio codec, as a key component of audio foundation models, lacks a widely accepted and holistic evaluation methodology; 
(3) existing speech benchmarks are heavily reliant on English, making it challenging to objectively assess models' performance on Chinese.
We introduce UltraEval-Audio, a unified framework addressing these challenges through a modular architecture supporting 10 languages, 14 task categories, 24 models, and 36 benchmarks with one-command evaluation and real-time leaderboards. For audio codec, we propose a three-dimensional evaluation scheme covering semantic accuracy, timbre fidelity, and acoustic quality. For Chinese evaluation, we introduce two new benchmarks: SpeechCMMLU and SpeechHSK. Our code, benchmarks, and leaderboards are available at https://github.com/OpenBMB/UltraEval-Audio.

UltraEval-Audio: A Unified Framework for Comprehensive Evaluation of Audio Foundation Models

This paper introduces AnnoHID, a semi-automated annotation framework designed for medical texts in low-resource languages. The system integrates large language models (LLMs) for pre-annotation and human validation, significantly reducing annotation time while maintaining accuracy. We demonstrate its application to Bahasa Indonesia medical social media texts from Alodokter, a medical Q&A platform, for named entity recognition (NER) and medical concept normalization (MCN). We conducted a user study with 21 participants to demonstrate the effectiveness of AnnoHID. The results show that LLM-assisted annotation yields higher inter-annotator agreement for both NER (kappa = 0.76) and MCN (kappa = 0.63) and that human review improves raw LLM NER output, raising the F1 score from 0.39 to 0.45. The framework is multilingual, human-in-the-loop, and interoperable with standard medical terminologies, such as SNOMED-CT. Future work focuses on mitigating pre-annotation bias, reducing annotation overhead, and scaling evaluations to larger datasets and additional low-resource languages.

AnnoHID: LLM-Assisted Annotation Framework for Low-Resource Medical Texts

In the high-cost simulation-driven design domain, translating ambiguous design requirements into a mathematical optimization formulation is a bottleneck for optimizing product performance. This process is time-consuming and heavily reliant on expert knowledge. While large language models (LLMs) offer potential for automating this task, existing approaches either suffer from poor formalization that fails to accurately align with the design intent or rely on solver feedback for data filtering, which is unavailable due to the high simulation costs. To address this challenge, we propose automated problem formulation (APF), a solver-independent framework that utilizes LLMs to convert engineers' natural language requirements into executable optimization models. The core of this framework is an innovative pipeline for automatically generating high-quality data, which overcomes the difficulty of constructing suitable fine-tuning datasets in the absence of high-cost solver feedback with the help of data generation and test instance annotation. The generated high-quality dataset is used to perform supervised fine-tuning on LLMs, significantly enhancing their ability to generate accurate and executable optimization problem formulations. Experimental results on antenna design demonstrate that APF significantly outperforms the existing methods in both the accuracy of requirement formalization and the quality of resulting radiation efficiency curves in meeting the design goals.

Solver-Independent Automated Problem Formulation via LLMs for High-Cost Simulation-Driven Design

Existing hard-label text attacks often rely on inefficient "outside-in" strategies that traverse vast search spaces. We propose PivotAttack, a query-efficient "inside-out" framework. It employs a Multi-Armed Bandit algorithm to identify Pivot Sets—combinatorial token groups acting as prediction anchors—and strategically perturbs them to induce label flips. This approach captures inter-word dependencies and minimizes query costs. Extensive experiments across traditional models and Large Language Models demonstrate that PivotAttack consistently outperforms state-of-the-art baselines in both Attack Success Rate and query efficiency.

PivotAttack: Rethinking the Search Trajectory in Hard-Label Text Attacks via Pivot Words

The stateless architecture of Large Language Models inherently lacks the mechanism to preserve dynamic context, compelling agents to redundantly reprocess history to maintain long-horizon autonomy. While latent memory offers a solution, current approaches are hindered by architectural segregation, relying on auxiliary encoders that decouple memory from the reasoning backbone. We propose \textbf{FlashMem}, a framework that distills intrinsic memory directly from transient reasoning states via computation reuse. Leveraging the property that internal representations uniquely encode input trajectories, FlashMem identifies the last hidden state as a sufficient statistic for the interaction history. This enables a \textbf{Shared-KV Consolidator} to synthesize memory by attending directly to the backbone's frozen cache, eliminating redundant re-parameterization. Furthermore, a parameter-free \textbf{Cognitive Monitor} leverages attention entropy to adaptively trigger consolidation only when high epistemic uncertainty is detected. Experiments demonstrate that FlashMem matches the performance of heavy baselines while reducing inference latency by \textbf{5 times}, effectively bridging the gap between efficiency and persistent cognition.

FlashMem: Distilling Intrinsic Latent Memory via Computation Reuse

Anonymizing textual documents is a highly context-sensitive problem: the appropriate balance between privacy protection and utility preservation varies with the data domain, privacy objectives, and downstream application. However, existing anonymization methods rely on static, manually designed strategies that lack the flexibility to adjust to diverse requirements and often fail to generalize across domains. We introduce adaptive text anonymization, a new task formulation in which anonymization strategies are automatically adapted to specific privacy–utility requirements. We propose a framework for task-specific prompt optimization that automatically constructs anonymization instructions for language models, enabling adaptation to different privacy goals, domains, and downstream usage patterns. To evaluate our approach, we present a benchmark spanning five datasets with diverse domains, privacy constraints, and utility objectives. Across all evaluated settings, our framework consistently achieves a better privacy–utility trade-off than existing baselines, while remaining computationally efficient and effective on open-source language models, with performance comparable to larger closed-source models. Additionally, we show that our method can discover novel anonymization strategies that explore different points along the privacy–utility trade-off frontier.

Adaptive Text Anonymization: Learning Privacy-Utility Trade-offs via Prompt Optimization

We present CadLLM, a training-free method to accelerate the inference throughput of diffusion-based LLMs (dLLMs). We first investigate on the dynamic nature of the token unmasking confidence across blocks and steps. Based on this observation, we then present a lightweight adaptive approach that can control the generation block size, step size, and threshold based on the average confidence score of the unmasked tokens. We further reduce the softmaxing overhead of token probability generation by dynamically leveraging a subset of vocabulary size to regulate sampling breadth. CadLLM is a plug-and-play model-agnostic with KV caching based dLLMs. Extensive experiments on four popular tasks demonstrate the efficacy of CadLLM to yield throughput improvement of up to 1.1-2.28x over the state-of-the-art baseline with competitive accuracy.

Improving the Throughput of Diffusion-based Large Language Models via a Training-Free Confidence-Aware Calibration

Diagnosing fine-grained hallucinations in Large Vision-Language Models (LVLMs) can greatly advance their reliable deployment in real-world applications. Nevertheless, current benchmarks predominantly employ flat metrics that treat errors in isolation, leaving a gap in evaluating the complex causal dependencies between visual perception and textual reasoning. Motivated by this, we introduce the Knowledge-Guided In-Context Probing (K-GIP) framework to fill this gap. Specifically, K-GIP constructs a high-fidelity dual-perception ground truth to transform abstract priors into multi-granularity queries. Furthermore, we propose a Verification Scene Graph metric equipped with a Sequential Logic Pruning protocol, which explicitly models existence-attribute dependencies to strictly penalize logical fractures. We conduct comprehensive evaluations of mainstream LVLMs across three datasets using K-GIP. The experimental results highlight that our methodology successfully isolates deep reasoning failures from simple perceptual misses. We hope K-GIP can serve as a valuable and rigorous standard to assess logical robustness in multimodal systems.

K-GIP: Diagnosing Logical Fractures in Large Vision-Language Models via Verification Scene Graphs and Sequential Pruning

Retrieval shapes how language models access and cite knowledge in retrieval-augmented generation (RAG). In historical research, the goal is often to locate the exact record for a specific regnal month, where temporal alignment matters as much as topical relevance. This is especially challenging for Classical Chinese annals: time is encoded in terse, implicit, non-Gregorian reign phrases that are context-dependent, so semantically plausible evidence can still be temporally invalid. We introduce **ChunQiuTR**, a time-keyed retrieval benchmark built from the **Spring and Autumn Annals** and its exegetical tradition. It organizes records by month-level reign keys and includes chrono-near confounders that mimic real retrieval failures. We propose **CTD** (Calendrical Temporal Dual-encoder), a time-aware dual-encoder combining Fourier-based absolute context with relative offset biasing. Experiments show consistent gains over semantic dual-encoder baselines under time-keyed evaluation. We will release ChunQiuTR and code after the anonymity period.

ChunQiuTR: Time-Keyed Temporal Retrieval in Classical Chinese Annals

Recent advancements in Generative Reward Models (GRMs) have demonstrated that scaling the length of Chain-of-Thought (CoT) reasoning considerably enhances the reliability of evaluation. However, current works predominantly rely on unstructured length scaling, ignoring the divergent efficacy of different reasoning mechanisms: Breadth-CoT (multi-dimensional principle coverage) and Depth-CoT (substantive judgment soundness). To address this, we introduce Mix-GRM, a framework that reconfigures raw rationales into structured Breadth-CoT and Depth-CoT through a modular synthesis pipeline, subsequently employing Supervised Fine-Tuning (SFT) and Reinforcement Learning with Verifiable Rewards (RLVR) to internalize and optimize these mechanisms. Comprehensive experiments demonstrate that Mix-GRM establishes a new state-of-the-art across five benchmarks, surpassing leading open-source RMs by an average of 8.2%. Our results reveal a clear divergence in reasoning: Breadth-CoT benefits subjective preference tasks, whereas Depth-CoT excels in objective correctness tasks. Consequently, misaligning the reasoning mechanism with the task directly degrades performance. Furthermore, we demonstrate that RLVR acts as a switching amplifier, inducing an emergent polarization where the model spontaneously allocates its reasoning style to match task demands.

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UltraEval-Audio: A Unified Framework for Comprehensive Evaluation of Audio Foundation Models

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