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Code-switching, alternating between languages within a conversation, is natural for multilingual users, yet poses fundamental challenges for large language models (LLMs). When a user code-switches in their prompt to an LLM, they typically do not specify the expected language of the LLM response, and thus LLMs must infer the output language from contextual and pragmatic cues. We find that current LLMs systematically fail to align with this expectation, responding in undesired languages even when cues are clear to humans. We introduce OLA, a benchmark to evaluate LLMs&#39; Output Language Alignment in code-switched interactions. OLA focuses on Korean--English code-switching and spans simple intra-sentential mixing to instruction–content mismatches. Even frontier models frequently misinterpret implicit language expectation, exhibiting a systematic bias toward non-English responses. We further show this bias generalizes beyond Korean to Chinese and Indonesian pairs. Models also show instability through mid-response switching and language intrusions. Chain-of-Thought prompting fails to resolve these errors, indicating weak pragmatic reasoning about output language. However, Code-Switching Aware DPO with minimal data (~1K examples) substantially reduces misalignment, suggesting these failures stem from insufficient alignment rather than fundamental limitations. Our results highlight the need to align multilingual LLMs with users&#39; implicit expectations in real-world code-switched interactions.

ACL 2026

OLA: Output Language Alignment in Code-Switched LLM Interactions

### Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Message from the General Chair: 
*It is my great pleasure and honor to welcome you to the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025), held in beautiful Vienna, Austria, from July 27 to August 1, 2025. ACL2025continues our field’s 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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ACL 2025

Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Code-switching, alternating between languages within a conversation, is natural for multilingual users, yet poses fundamental challenges for large language models (LLMs). When a user code-switches in their prompt to an LLM, they typically do not specify the expected language of the LLM response, and thus LLMs must infer the output language from contextual and pragmatic cues. We find that current LLMs systematically fail to align with this expectation, responding in undesired languages even when cues are clear to humans. We introduce OLA, a benchmark to evaluate LLMs' Output Language Alignment in code-switched interactions. OLA focuses on Korean--English code-switching and spans simple intra-sentential mixing to instruction–content mismatches. Even frontier models frequently misinterpret implicit language expectation, exhibiting a systematic bias toward non-English responses. We further show this bias generalizes beyond Korean to Chinese and Indonesian pairs. Models also show instability through mid-response switching and language intrusions. Chain-of-Thought prompting fails to resolve these errors, indicating weak pragmatic reasoning about output language. However, Code-Switching Aware DPO with minimal data (~1K examples) substantially reduces misalignment, suggesting these failures stem from insufficient alignment rather than fundamental limitations. Our results highlight the need to align multilingual LLMs with users' implicit expectations in real-world code-switched interactions.

poster

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. 
[Full Message from General Chair Philipp Koehn](https://drive.google.com/file/d/1QiJFOmMjvDgsDo3i1XzlQnImxe3RU_Cy/view?usp=sharing) 
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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.

Modern evaluation of Legal QA systems is shifting from terminal accuracy toward process-aware analyses of model reasoning. We propose a diagnostic framework grounded in monotonic pedagogical scaffolding, where language models receive gold-standard, case-relevant information across stages aligned with the canonical legal framework FIRAC — Facts, Issue, Rules, Application, Conclusion. By strictly adding solution-relevant content at each step, we introduce a controlled monotonic intervention that allows for the evaluation of reasoning trajectories rather than isolated outcomes.

This longitudinal design enables the introduction of two transition-based diagnostics: Errors-to-Success (E2S) quantifies the guidance required to reach correctness, while Success-to-Errors (S2E) measures the fragility of that correctness under additional structure. These local patterns define a global robustness criterion termed Stable Accuracy, which credits a response only if the model maintains correctness throughout all scaffolding stages and enforces a higher bar for correctness by distinguishing sustained reasoning from transient patterns.

We instantiate the framework on 3,123 Brazilian Bar Exam questions paired with expert-annotated explanations. Our findings reveal model instability patterns hidden from accuracy-only metrics and demonstrate that terminal accuracy systematically overestimates legal reasoning competence. To test the robustness of our diagnostics, we also evaluate a majority-vote aggregation across multiple reasoning samples, finding that the observed instability patterns persist under this stronger inference setting. Furthermore, principal component analysis indicates that legal domains cluster into distinct regions, suggesting systematic differences in reasoning demands across domains. While focused on the legal domain, our evaluation protocol is generalizable to any task with a staged reasoning structure.

Monotonic Scaffolding as a Diagnostic Lens for Legal Reasoning in LLMs

Large language models (LLMs) store and recall factual knowledge, yet the precise mechanism of how entity representations are transformed to enable specific attribute retrieval remains underexplored. In this work, we investigate this mechanism through the lens of an ``attribute-computation path''—a sequence of computational steps over the entity representation required to elicit a target attribute. We then propose an iterative patching protocol to identify a minimal subset of layers necessary for this computation. Applying our method to LLaMA 3.1 8B and Qwen 3 8B, we find that these paths are non-contiguous, often skipping layers, and that models possess multiple, functionally-equivalent paths for the same entity and fact, highlighting a high degree of redundancy in attribute computation. This implies that knowledge computation is highly distributed, potentially explaining the localization-editing mismatch and suggesting that knowledge storage and retrieval in LLMs is far from being well understood.

Factual Retrieval in LLMs Is a Redundant, Distributed and Non-Contiguous Process

Many approaches to LLM red-teaming leverage an attacker LLM to discover jailbreaks against a target. Several of them task the attacker with identifying effective strategies through trial and error, resulting in a semantically limited range of successes. Another approach discovers diverse attacks by combining crowdsourced harmful queries and tactics into instructions for the attacker, but does so at random, limiting effectiveness. This article introduces a novel framework, Adaptive Instruction Composition, that combines crowdsourced texts according to an adaptive mechanism trained to jointly optimize effectiveness with diversity. We use reinforcement learning to balance exploration with exploitation in a combinatorial space of instructions to guide the attacker toward diverse generations tailored to target vulnerabilities. We demonstrate that our approach substantially outperforms random combination on a set of effectiveness and diversity metrics, even under model transfer. Further, we show that it surpasses a host of recent adaptive approaches on Harmbench. We employ a lightweight neural contextual bandit that adapts to contrastive embedding inputs, and provide ablations suggesting that the contrastive pretraining enables the network to rapidly generalize and scale to the massive space as it learns.

Adaptive Instruction Composition for Automated LLM Red-Teaming

Reasoning-enhanced large language models rely on intermediate reasoning signals to solve complex, multi-step tasks, making reasoning behavior a valuable form of intellectual property. Meanwhile, knowledge distillation enables an adversary to replicate this behavior in a realistic black-box setting by repeatedly querying a deployed model on a target domain and training a local student to imitate its outputs, including reasoning traces. Existing LLM watermarks primarily operate on surface text and decoding-time token biases, and thus fail to provide reliable attribution of reasoning behavior once it is transferred through knowledge distillation. ReasMark entangles the watermark with the target-domain input distribution by selecting watermark tokens from high-frequency prompts, so distillation queries naturally activate it. It then embeds the watermark by score-conditioned losses that create a detectable reasoning-length gap for black-box verification. Comprehensive experiments across multiple LLMs, datasets, and distillation settings demonstrate that ReasMark consistently outperforms existing baselines while preserving task utility.

ReasMark: A Robust Watermark for Attributing LLM Reasoning Under Knowledge Distillation Attacks

Measuring the quality of public deliberation requires evaluating not only civility or argument structure, but also the informational progress of a conversation. We introduce a framework for Conversational Information Gain (CIG) that evaluates each utterance in terms of how it advances collective understanding of the target topic. To operationalize CIG, we model an evolving semantic memory of the discussion: the system extracts atomic claims from utterances and incrementally consolidates them into a structured memory state. Using this memory, we score each utterance along three interpretable dimensions: Novelty, Relevance, and Implication Scope. We annotate 80 segments from two moderated deliberative settings (TV debates and community discussions) with these dimensions and show that memory-derived dynamics (e.g., the number of claim updates) correlate more strongly with human-perceived CIG than traditional heuristics such as utterance length or TF--IDF. We develop effective LLM-based CIG predictors paving the way for information-focused conversation quality analysis in dialogues and deliberative success.

CIG: Measuring Conversational Information Gain in Deliberative Dialogues with Semantic Memory Dynamics

Recovering business requirements from
COBOL and PL/I portfolios is difficult because
logic is scattered across interdependent
programs and data definitions, and existing
analyses seldom yield stakeholder-facing
artifacts. We introduce an LLM-augmented
reverse-engineering pipeline that couples
grammar-based parsing and control-flow
and data-flow analysis with a large language
model to translate an enriched intermediate
representation into structured specifications.
The pipeline produces business requirements
documents, explicit rule catalogues, end-to-end
data lineage, create–read–update–delete
matrices, and field-level source-to-target
mappings, each linked to the supporting code.
In a financial industry setting, containing
3.2 millions of lines of COBOL, the system
achieves 93% agreement with expert-authored
business rules and reduces documentation
effort by approximately 70%, as measured
against manually produced requirement
documents and rule sets. On an additional
internal corpus spanning 3.4M+ lines across
online, batch, and job control workloads, the
approach yields approximately 3.2–3.3× faster
analysis while improving artifact consistency
and traceability.

Leveraging Generative AI for Extracting Business Requirements from Legacy COBOL and PL/I Code

The scalability of high-quality online education is hindered by the high costs and slow cycles of manual content creation.
Despite advancements in video generation, current approaches often fail to ensure pedagogical structure and precise control due to their pixel-level, black-box nature.
In this paper, we propose Generative Teaching, a novel paradigm shifting educators from manual creators to high-level directors who focus on pedagogical intents while agents handle the execution. To realize this vision, we introduce TeachMaster, a multi-agent framework that leverages code as an intermediate semantic medium. Unlike traditional video generation methods, TeachMaster orchestrates a collaborative team of agents, spanning planning, design, and rendering, to automate the production of interpretable, editable, and curriculum-ready educational videos. 
Experiments validate that TeachMaster significantly boosts production efficiency without compromising structural coherence or visual fidelity, slashing production costs to only 0.3% of traditional online course videos and providing a robust solution for scalable education.

TeachMaster: Generative Teaching via Code

Online advertising governance faces significant challenges due to the non-stationary nature of regulatory policies, where emerging mandates (e.g., restrictions on education or aesthetic anxiety) create severe label inconsistencies and reasoning ambiguities in historical datasets. In this paper, we propose ARGUS, a policy-adaptive governance system that enables evolving reinforcement through multi-agent adversarial umpiring. ARGUS addresses the sparsity of new policy data by employing a three-stage framework: (1) Policy Seeding for initial perception; (2) Adversarial Label Rectification, which utilizes a ''Prosecutor-Defender-Umpire'' architecture to resolve conflicts between stale labels and new mandates; and (3) Latent Knowledge Discovery, which employs a tripartite dialectical discussion to unearth sophisticated, ``gray-area'' violations. By leveraging RAG-enhanced policy knowledge and Chain-of-Thought synthesis as dynamic rewards for reinforcement learning, ARGUS synchronizes its reasoning pathways with evolving regulations. Extensive experiments on both industrial and public datasets demonstrate that ARGUS significantly outperforms traditional fine-tuning baselines, achieving superior policy-adaptive learning with minimal gold data.

ARGUS: Policy-Adaptive Ad Governance via Evolving Reinforcement with Adversarial Umpiring

Given the ubiquitous nature of Large Language Models (LLMs) and its impressive capabilities, malicious uses of this technology to generate harmful content have been observed. Thus, to mitigate this serious security risk LLMs pose, many researchers have proposed two techniques for detecting synthetic texts generated from LLMs - watermark and automatic detection. The idea with watermarking LLMs involves infusing generated content with algorithmically-identifiable patterns during generation. This makes accurate synthetic text detection achievable with watermark detection. While, for automatic detection, the focus is on using statistical and linguistic cues to reveal authorship of texts as human or LLM. Currently, both types of synthetic text detectors achieve state-of-the-art performance, however, the better detector is still unknown. To ascertain the better detection method, we evaluate each method on their performance on both unperturbed and perturbed (i.e., adversarially manipulated texts) data. We perform a comprehensive study across six different sizes of Qwen2.5 models, six watermark techniques and detectors, two automatic detectors, three authorship obfuscation methods for different levels of syntactic changes, and two datasets of different text lengths. Our results suggest that there is no detector that consistently outperforms on all scenarios. However, we observe that the (1) automatic detectors are better for short synthetic text detection; and (2) watermark detectors perform better defending against the word-level attack implemented.

Synthetic Text Detection in the Age of Large Language Models: Watermark vs. Automatic Detection

Document understanding in real-world applications often requires processing heterogeneous, multi-page document packets containing multiple documents stitched together. Despite recent advances in visual document understanding, the fundamental task of document packet splitting, which involves separating a document packet into individual units, remains largely unaddressed. We present the first comprehensive benchmark dataset, $\textit{DocSplit}$, along with novel evaluation metrics for assessing the document packet splitting capabilities of large language models. $\textit{DocSplit}$ comprises five datasets of varying complexity, covering diverse document types, layouts, and multimodal settings. We formalize the $\textit{DocSplit}$ task, which requires models to identify document boundaries, classify document types, and maintain correct page ordering within a document packet. The benchmark addresses real-world challenges, including out-of-order pages, interleaved documents, and documents lacking clear demarcations. We conduct extensive experiments evaluating multimodal LLMs on our datasets, revealing significant performance gaps in current models' ability to handle complex document splitting tasks. The $\textit{DocSplit}$ benchmark datasets and proposed novel evaluation metrics provide a systematic framework for advancing document understanding capabilities essential for legal, financial, healthcare, and other document-intensive domains. We release the datasets and evaluation code to facilitate future research in document packet processing.

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Monotonic Scaffolding as a Diagnostic Lens for Legal Reasoning in LLMs

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