United States

Large language models (LLMs) have shown considerable promise for annotation purposes, yet questions remain about their ability to capture human label variation (HLV) --- genuine disagreement between annotators often observed across NLP tasks. Here, we investigate how label and explanation variation manifests within and across LLMs with respect to the Natural Language Inference (NLI) task. Using zero-shot prompting with exact human annotation instructions, we treat individual model generations as participants and examine three response sampling strategies: varying generation parameters, leveraging within-family model size differences, and pooling responses from distinct LLMs. We show that, while model ensembles can generate label distributions similar to humans, they likewise exhibit distinct, idiosyncratic judgments and disagreement patterns. We further analyze explanation variation, observing that, although models generate longer explanations than humans, they demonstrate substantially less stylistic diversity. Our findings suggest that, while LLMs may serve as useful tools for generating diverse annotations, they should not be viewed as drop-in replacements for human annotators --- particularly in applications requiring authentic representation of diversity in human judgments, such as NLI.

ACL 2026

Label and Explanation Variation in LLM-Based Annotation: a Case Study in Natural Language Inference

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>
[Full Message from General Chair Philipp Koehn](https://drive.google.com/file/d/1QiJFOmMjvDgsDo3i1XzlQnImxe3RU_Cy/view?usp=sharing) <br><br><br>
<html><button style="display: inline-flex; align-items: center; justify-content: center; white-space: nowrap; border-radius: 9999px; font-weight: bold; background: #7c3aed; color: white; font-family: 'Space Grotesk', sans-serif; height: 40px; font-size: 16px; padding: 0 20px; border: none; cursor: pointer" onclick="window.open('https://drive.google.com/file/d/1QiJFOmMjvDgsDo3i1XzlQnImxe3RU_Cy/view?usp=sharing')">Conference Handbook</button></html>
<br><br><br><br>

To access this event page, you need to log in with the **email address you registered with**. <br>Access credentials will be sent to your email from Underline -  subject line "Welcome to ACL 2026". Please be sure to check your spam email folder if you do not see an email confirmation right away.

To access this event page, you are required to register.
Please complete your registration to continue.

We recommend reading [**the registration information**](https://2026.aclweb.org/registration/) first.

**Online Registration Form**: https://acl.swoogo.com/acl2026

Registration Required

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

technical paper

Machine unlearning aims to forget sensitive knowledge from Large Language Models (LLMs) while maintaining general utility. However, existing approaches typically treat all tokens in a response indiscriminately and enforce uncertainty over the entire vocabulary. This global treatment results in unnecessary utility degradation and extends optimization to content-agnostic regions. To address these limitations, we propose PALU (Prefix-Aware Localized Unlearning), a framework driven by a local entropy maximization objective across both temporal and vocabulary dimensions. PALU reveals that (i) suppressing the sensitive prefix alone is sufficient to sever the causal generation link, and (ii) flattening only the top-K logits is adequate to maximize uncertainty in the critical subspace. These findings allow PALU to alleviate redundant optimization across the full vocabulary and parameter space while minimizing collateral damage to general model performance. Comprehensive evaluations validate that PALU achieves superior forgetting efficacy and utility preservation compared to state-of-the-art baselines. Our code is available at https://github.com/nxZhai/PALU.

Maximizing Local Entropy Where It Matters: Prefix-Aware Localized LLM Unlearning

We present AutoSchemaKG, a framework for fully autonomous knowledge graph construction that eliminates the need for predefined schemas. Our system leverages large language models to simultaneously extract knowledge triples and induce comprehensive schemas directly from text, modeling both entities and events while employing conceptualization to organize instances into semantic categories. Processing over 50 million documents, we construct ATLAS (Automated Triple Linking And Schema induction), a family of knowledge graphs with 900+ million nodes and 5.9 billion edges. This approach outperforms state-of-the-art baselines on multi-hop QA tasks and enhances LLM factuality. Notably, our schema induction achieves 92% semantic alignment with human-crafted schemas with zero manual intervention, demonstrating that billion-scale knowledge graphs with dynamically induced schemas can effectively complement parametric knowledge in large language models.

AutoSchemaKG: Autonomous Knowledge Graph Construction through Dynamic Schema Induction from Web-Scale Corpora

Deep search agents that combine large language models with retrieval tools excel at complex, multi-hop queries. Yet, existing benchmarks such as BrowseComp rely on black-box web search APIs, facing key limitations. (1) Fairness: for agents, dynamic and opaque web APIs hinder reproducibility and fair comparisons across agents. (2) Disentanglement: for retrieval, the lack of a fixed document corpus makes it impossible to isolate retriever contributions from end-to-end search agent accuracy. We introduce BrowseComp-Plus, a benchmark derived from BrowseComp that employs a fixed, human-verified corpus, enabling controlled retrieval for deep search agents. BrowseComp-Plus clearly distinguishes agent performance: with a BM25 retriever, the open-source Search-R1 achieves 3.86% accuracy, while GPT-5 achieves 55.9%. Additionally, BrowseComp-Plus makes retrieval gains explicit: pairing GPT-5 with Qwen3-Embedding-8B retriever further improves accuracy to 70.1% while reducing search calls. Overall, BrowseComp-Plus provides a fair and disentangled testbed, advancing both deep search agent evaluation and retrieval research for agentic search. Code and data can be found at: https://texttron.github.io/BrowseComp-Plus/

BrowseComp-Plus: A Fair and Disentangled Evaluation Benchmark for Deep Search Agents

Decoder-only large language models (LLMs) have been increasingly adopted to build embedding models for diverse tasks. To overcome the inherent limitations of causal attention in representation learning, many existing methods modify the attention mechanism to be bidirectional, potentially undermining LLMs' ability to extract semantic information acquired during pre-training. Meanwhile, leading unidirectional approaches often rely on extra input text to generate contextualized embeddings, inevitably increasing computational costs. In this work, we propose Causal2Vec, a general-purpose embedding model tailored to enhance the performance of decoder-only LLMs without altering their original architectures or introducing significant computational overhead. Specifically, we first employ a lightweight BERT-style model to pre-encode the input text into a single Contextual token, which is then prepended to the LLM's input sequence, allowing each token to capture contextualized information even without attending to future tokens. Furthermore, to mitigate the recency bias introduced by last-token pooling, we concatenate the last hidden states of Contextual and EOS tokens as the final text embedding. In practice, Causal2Vec achieves a new state-of-the-art performance on the MTEB benchmark among models trained solely on publicly available retrieval datasets.

Causal2Vec: Improving Decoder-only LLMs as Embedding Models through a Contextual Token

Large Language Models (LLMs) have greatly advanced Natural Language Processing (NLP), particularly through instruction tuning, which enables broad task generalization without additional fine-tuning. However, their reliance on large-scale datasets—often collected from human or web sources—makes them vulnerable to backdoor attacks, where adversaries poison a small subset of data to implant hidden behaviors. Despite this growing risk, defenses for instruction-tuned models remain underexplored. We propose MB-Defense (Merging \& Breaking Defense Framework), a novel training pipeline that immunizes instruction-tuned LLMs against diverse backdoor threats. MB-Defense comprises two stages: (i) Defensive Poisoning, which merges attacker and defensive triggers into a unified backdoor representation, and (ii) Backdoor Neutralization, which breaks this representation through additional training to restore clean behavior. Extensive experiments across multiple LLMs show that MB-Defense substantially lowers attack success rates while preserving instruction-following ability. Our method offers a generalizable and data-efficient defense strategy, improving the robustness of instruction-tuned LLMs against unseen backdoor attacks.

Merging Triggers, Breaking Backdoors: Defensive Poisoning for Instruction-Tuned Language Models

As Large Language Models (LLMs) increasingly operate as Deep Research (DR) Agents capable of autonomous investigation and information synthesis, reliable evaluation of their task performance has become a critical bottleneck. Current benchmarks predominantly rely on static datasets, which suffer from several limitations: limited task generality, temporal misalignment, and data contamination. To address these, we introduce DR-Arena, a fully automated evaluation framework that pushes DR agents to their capability limits through dynamic investigation. DR-Arena constructs real-time Information Trees from fresh web trends to ensure the evaluation rubric is synchronized with the live world state, and employs an automated Examiner to generate structured tasks testing two orthogonal capabilities: Deep reasoning and Wide coverage. DR-Arena further adopts Adaptive Evolvement Loop, a state-machine controller that dynamically escalates task complexity based on real-time performance, demanding deeper deduction or wider aggregation until a decisive capability boundary emerges. Experiments with six advanced DR agents demonstrate that DR-Arena achieves a Spearman correlation of 0.94 with the LMSYS Search Arena leaderboard. This represents state-of-the-art alignment with human preferences without any manual efforts, validating DR-Arena as a reliable alternative for costly human adjudication.

DR-Arena: an Automated Evaluation Framework for Deep Research Agents

Anomaly detection aims at distinguishing between \textit{in-distribution} samples, which belong to the same distribution as the training set, and \textit{out-of-distribution} samples, which lie outside of it. In textual anomaly detection, recent approaches routinely apply anomaly detection algorithms directly to embeddings extracted from pre-trained embedding models (\textit{two-stage approaches}). However, the geometric properties of pre-trained embeddings can hinder the effectiveness of detection algorithms, which often rely on distance-based measures. In this work, we first highlight the relevance of similarity-trained models for textual anomaly detection. Beyond being trained to capture semantic similarities, these models also exhibit geometric properties that appear better suited to detection algorithms. We further demonstrate that, besides model choice, a simple post-processing step can significantly improve anomaly detection by adapting embeddings to the assumptions made by classical detection algorithms. The bulk of our experiments is done on a reformulation of the classification tasks from the MTEB benchmark into anomaly detection tasks.

Enhancing Two Steps Textual Anomaly Detection through Anisotropy Mitigation

Supervised Fine-Tuning (SFT) of large language models often suffers from task interference and catastrophic forgetting. Recent approaches alleviate this issue by isolating task-critical parameters during training. However, these methods represent a static solution to a dynamic problem, assuming that parameter importance remains fixed once identified. In this work, we empirically demonstrate that parameter importance exhibits temporal drift over the course of training. To address this, we propose Evolving Parameter Isolation (EPI), a fine-tuning framework that adapts isolation decisions based on online estimates of parameter importance. Instead of freezing a fixed subset of parameters, EPI periodically updates isolation masks using gradient-based signals, enabling the model to protect emerging task-critical parameters while releasing outdated ones to recover plasticity. Experiments on diverse multi-task benchmarks demonstrate that EPI consistently reduces interference and forgetting compared to static isolation and standard fine-tuning, while improving overall generalization. Our analysis highlights the necessity of synchronizing isolation mechanisms with the evolving dynamics of learning diverse abilities.

Parameter Importance is Not Static: Evolving Parameter Isolation for Supervised Fine-Tuning

Spatial language understanding is fundamental to tasks from robot navigation to document analysis, yet current work exhibits biases toward English and prepositional marking. We present a multilingual framework and benchmark decomposing spatial relations into surface elements (figure, ground, predicate, markers) and semantic components (dynamicity, stasis). Evaluating frontier LLMs on Spanish, Basque, and Chinese with text-only input, we find high accuracy on figure and ground identification but persistent gaps in two areas: semantic classification of topological and projective relations, and surface identification of morphological spatial markers—Basque case affixes proving most challenging at as low as 15.3\%. These results suggest that surface parsing does not entail spatial understanding, and that evaluation must include typologically diverse spatial marking strategies.

Where the Cat Sat: A Multilingual Framework for Spatial Language Understanding

The evaluation of societal biases in NLP models is critically hindered by a geo-cultural gap, This leaves regions such as Latin America severely underserved, making it impossible to adequately assess or mitigate the perpetuation of harmful regional stereotypes in language technologies. This paper presents LACES, a stereotype association dataset, for 15 Latin American countries. This dataset includes 4,789 stereotype associations manually created and annotated by 83 participants. The dataset was developed through targeted community partnerships across Latin America. Additionally, in this paper, we propose a novel adaptive data collection methodology that uniquely integrates the sourcing of new stereotype entries and the validation of existing data within a single, unified workflow. This approach results in a resource with more unique stereotypes than previous static collection methods, enabling a more efficient stereotype collection. The paper further supports the quality of LACES by demonstrating reduced efficacy of debiasing methods on this dataset in comparison to existing popular stereotype benchmarks.

Downloads

Next from ACL 2026

Maximizing Local Entropy Where It Matters: Prefix-Aware Localized LLM Unlearning

Similar lecture

Agree, Disagree, Explain: Decomposing Human Label Variation in NLI through the Lens of Explanations

.css-70qvj9{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}Downloads

Next from ACL 2026

Maximizing Local Entropy Where It Matters: Prefix-Aware Localized LLM Unlearning

Similar lecture

Agree, Disagree, Explain: Decomposing Human Label Variation in NLI through the Lens of Explanations

Downloads