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Improving the quality of model-generated summaries, especially factuality, the accuracy of a summary with respect to its source content, remains a challenge. While reranking could select the optimal output from multiple generated candidates, it is limited to only using the source as guidance, resulting in unreliable summaries. To address this limitation, we propose ConSUM that reranks candidate summaries by considering two factors: consistency to the source document and consensus among the other candidates. Consensus is established using Minimum Bayes Risk (MBR) decoding over the set of generated summaries, while ensuring consistency by employing factuality-aware metrics that compare the summary against the source. Rigorous testing demonstrates that our system is competitive with existing methods, with human evaluations further confirming that its generated summaries are preferred over those from other systems.

ACL 2026

Enhancing Factuality through Consensus and Consistency in Summarization Using Minimum Bayes Risk Decoding

## Welcome to NAACL 2022!
The Annual Meeting of the North American Chapter of the Association for Computational Linguistics (NAACL) is taking place July 10-15, 2022 as a hybrid event, in Seattle, WA and online. We are happy to welcome all of you to conference! 
 
The main conference program features oral presentations, in-person and virtual posters and demo sessions, a plenary session for our best paper presentations and awards, keynote presentations, a plenary panel on the place of linguistics and symbolic structures in NLP, and an Industrial Track panel on Careers in NLP.
 
Posters (including Findings of NAACL 2022) and demos are grouped by areas for both the in-person and the virtual sessions. For the virtual component, the talks will be on Zoom and the posters and the demos will be in GatherTown. The Student Research Workshop and will have an oral session and a poster session.
 
The program also features 6 Tutorials and 26 Workshops.
 
 We wish you a wonderful conference!
 
[The NAACL 2022 Organizing Committee](https://2022.naacl.org/committees/organization/)

NAACL 2022

2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics

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.

Tool agents---LLM-based systems that interact with external APIs---offer a way to execute real-world tasks. However, as tasks become increasingly complex, these agents struggle to identify and call the correct APIs in the proper order. To tackle this problem, we investigate converting API documentation into a structured API graph that captures API dependencies and leveraging it for multi-tool queries that require compositional API calls. To support this, we introduce In-N-Out, the first expert-annotated dataset of API graphs built from two real-world API benchmarks and their documentation. Using In-N-Out significantly improves performance on both tool retrieval and multi-tool query generation, nearly doubling that of LLMs using documentation alone. Moreover, graphs generated by models fine-tuned on In-N-Out close 90% of this gap, showing that our dataset helps models learn to comprehend API documentation and parameter relationships. Our findings highlight the promise of using explicit API graphs for tool agents and the utility of In-N-Out as a valuable resource. We will release the dataset and code publicly.

In-N-Out: A Parameter-Level API Graph Dataset for Tool Agents

Rhetorical questions are asked not to seek information but to persuade or signal stance. How large language models internally represent them remains unclear. We analyze rhetorical questions in LLM representations using linear probes on two social-media datasets with different discourse contexts, and find that rhetorical signals emerge early and are most stably captured by last-token representations. Rhetorical questions are linearly separable from information-seeking questions within datasets, and remain detectable under cross-dataset transfer, reaching AUROC around 0.7-0.8. However, we demonstrate that transferability does not simply imply a shared representation. Probes trained on different datasets produce different rankings when applied to the same target corpus, with overlap among the top-ranked instances often below 0.2. Qualitative analysis shows that these divergences correspond to distinct rhetorical phenomena: some probes capture discourse-level rhetorical stance embedded in extended argumentation, while others emphasize localized, syntax-driven interrogative acts. Together, these findings suggest that rhetorical questions in LLM representations are encoded by multiple linear directions emphasizing different cues, rather than a single shared direction.

Rhetorical Questions in LLM Representations: A Linear Probing Study

Large language models (LLMs) can reliably distinguish grammatical from ungrammatical sentences, but how grammatical knowledge is represented within the model remains an open question. We investigate whether different syntactic phenomena recruit shared or distinct components in LLMs. Using a functional localization approach inspired by cognitive neuroscience, we identify the LLM units most responsive to 67 English syntactic phenomena in seven open-weight models. These units are consistently recruited across sentence instances and causally support model performance. Critically, different types of syntactic agreement (e.g., subject-verb, anaphor, determiner-noun) recruit overlapping sets of units, suggesting that agreement constitutes a meaningful functional category in LLMs. This pattern holds in Russian and Chinese, and further, across languages: in a cross-lingual analysis of 57 languages, syntactically similar languages share more units for subject-verb agreement. Taken together, these findings reveal that syntactic agreement—a critical marker of syntactic dependencies—constitutes a meaningful category within LLMs' representational spaces.

Different types of syntactic agreement recruit the same units within large language models

Large language model (LLM) approaches to tabular summarization rely on extensive prompt engineering, decomposition pipelines, or entity-level intermediate representations to achieve strong performance. While effective, these strategies are computationally expensive and offer limited insight into how well models maintain state over long, evolving narratives. We introduce SporTabSet, a diagnostic benchmark for long-context tabular summarization across two complementary sports domains that require tracking multiple entities and aggregating statistics under domain-specific rules. Using SporTabSet, we systematically evaluate decomposition-based strategies across several long context LLMs. Results show that although decomposition substantially improves accuracy and numerical fidelity, gains stem mainly from dissecting multi-entity interference rather than improved local arithmetic. Robustness experiments further reveal high sensitivity to surface-level cues with structured failures, including hallucination, omission, and role confusion. Together, these findings identify consistent multi-entity memory as a key bottleneck in long-context table generation, motivating diagnostic evaluation as a prerequisite for scalable, efficient, and reliable tabular summarization models.

Moneyball with LLMs: Analyzing Tabular Summarization in Sports Narratives

Large Language Models (LLMs) are increasingly used as judges to evaluate text quality, moderate content, and assess arguments. We investigate whether alignment-instilled prior beliefs bias LLM judgments, using persuasion evaluation as a representative task. We find a systematic failure: models conflate their trained beliefs with rhetorical quality, rating identical claims differently based on belief alignment rather than argumentative merit. A bare assertion aligned with training receives higher scores than a well-crafted counter-argument, even when explicitly instructed to judge rhetoric alone. We introduce ConvinceQA, a dataset of 27,756 persuasive arguments with controlled stance variation across subjective, harmful, and misinformation domains, and demonstrate this prior prejudice across models. We exploit this failure through persuasion-based probing: evaluating minimal pairs that differ only in the subject token bypasses learned refusals and reveals hidden biases. Analysis identifies three failure modes, with belief-conditioned rating inflation accounting for 88\% of cases. Cross-task validation on essay quality assessment and debate judging confirms this is a pervasive limitation.

 Prior Beliefs Prejudice LLM-as-Judge: Evidence from Persuasion Evaluation

Recent work has demonstrated the potential of large language models (LLMs) for program optimization, a key challenge in programming languages. We propose a blackbox adaptation method called Retrieval Augmented Search (RAS) that performs beam search over candidate optimizations; at each step, it retrieves in-context examples from a given training dataset of slow-fast program pairs to guide the LLM. Critically, we find that performing contextual retrieval based on an LLM-generated natural language description significantly outperforms retrieval based on the source code. We also propose AEGIS, a method for improving interpretability by decomposing training examples into "atomic edits'' that are significantly more incremental in nature. We show that RAS performs up to 2.06$\times$ better than prior state-of-the-art blackbox adaptation strategies on optimizing C++ programs, and that AEGIS performs up to 1.37$\times$ better while making significantly smaller edits. We also show that using RAS improves the mean runtime percentile of Python programs by 10.27 compared to baselines.

LLM Program Optimization via Retrieval Augmented Search

Acceptability judgments provide a crucial basis for understanding how sentences are perceived as natural or well-formed, and they are increasingly used to assess the linguistic capability of large language models (LLMs). Unlike grammaticality, acceptability depends not only on structural form but also on contextual and domain-specific factors. Most prior work evaluates sentences in isolation, and relatively little is known about how explicit contextual cues influence LLM acceptability judgments across domains. This study examines how contextual information affects model-generated acceptability ratings across multiple domains and several LLMs, using different forms of domain-specific contextual cues to situate sentences in their intended usage settings. The results show that context can meaningfully shift model judgments, although its effects vary across models and domains. Overall, the findings provide evidence on contextual effects in LLM acceptability judgment and support the development of more context-aware evaluation frameworks.

Assessing the Effect of Context in Multi-domain Acceptability Judgment

Large language models (LLMs) frequently produce source code that seems correct and well-formed, yet includes hallucinated elements that cause downstream test failures. In this study, we benchmark state-of-the-art uncertainty quantification methods and existing baselines for the task of hallucination detection in source code and introduce a diff-based pipeline to construct a code dataset annotated with line-level hallucinations. Building on this, we train a lightweight Transformer-based detector that uses LLM internal representations to identify hallucinations, substantially outperforming existing methods across several code generation domains. The detector also shows particular promise for enabling self-correction in LLM-based coding agents. We release the first publicly available dataset of line-level code hallucinations, along with the corresponding source code and trained hallucination detectors https://github.com/datapaf/CodeHallucinationDetection

Efficient Hallucination Detection in Automatic Code Generation

Retrieval-augmented generation (RAG) extends the capabilities of large language models (LLMs) by providing access to external knowledge. However, traditional retrieval-augmented LLMs rely on a silent reading paradigm that processes all retrieved documents passively, forcing them to reason without any interaction with the documents. This paradigm contrasts sharply with human interactive reading behavior, where external tools, such as bookmarks and notes, are used to offload cognitive demands. This paper introduces BubbleRAG, an enhanced RAG framework that emulates human interactive reading through annotation and re-reading. Specifically, BubbleRAG utilizes a lightweight thought bubble module that offloads LLM's internal cognition into external bookmark tokens, which are then annotated back into the context. These bookmarks serve as externalized memory, allowing the LLM to revisit these annotations in subsequent reading and answering. Notably, BubbleRAG is particularly suitable for low-resource scenarios, as the LLM parameters remain frozen. Extensive experiments confirm the effectiveness, robustness, and generalizability of BubbleRAG. Our findings demonstrate that BubbleRAG enables LLMs to achieve superior evidence identification abilities typically seen in retrievers, while establishing a cognitive link between external and internal information during answer generation. The source code is available at https://github.com/yefd/BubbleRAG.

BubbleRAG: Interactive Cognitive Offloading with Thought Bubble in Retrieval-Augmented Generation

What makes some explanations more satisfying than others? Why are humans so driven to explain? In this talk I’ll take a cognitive scientific approach to explanation and understanding, drawing on decades of work in psychology, education, and philosophy to argue for a function-first approach to explanations—one on which we begin by characterizing what it is that explaining achieves. I’ll suggest that the process of explaining plays a crucial role in discovery and generalization, and that we can understand the structure and phenomenology of explanations by first appreciating how explaining drives learning and helps construct our understanding of the world. I’ll also consider some potential implications for explanation and understanding in AI systems.

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In-N-Out: A Parameter-Level API Graph Dataset for Tool Agents

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