United States

Retrieval-augmented generation (RAG) based on dense embeddings has become a dominant paradigm for text retrieval. However, many real-world applications require attribute-specific querying, where explicit values or properties must be extracted from text (e.g., symptoms in clinical notes or dosage values in medical reports). Dense retrieval handles paraphrastic variation well but often entangles multiple attributes within a single embedding, making value extraction difficult. Knowledge graphs (KGs), in contrast, support explicit attribute access but are brittle under linguistic and structural variation, leading to low recall. This thesis proposal aims to investigate the representational trade-off underlying these approaches. We study knowledge graph representations from an information-theoretic and optimal coding perspective, focusing on the tension between fine-grained factorization and compact canonicalization of concepts. Building on this perspective, we propose a query-driven framework for constructing and retrieving knowledge graphs from text, aiming to combine the robustness of dense retrieval with the explicit queryability of symbolic representations.

ACL 2026

Thesis Proposal: On the Granularity-Robustness Trade-off in Text-Derived Knowledge Graphs

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible. The experience for on-site participants would closely approximate a normal pre-COVID *ACL conference, with 5-6 thematically organized parallel sessions and live Q/A and interactive discussion immediately after the talks. Presentations by virtual participants will be equitably interleaved with those of on-site participants, projected on the auditorium screens as if on-site, and also followed immediately by live Q/A and interactive discussion at a time during reasonable waking hours for the virtual presenter. For all participants, on-site and virtual, who are unable to attend a session due to either time-zone issues or because they are participating in another session live, talk recordings and slides will be available online at a minimum after the live presentation (and in many cases before as well), and questions may be submitted in advance on session-specific discussion boards and answered live in session with the usual visual aids if desired.

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EMNLP 2021

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible.

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.

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>
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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.

Federated learning with heterogeneous client architectures cannot rely on parameter aggregation. Prototype-based methods address architectural heterogeneity by exchanging class-level representations, but naively averaging prototypes across non-IID clients leads to semantic drift and poor inter-class separation. We propose FedPAGR, a framework where heterogeneous clients project their features into a shared consensus space and exchange class prototypes with a central server. The server refines aggregated prototypes through a geometric regularization objective that enforces agreement with client submissions and inter-class angular separation. Clients anchor their classifiers to the refined prototypes and train with a composite objective combining classification, prototype alignment, and entropy regularization. We evaluate FedPAGR across multiple domains, including four image benchmarks and a clinical NLP task using heterogeneous ClinicalBERT variants, with five architectures per federation under severe label heterogeneity ($\alpha{=}0.1$). FedPAGR achieves the highest ensemble accuracy across all four image datasets and the highest local test accuracy on low-class and clinical tasks, including a 4.99-point improvement over the strongest baseline on MIMIC-IV, while remaining competitive on high-class benchmarks.

FedPAGR: Federated Prototype Alignment via Geometric Refinement for Heterogeneous Architectures

Federated learning is often framed as a practical trade-off in clinical NLP: safer data handling at the cost of lower predictive performance. We test this assumption in a benchmark-specific study of Polish medical text classification and show that the expected penalty of locality is not inevitable. A key issue is evaluation granularity: the test split contains 10{,}634 rows but only 670 unique normalized text hashes, with 18 inconsistent groups removed in strict grouped evaluation. We therefore compare centralized and federated training under both conventional instance-level scoring and a stricter hash-level protocol that controls duplicate inflation. In the strongest settings, federated training matches or slightly exceeds the centralized baseline, reaching instance-level Macro-F1 of $0.8826 \pm 0.0177$ versus $0.8689 \pm 0.0124$, and hash-level Macro-F1 of $0.8908 \pm 0.0220$ versus $0.8841 \pm 0.0078$.We do not claim formal privacy guarantees; rather, we show that in this duplicate-heavy Polish medical text benchmark, conclusions about locality depend strongly on evaluation hygiene. An anonymized GitHub repository with code, run manifests, and evaluation scripts is included with the submission.

Does Locality Cost in Polish Medical Text Classification? Duplicate-Aware Evaluation of Federated Learning

Active learning (AL) reduces labeled data requirements in NLP, yet most methods optimize label efficiency while ignoring annotation cost. Standard uncertainty sampling assumes uniform effort, leading to suboptimal resource allocation when documents vary in length. Supasan and Athuraliya (2026) introduced CAL-Log, a cost-aware AL variant using logarithmic cost modeling C(x)=α+β log(1+L(x)), where C(x) is the predicted annotation time for document x and L(x) is its token length, grounded in information foraging theory (Pirolli and Card, 1999) and psycholinguistic studies of human skimming (Rayner, 1998). This paper presents CAL-Log in full, extending that preliminary framework with two new contributions: temperature-scaled calibrated entropy and online per-annotator cost adaptation, which together resolve the cold-start calibration bottleneck identified in the prior work. Experiments on ten text classification benchmarks demonstrate a 3.3× speedup over BADGE (Batch Active learning by Diverse Gradient Embeddings; Ash et al., 2020) and 3.9× over Entropy sampling to reach F1=0.80, with large effect sizes (Cohen's d>0.8). A live annotation deployment with preliminary user evaluation (N=7) confirms that the online cost model produces reading-speed classifications consistent with annotator self-reports, and that a transparency interface successfully communicates the scoring rationale to non-expert users.

CAL-Log: Cost-Aware Active Learning with Logarithmic Cognitive Effort Modeling and Online Adaptation to Human Annotation Behavior

Self-consistency improves large language models by sampling multiple reasoning paths and selecting the most frequent answer, but majority vote often fails to recover correct answers that are already present among samples. In this work, we reformulate answer selection in self-consistency as a ranking problem. Instead of relying on a single uncertainty or confidence signal, we train a lightweight reranker to score candidate answers using five carefully designed features that capture answer-level frequency, semantic centrality, and reasoning-trace consistency. We instantiate this approach with a LambdaRank model and evaluate it on three datasets under a range of test-time budgets. Across datasets, the proposed method consistently achieves a better accuracy-efficiency trade-off than standard self-consistency and strong baselines, with particularly large gains on question answering benchmarks. Further analysis shows that the proposed features are individually useful and, more importantly, complementary, highlighting the value of learning to combine multiple informative signals for test-time answer selection.

Boosting Self-Consistency with Ranking

Open Information Extraction (OIE) has largely focused on extracting relational tuples from text, yet in its current form remains unsuitable for downstream systems due to the absence of standardized, semantically sound representations. This thesis argues that the field has been addressing extraction as a surface-level prediction problem, leading to outputs that are semantically incomplete and logically ambiguous, particularly in the presence of modality, negation, conditionality, quantification, and attribution. We propose a normalization-first framework that reframes OIE as a structured semantic transformation pipeline, where raw text is first converted into a lossless, canonical form of declarative, active-voice, and irreducible sentence units, and extraction is constrained to atomic unary and binary relations augmented with explicit semantic annotations. Within a Probably Approximately Correct (PAC) learning perspective, we formalize soundness, completeness, and usefulness as approximate yet verifiable guarantees over extraction quality, acknowledging the inherent undecidability of full semantic interpretation. This thesis outlines a feasible research program to develop the theoretical foundations, models, and evaluation protocols required to produce system-ready OIE representations, thereby establishing a principled and executable path toward making OIE directly usable for downstream reasoning and machine interpretability.

Thesis Proposal: A Normalization-First Framework for Sound, Complete, and Utility-Ready Open Information Extraction

LiTS is a modular Python framework for LLM reasoning via tree search. It decomposes tree search into three reusable components—Policy, Transition, and RewardModel—that plug into algorithms like MCTS and BFS. A decorator-based registry enables domain experts to extend to new domains by registering components, and algorithmic researchers to implement custom search algorithms. We demonstrate composability on MATH500 (language reasoning), Crosswords (environment planning), and MapEval (tool use), showing that components and algorithms are orthogonal: components are reusable across algorithms within each task type, and algorithms work across all components and domains. We also report a mode-collapse finding: in infinite action spaces, LLM policy diversity—not reward quality—is the bottleneck for effective tree search. A demonstration video is available at https://youtu.be/nRGX43YrR3I. The package is released under the Apache 2.0 license at https://github.com/xinzhel/lits-llm, including installation instructions and runnable examples that enable users to reproduce the demonstrated workflows.

LiTS: A Modular Framework for LLM Tree Search

Large language model (LLM) agents are increasingly applied to financial decision-making tasks that require interaction with external tools, including market data retrieval, news analysis, and trade execution. However, existing trading systems rely on fragmented and task-specific APIs, resulting in inconsistent schemas, complex integration, and limited reproducibility. We present QFinZero, a unified trading environment for LLM-based financial agents. QFinZero standardizes three core capabilities: (i) multi-frequency market and derivatives data access, (ii) structured news and event retrieval, and (iii) stateful brokerage simulation with explicit order lifecycle management. All tools adopt consistent JSON schemas and time-aligned interfaces, enabling agents to acquire information and execute trades within a coherent framework. By abstracting financial interaction into composable, agent-invokable primitives, QFinZero reduces engineering overhead and supports reproducible evaluation through comprehensive logging and deterministic replay. We argue that such a standardized trading environment is essential for scalable research on LLM-based financial agents.

QFinZero: A Unified Financial Toolchain for LLM-Based Trading Agents

We propose Fast-MIA (https://github.com/Nikkei/fast-mia), a Python library for efficiently evaluating membership inference attacks (MIA) against large language models (LLMs).
MIA has emerged as a crucial technique for auditing privacy risks and copyright infringement in LLMs. 
However, computational demands have grown substantially: recent methods rely on repeated inference, while practical auditing requires large-scale evaluation.
Progress is further hindered by existing implementations that execute methods independently, redundantly computing shared intermediate results such as log-probabilities.
To address these challenges, Fast-MIA combines two strategies: (1) high-throughput batch inference via vLLM, achieving approximately 5$\times$ speedup, and (2) a cross-method caching architecture that computes intermediate results once and shares them across methods.
The library includes representative MIA methods under a unified framework, integrates with established benchmarks, and supports flexible YAML configuration.
We release Fast-MIA under the Apache License 2.0 to support scalable and reproducible MIA research.

Fast-MIA: Efficient and Scalable Membership Inference for LLMs

Efforts over the past three decades have produced web archives containing billions of webpage snapshots and petabytes of data. The End of Term Web Archive alone contains millions of PDFs produced by the federal government. While preservation with web archives has been successful, significant challenges for access and discoverability remain. In this paper, we introduce GovScape, a public search system that supports multimodal searches across 10,015,993 federal government PDFs from the 2020 End of Term crawl (70,958,487 total PDF pages) -- to our knowledge, all renderable PDFs in the 2020 crawl that are 50 pages or under. GovScape supports four primary forms of search: in addition to providing (1) filter conditions over metadata facets including domain and crawl date and (2) exact text search against the PDF text, we provide (3) semantic text search and (4) visual search against the PDFs across individual pages, enabling users to structure queries such as “redacted documents'' or “pie charts.'' We detail GovScape's search affordances, embedding pipeline, system architecture, and open source codebase. Significantly, the total estimated compute cost for GovScape's pre-processing pipeline for 10 million PDFs was approximately $1,500, equivalent to 47,000 PDF pages per dollar spent on compute, demonstrating the potential for immediate scalability. We evaluate GovScape by (1) analyzing 1,679 search queries and (2) benchmarking vector and keyword index efficiency using these queries. GovScape can be found at https://www.govscape.net.

GovScape: A Public Multimodal Search System for 70 Million Pages of Government PDFs

Linux kernel device drivers are tightly coupled with hardware, making them difficult to execute and test without physical devices. This heavily limits automated code analysis and vulnerability discovery. While manual modeling is unscalable, Large Language Models (LLMs) offer a new approach to scale virtual device construction across the Linux driver ecosystem. In this paper, we present DevGen, an LLM-powered tool that generates QEMU-based virtual devices directly from Linux driver source code. DevGen combines static analysis to gather necessary context, guides the LLM through step-by-step prompting, and uses an automated self-correction loop driven by compilation and execution feedback. To further reduce errors, similar fixes are retrieved from a library of common modeling failures and incorporated into the repair prompt, which supports more targeted corrections in later iterations. The generated devices finally integrate with QEMU and Syzkaller, enabling driver fuzzing without physical hardware. DevGen is evaluated on 50 PCI/PCIe drivers from Linux 6.18 using three mainstream LLMs, and successfully generates usable models for 44 drivers. In these drivers, 24% of them achieve significant improvements in fuzzing coverage, and 7 previously unknown crashes are triggered with 1 CVE assigned. These results demonstrate the practical capability of LLMs to automate complex, system-level code generation tasks.

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FedPAGR: Federated Prototype Alignment via Geometric Refinement for Heterogeneous Architectures

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FedPAGR: Federated Prototype Alignment via Geometric Refinement for Heterogeneous Architectures

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Combining Lexical and Dense Retrieval for Computationally Efficient Multi-hop Question Answering

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