United States

Administrative tasks in the healthcare domain share linguistic commonalities, but it can be time-consuming to manually design LLM prompts for each use case. When calling health insurers, interactive voice response (IVR) systems cause delays in patient care and increase provider burnout due to complex routing and long hold times. Thus, IVR navigation models can offer significant time savings and reduce barriers to care. We propose an automated LLM pipeline that leverages a small number of ground truth datasets to transfer specialized prompts from one task to another; specifically, we performed a cross-task transfer of our IVR navigation logic, adapting the prompt from reaching the claims department to reaching the patient benefit department. We implemented a product-quality model which obtains high success rates in large-scale industrial healthcare settings with our proposed transfer learning pipeline with only a small number of human annotations. We obtained 82\% accuracy for turn-level evaluation, surpassing an optimized human-designed prompt (79\%) while reducing the prompt complexity by up to 80\%

ACL 2026

Transfer Learning for Generalizable Automated LLM Improvement Pipeline for IVR Navigation

### Welcome to CogSci Conference 2025!

The 47th Annual Meeting of the Cognitive Science Society was a hybrid meeting held in San Francisco. 

<div style="position:relative;padding-top:0;width:900px;height:500px;"><iframe style="position:absolute;border:none;width:100%;height:100%;left:0;top:0;" src="https://online.fliphtml5.com/ebtyf/amvr/"  seamless="seamless" scrolling="no" frameborder="0" allowtransparency="true" allowfullscreen="true" ></iframe></div>

#### About

The Cognitive Science Society brings together researchers from around the world who hold a common goal: understanding the nature of the human mind. The mission of the Society is to promote Cognitive Science as a discipline, and to foster scientific interchange among researchers in various areas of study, including Artificial Intelligence, Linguistics, Anthropology, Psychology, Neuroscience, Philosophy, and Education.

The Society is a non-profit professional organization and its activities include sponsoring an annual conference and publishing the journals Cognitive Science and TopiCS.

#### Our History 

* **Society Creation**<br>
The Society was incorporated as a 501(c)(3) non-profit professional organization in Massachusetts in 1979. The organizing committee included Roger Schank, Allan Collins, Donald Norman, and a number of other scholars from psychology, linguistics, computer science, and philosophy. 
<br><br>
* **Conference Creation**<br>
The first conference on cognitive science was held at La Jolla, California in August, 1979, and has occurred annually since then. The proceedings of each conference are published, and those from most years are available through Lawrence Erlbaum Associates, Inc. The annual proceedings of the Cognitive Science Conference represent a major source of information on new work and new ideas in the scientific study of thinking. In 1990, the Society, with help from an anonymous donor, established the David Marr Prize for the best student paper at each annual meeting.
<br><br>
* **Journal Creation**<br>
The Journal, Cognitive Science, began publication in 1976, and is now published by Wiley-Blackwell. The Executive Editor is currently Richard P. Cooper of Birkbeck, University of London, and there are 18 Associate Editors and a 30-member editorial board. It serves as the premier outlet for research reports that intersect two or more disciplines. Copyrights for articles published in the journal are held by the Society. The Governing Board of the Cognitive Science Society voted in late 2006 to found a new journal, Topics in Cognitive Science (topiCS). The Editor in Chief is Wayne Gray, Cognitive Science Department, Rensselaer Polytechnic Institute. The journal seeks to fill a niche not occupied by Cognitive Science Journal or other cognitive science journals. Membership in the Society includes a subscription to Cognitive Science and TopiCS. Copyrights for articles published in the journal are held by the Society.
<br><br>

#### Code of Conduct

By attending the CogSci 2025 Conference, you are required to adhere to the society’s **[Code of Conduct](https://drive.google.com/file/d/1ChPuihLy6jE_BWqfO7J2KKgX35JW2zsM/view?usp=sharing)**.
<br><br>


You need to log in with the email address you registered with. 

Login credentials were sent to you from Underline -  subject line "Welcome to the CogSci 2025 Conference". Please be sure to check your spam/promotional inbox  if you do not see an email confirmation right away.





Please log in to join this event.

To access the site, please register [**here**](https://cognitivesciencesociety.org/registration/).

If you are registered and feel like you are seeing this message by mistake, please make sure you are logged in with the same email that you registered with. 

Please register!

CogSci 2025

The 47th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

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>
<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.

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.

Packing and shuffling tokens is a common practice in training auto-regressive language models to prevent overfitting and improve efficiency. Documents are typically concatenated to chunks of maximum sequence length (MSL) and shuffled in chunks of tokens (atom-size chunk), possibly breaking context within documents. An alternative approach is padding, which only includes one document per chunk. To optimize both packing strategies (concatenation vs padding), we explored the optimal atom size for shuffling and compared performance and efficiency. We found that in the most common setup (where average document length is greater than MSL), matching atom size to MSL yields the lowest perplexity, controlling for dataset. Also, padding yields lower final perplexity than concatenation at the cost of lower efficiency. This trade-off informs the choice of shuffling and packing methods in training LMs.

Optimizing Packing and Shuffling Strategies for Enhanced Performance in Generative Language Models

Large language models excel at technical problem solving in English but struggle when questions are posed in Bangla. While translation offers a practical solution, existing Bangla-English systems frequently mistranslate specialized terminology, altering problem semantics and degrading downstream performance. We present BanglaSTEM, a dataset of 5,000 Bangla-English sentence pairs covering computer science, mathematics, physics, chemistry, and biology. Our pipeline extracts matching passages from official bilingual curriculum textbooks using OCR, then uses LLMs to align sentences and mark technical terms. These aligned examples serve as few-shot prompts for generating over 12,000 new translation pairs from LLMs, avoiding copyright issues. Human evaluators then select the best 5,000 pairs that correctly preserve technical terminology. We also test a term-weighted BLEU metric that gives higher weight to technical words, since standard metrics treat terminology errors and common word errors equally. We show that our weighted metric correlates better with downstream accuracy in code generation and math solving, while standard BLEU gives high scores even for wrong translations. The full implementation, dataset, and model will be made publicly available.

BanglaSTEM: A Parallel Corpus and Term-Weighted Evaluation for Technical Bangla-English Translation

Aspect-Term Sentiment Analysis (ATSA) aims to predict sentiment polarity for specific aspect terms, a task complicated by conflicting sentiments and limited context in short texts. Existing graph-based approaches, including recent multi-graph methods, rely on predefined pairwise structures to capture different linguistic views. However, these approaches impose a fixed interaction granularity and depend on multiple predefined graphs, limiting their ability to adapt to sentence-specific sentiment composition and capture higher-order relationships. We propose HyperATSA, a dynamic hypergraph framework that constructs instance-specific aspect-opinion groupings directly from contextual token representations, without relying on external parsers. Hyperedges are induced via hierarchical agglomerative clustering over token embeddings, with a novel acceleration-based cutoff that identifies natural group boundaries per sentence. Experiments on Lap14, Rest14, and MAMS demonstrate consistent improvements over strong graph-based baselines, with controlled comparisons across BERT and RoBERTa backbones confirming that the gains stem from the hypergraph mechanism rather than encoder strength.

From Graphs to Hypergraphs: Enhancing Aspect-Term Sentiment Analysis via Multi-Level Relational Modeling

Faithfulness in NLP explanations is typically assessed via input perturbations, implicitly treating explanations as causal drivers of decisions, an assumption ill-suited to human judgment. In educational assessment, explanations are better understood as structured justifications accompanying holistic decisions. We propose an explainability-first framework that treats human explanations as structured artifacts independent of prediction. By modeling explanation structure explicitly, our approach enables diagnostic analyses of emphasis, consistency, and dependence. We find that scores remain invariant under evidence removal, while explanatory structure persists in tutor-specific and rubric dependent ways, revealing a mismatch between prevailing faithfulness metrics and human reasoning. This provides a model-free foundation for auditing explanations and designing NLP systems whose explanations align with human evaluative practice.

Faithfulness Beyond Plausibility: Auditing Human Explanations in Educational Assessment

Although existing frameworks for large language model (LLM) inference on CPUs are mature, they fail to fully exploit the computational potential of many-core CPU platforms. Many-core CPUs are widely deployed in web servers and high-end networking devices, and are typically organized into multiple NUMA nodes that group cores and memory. Current frameworks largely overlook the substantial overhead of cross-NUMA memory access, limiting inference scalability and intelligence enabling on such platforms. To address this limitation, we build ArcLight, a lightweight LLM inference architecture designed from the ground up for many-core CPUs. ArcLight integrates efficient memory management and thread scheduling, and introduces finely controlled tensor parallelism to mitigate the cross-node memory access wall. Experimental results show that ArcLight significantly surpasses the performance ceiling of mainstream frameworks, achieving up to 46% higher inference throughput. Moreover, ArcLight maintains compatibility with arbitrary CPU devices. ArcLight is publicly available at https://github.com/OpenBMB/ArcLight.

ArcLight: A Lightweight LLM Inference Architecture for Many-Core CPUs

Public procurement data is legally open, yet practically locked inside thousands of unstructured PDFs and inconsistent portal metadata. pAtChWoRK starts with these fragmented, unstructured sources then leverages a hybrid pipeline (traditional NLP with LLM-based technologies) to restructure this information into a navigable knowledge base. Specifically, pAtChWoRK corrects manual classification errors, extracts complex unstructured fields such as award and solvency criteria and tenders' objectives, and assists users in easily navigating the tender landscape. This unified process enables more effective handling of the transparency bottlenecks that hinder competition and oversight in public administration. A user study with practitioners across different procurement

pAtChWoRK: Patching the Pieces of Public Procurement Documents

Vision-Language Models (VLMs) have become increasingly important in AI applications, yet interpretability tools for these models lag behind those available for text-only language models. While libraries like TransformerLens have enabled significant progress in understanding language models, existing tools for VLMs are limited to basic activation probing and saving. We introduce Spectra, a library specifically designed for mechanistic interpretability of VLMs that provides unified abstractions for activation patching, attention pattern analysis, and meta-functions across diverse VLM architectures. Built on HuggingFace's Transformers, our library handles architecture-specific complexities through per-checkpoint configurations while maintaining a simple, high-level interface. We demonstrate the library's capabilities by performing interpretability experiments on a counting task, showing how researchers can easily perform experiments that were previously cumbersome to do. The library currently supports Qwen2.5-VL, Qwen3-VL, LLaVA 1.5 and SmolVLM, with a design that facilitates extension to additional architectures. The library can be found at \url{github.com/clemneo/vlm-spectra}.

Spectra: A Mechanistic Interpretability Library for Vision-Language Models

We present OpenGlass, an open-source, privacy-preserving system for low-latency multimodal assistance in real-world deployments, with a primary focus on blind and low-vision users. Deploying large multimodal models for assistive use faces a practical tension between capability and interaction: cloud or server inference can provide strong visual understanding but often incurs multi-second delays and network jitter, while wearable devices can continuously sense the environment but have limited compute and power budgets. OpenGlass resolves this gap with a split local-first design: a lightweight glasses-side sensing unit captures visual context, while a nearby consumer-grade device performs multimodal inference and streams concise spoken feedback over local Wi-Fi or cellular connections, avoiding reliance on remote cloud services. We introduce a system-level evaluation suite covering response quality, end-to-end latency from query completion to audio playback, and safety-first abstention with auditable logs. OpenGlass supports three representative interaction patterns: mobile scene guidance for obstacle avoidance, user initiated interruption of speech output, and multi-turn question answering grounded in the current scene. In real deployment on consumer-grade hardware, OpenGlass achieves sub-2s system-level end-to-end latency from query completion to first audio playback.

OpenGlass: A Sensing-Computing Split Architecture for Local MLLM-Driven Real-Time Visual Assistance

Large language models (LLMs) have shown remarkable potential in scientific hypothesis discovery. However, existing approaches treat divergent exploratory ideation and convergent fine-grained refinement as isolated tasks, lacking a principled mechanism for human guidance. We present MOOSE-Copilot, the first unified framework to bridge this abstraction gap through a formalized human–AI interaction protocol. Our system empowers scientists to actively steer the automated process via three explicit guiding signals: initial blueprints, inter-stage routing, and regenerative feedback. Quantitative evaluations demonstrate that injecting these structured expert signals significantly outperforms purely autonomous baselines, establishing a theoretical upper bound for hypothesis quality through iterative refinement. Furthermore, we operationalize this paradigm into an accessible web-based interface featuring interactive tree visualization. By reducing the cognitively demanding discovery process into manageable subtasks of conceptual selection and critique, MOOSE-Copilot enables interdisciplinary researchers to effectively orchestrate and accelerate end-to-end scientific breakthroughs.

MOOSE-Copilot: A Web-Based Interactive Assistant for Unified Exploratory- and Fine-Grained Scientific Hypothesis Discovery

Transformer architecture gradually dominates the LLM field. Recent advances in training optimization for Transformer-based large language models (LLMs) primarily focus on architectural modifications or optimizer adjustments. However, these approaches lack systematic optimization of weight patterns during training. Weight pattern refers to the distribution and relative magnitudes of weight parameters in a neural network. To address this issue, we propose a Weight Scaling method called WISCA to enhance training efficiency and model quality by strategically improving neural network weight patterns—without changing network structures. By rescaling weights while preserving model outputs, WISCA indirectly optimizes the model’s training trajectory. Experiments demonstrate that WISCA significantly improves convergence quality (measured by generalization capability and loss reduction), particularly in LLMs with Grouped Query Attention (GQA) architectures and LoRA fine-tuning tasks. Empirical results show 5.6% average improvement on zero-shot validation tasks and 2.12% average reduction in training perplexity across multiple architectures.

Downloads

Next from ACL 2026

Optimizing Packing and Shuffling Strategies for Enhanced Performance in Generative Language Models

Similar lecture

Comparing Navigation in Immersive and Desktop VR Environments