Singapore

Automated short answer grading with feedback (ASAG-F) systems currently face challenges in transparency, pedagogical alignment, and cost-effectiveness that limit their real-world deployment. We introduce GraphRAG, a knowledge graph-based retrieval-augmented generation framework that addresses these limitations by grounding all large language model (LLM)-generated feedback and scores in instructor-curated atomic facts, ensuring traceability and verifiability. Using the Short Answer Feedback (SAF) dataset with 31 topics, we evaluate GraphRAG on unseen-question and unseen-answer splits. Our systematic evaluation demonstrates that GraphRAG achieves grading accuracy comparable to vector-based RAG and generally superior to a fine-tuned LLM baseline model while providing more transparent source attribution. Additional findings include: (1) Instructing the LLM to discretize continuous scores to match pedagogical rubrics, such as the 0.25 increments common in SAF, improves grading accuracy; (2) LLM-generated feedback exhibits length-dependent quality variations when unconstrained; prompt-based length control substantially enhances feedback quality and its stability, achieving optimal balance of instructional richness and conciseness; (3) Performance scaling analysis reveals that basic models like GPT-4o-mini offer cost-effective performance, while premium models like Claude-Opus-4 show diminishing returns. These results demonstrate that GraphRAG offers a robust, explainable, pedagogy-aligned, and cost-effective solution for large-scale educational applications, enabling transparent automated grading with effective pedagogical feedback and practical deployment costs.

AAAI 2026

Graph RAG for Automated Short Answer Grading with Feedback: Bridging Pedagogical Needs and Technical Capabilities

ai for education: automated assistants

hai: explainable ai (xai) for human understanding

knowledge graphs & kb completio

dmkm: linked open data

app: other applications

# Welcome everyone to ACL 2022!

The 60th Annual Meeting of the Association for Computational Linguistics is taking place May 22-27, 2022 as a hybrid event, in Dublin and online. We are happy to welcome all of you to this anniversary edition with an almost 50-50 in-person and virtual participation. 
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, three amazing keynote events and two new initiatives of invited talks: Spotlight Talks for Young Rising Stars (STIRS) and The Next Big Idea Talks. Posters (including Findings of ACL 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 will have an oral session and a poster session as part of Poster Session 1. The program also features eight Tutorials and 28 Workshops. 

 
We wish you a wonderful conference! 
[**The ACL 2022 Organizing Committee**](https://www.2022.aclweb.org/organisers)
 
[**Conference Handbook**](https://drive.google.com/file/d/1_BUCMfhMVrjG9E2e71aHdHeE28KSje0l/view?usp=sharing) 
[**Mini Handbook**](https://drive.google.com/file/d/1qlBKl0wzmlVF1oCeMQl3BahLd9nLP5Ce/view?usp=sharing) 
[**Posters and Demo guides**](https://drive.google.com/file/d/1UucMAoCNncIOaH1rMMDa0owuG9qgvJTG/view?usp=sharing)

ACL 2022

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing (NLP). 

technical paper

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

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We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

Large language models (LLMs) have rapidly transformed the landscape of AI, demonstrating remarkable capabilities across reasoning, communication, and problem-solving. Yet, realizing their full potential requires addressing two critical challenges. First, their behavior must be steered and refined after training to ensure reliability, safety, and alignment with human values and intentions. Second, their large scale comes with substantial costs in training and deployment, necessitating research into more efficient methods.
My research centers on advancing both of these fronts—making LLMs both aligned and efficient. On one side, I investigate post-training techniques that allow models to better reflect human preferences, demonstrate strong reasoning capabilities, and mitigate hallucination. On the other side, I study methods for improving data efficiency in training and inference efficiency in deployment. Together, these thrusts highlight a broader vision of enabling LLMs that are not only powerful, but also trustworthy and accessible at scale.

Towards Aligned and Efficient Large Language Models

While large language models (LLMs) challenge conventional methods of teaching and learning, they present an exciting opportunity to improve efficiency and scale high-quality instruction. One promising application is the generation of customized exams, tailored to specific course content. There has been significant recent excitement on automatically generating questions using artificial intelligence, but also comparatively little work evaluating the psychometric quality of these items in real-world educational settings. Filling this gap is an important step toward understanding generative AI's role in effective test design. In this study, we introduce and evaluate an iterative refinement strategy for question generation, repeatedly producing, assessing, and improving questions through cycles of LLM-generated critique and revision. We evaluate the quality of these AI-generated questions in a large-scale field study involving 91 classes---covering computer science, mathematics, chemistry, and more---in dozens of colleges across the United States, comprising nearly 1700 students. Our analysis, based on item response theory (IRT), suggests that for students in our sample the AI-generated questions performed comparably to expert-created questions designed for standardized exams. Our results illustrate the power of AI to make high-quality assessments more readily available, benefiting both teachers and students.

Assessing the Quality of AI-Generated Exams: A Large-Scale Field Study

Automated scoring plays a crucial role in education by reducing the reliance on human raters and offering scalable and immediate evaluation of student work. While large language models (LLMs) have shown strong potential in this task, their use as end-to-end raters faces challenges such as low accuracy, prompt sensitivity, limited interpretability, and rubric misalignment, which hinder practical implementation. To address the limitations, we propose AutoSCORE, a multi-agent LLM framework enhancing automated scoring via rubric-aligned Structured COmponent REcognition. With two agents, AutoSCORE first extracts rubric-relevant components from student responses and encodes them into a structured representation (i.e., Scoring Rubric Component Extraction Agent), which is then used to assign final scores (i.e., Scoring Agent). This design ensures that model reasoning follows a human-like grading process, enhancing interpretability and robustness. We evaluate AutoSCORE on four benchmark datasets from the ASAP benchmark, using both proprietary and open-source LLMs (GPT-4o, LLaMA-3.1-8B, LLaMA-3.1-70B). Across diverse tasks and rubrics, AutoSCORE predominantly improves scoring accuracy, human-machine agreement (QWK, correlations), and reduces error metrics (MAE, RMSE) compared to single-agent baselines, with particularly strong benefits on complex, multidimensional rubrics, and especially large relative gains on smaller LLMs. These results demonstrate that structured component recognition combined with multi-agent design offers a scalable, reliable, and interpretable solution for automated scoring.

AutoSCORE: Enhancing Automated Scoring with Multi-Agent Large Language Models via Structured Component Recognition

The teacher-student paradigm has emerged as a canonical framework in semi-supervised learning. When applied to medical image segmentation, the paradigm faces challenges due to inherent image ambiguities, making it particularly vulnerable to erroneous supervision. Crucially, the student's iterative reconfirmation of these errors leads to self-reinforcing bias. While some studies attempt to mitigate this bias, they often rely on external modifications to the conventional teacher-student framework, overlooking its intrinsic potential for error correction. In response, this work introduces a feedback mechanism into the teacher-student framework to counteract error reconfirmations. Here, the student provides feedback on the changes induced by the teacher's pseudo-labels, enabling the teacher to refine these labels accordingly. We specify that this interaction hinges on two key components: the feedback attributor, which designates pseudo-labels triggering the student's update, and the feedback receiver, which determines where to apply this feedback. Building on this, a dual-teacher feedback model is further proposed, which allows more dynamics in the feedback loop and fosters more gains by resolving disagreements through cross-teacher supervision while avoiding consistent errors. Comprehensive evaluations on three medical image benchmarks demonstrate the method's effectiveness in addressing error propagation in semi-supervised medical image segmentation.

DualFete: Revisiting Teacher-Student Interactions from a Feedback Perspective for Semi-supervised Medical Image Segmentation

Text-Based Person Retrieval (TBPR) aims to accurately retrieve target individuals from large-scale image databases using only textual descriptions. Existing methods typically assume a ground-truth correspondence between text and images (i.e., strongly correlated). However, in real-world scenarios, this assumption may not be able to hold for the cross-modal matching due to weak or even corrupted correlations between textual descriptions and visual content, referred to as noisy correspondence (NC). Such NC largely disrupts the correspondence learning between visual and semantic modalities. Though prior works have improved single-modal robustness against noisy labels, systematic modeling of both cross-modal and intra-modal geometric structures in TBPR remains limited attention. In this paper, we propose Geometric Structure Consistency Alignment (\textbf{GSCA}) to TBPR, which leverages cross-modal cosine similarity and intra-modal nearest-neighbor affinity to learn visual-semantic consistency under noisy correspondence. To mitigate the structural corruption caused by noisy pairs, we introduce the Structure Refinement and Mining (\textbf{SRAM}) module. By partitioning training data into clean, ambiguous, and noisy subsets, SRAM enables the model to strategically refine the cross-modal correspondence by mining reliablepairs, thus enhancing the reliability of positive or negative samples discrimination and preserving structural consistency across modalities. Extensive experiments demonstrate that our method achieves state-of-the-art performance across three public datasets. On CUHK-PEDES, it boosts Rank-1 by 1.42\% in noise-free conditions, sustaining a robust 74.25\% Rank-1 under a 50\% noise ratio.

Geometry-Aware Noisy Correspondence Mitigation for Cross-Modal Text-Based Person Retrieval

Semi-Supervised Instance Segmentation (SSIS) involves classifying and grouping image pixels into distinct object instances using limited labeled data alongside large-scale unlabeled data. A major challenge in SSIS lies in the inherent noise of pseudo-labels, particularly when class and mask qualities are coupled into a single confidence score for filtering. Such coupling often results in sub-optimal trade-offs between semantic accuracy and spatial precision. To address this, we propose a novel Pseudo-Label Decoupling and Correction (PL-DC) framework, which explicitly decouples and enhances the pseudo-label selection process for SSIS. At the instance level, we introduce a Decoupled Filtering with Adaptive Class-Aware Thresholds mechanism, which independently evaluates class and mask qualities using category-specific thresholds updated via exponential moving averages. At the category level, we design a Dynamic Instance Category Correction module that reassigns ambiguous class pseudo-label by leveraging semantic prototypes and consistency alignment. At the pixel level, a Pixel-Level Mask Uncertainty-Aware mechanism is applied to suppress the influence of unreliable pixels during mask supervision, further improving the robustness against pixel-wise noise. Extensive experiments on COCO and Cityscapes datasets demonstrate that the proposed PL-DC achieves significant performance improvements, setting new state-of-the-art results. Notably, PL-DC achieves gains of +11.7 mAP with just 1% labeled COCO data and +16.4 mAP with 5% Cityscapes labels, showing its effectiveness under extremely low-label regimes.

Robust Pseudo-Labeling via Decoupled Class-Aware Filtering and Dynamic Category Correction

In multi-objective decision-making with hierarchical preferences, lexicographic bandits provide a natural framework for optimizing multiple objectives in a prioritized order. In this setting, a learner repeatedly selects arms and observes reward vectors, aiming to maximize the reward for the highest-priority objective, then the next, and so on. While previous studies have primarily focused on regret minimization, this work bridges the gap between *regret minimization* and *best arm identification* under lexicographic preferences. We propose two elimination-based algorithms to address this joint objective. The first algorithm eliminates suboptimal arms sequentially, layer by layer, in accordance with the objective priorities, and achieves sample complexity and regret bounds comparable to those of the best single-objective algorithms. The second algorithm simultaneously leverages reward information from all objectives in each round, effectively exploiting cross-objective dependencies. Remarkably, it outperforms the known lower bound for the single-objective bandit problem, highlighting the benefit of cross-objective information sharing in the multi-objective setting. Empirical results further validate their superior performance over baselines.

Beyond the Lower Bound: Bridging Regret Minimization and Best Arm Identification in Lexicographic Bandits

We present CineMPC, a complete cinematographic
system that autonomously controls a drone to film multiple
targets
recording user-specified aesthetic objectives. Existing
solutions in
autonomous cinematography control only the camera
extrinsics,
namely, its position and orientation. In contrast, CineMPC
is the
first solution that includes the camera intrinsic
parameters in the
control loop, which are essential tools for controlling
cinematographic effects such as focus, depth of field, and
zoom. The system
estimates the relative poses between the targets and the
camera
from an RGB-D image and optimizes a trajectory for the
extrinsic
and intrinsic camera parameters to film the artistic and
technical
requirements specified by the user. The drone and the camera
are controlled in a nonlinear model predicted control (MPC)
loop
by reoptimizing the trajectory at each time step in
response to
current conditions in the scene. The perception system of
CineMPC
can track the targets’ position and orientation despite the
camera
effects. Experiments in a photo-realistic simulation and
with a real
platform demonstrate the capabilities of the system to
achieve a
full array of cinematographic effects that are not possible
without
the control of the intrinsics of the camera. Code for
CineMPC is
implemented following a modular architecture in ROS and
released
to the community.

CineMPC: A fully autonomous drone cinematography system
incorporating zoom, focus, pose, and scene composition

Radio Frequency Fingerprinting (RFF) exploits inherent hardware-level imperfections of wireless transmitters as unclonable identifiers for device identification. These unique signatures, concealed in transmitted signals, inevitably experience complex distortions during wireless propagation (i.e., coupled with ambient noise and channel fading), making it extremely challenging for reliable extraction. Despite substantial research efforts dedicated to advancing effective fingerprint extraction techniques, current approaches still struggle in handling fingerprint robustness under distance variations, leading to severe SNR fluctuations and complex multipath effects. To address this gap, we propose the first unsupervised framework for distance-invariant radio frequency fingerprinting, eliminating dependence on labeled target domain data. Specifically, we first preprocess raw RF samples by confining them within a specified variation range and filtering noisy high-frequency components while avoiding aliasing. For source domain data, we then propose a set of physics-inspired data augmentation techniques designed to emulate realistic wireless signal propagation effects. Building on this, we introduce a dual alignment contrastive learning method to explicitly decouple identity-discriminative features, ensuring the model focuses on device-specific traits. Furthermore, we incorporate a pseudo-labeling-based domain adaptation module to refine the model for the unlabeled target domain, enhancing its generalization to unseen distances. Extensive experiments on public datasets show that our method achieves the identification accuracy outperforming state-of-the-art approaches by 40\%, while maintaining computational efficiency suitable for edge deployment.

Towards Distance-Invariant Radio Frequency Fingerprinting via Augmented Unsupervised Learning

An agent acts in its world to achieve its objectives. Intelligence allows the agent to make decisions and act. In natural domains, sensing is limited, so acting is gambling. It’s a myth that passive learning and more data are all we need. An agent cannot learn from observations alone. It needs a real body to carry out experiments in its world, testing hypotheses, to determine causation, refining its model of the world’s dynamics. The agent is acting as a scientist: refining its model through experiments and acting appropriately to achieve its objectives. Its objectives depend on its preferences and values, and those of other agents its actions impact. Determining which values to use, and how preferences can be acquired fairly, is a major non-technical challenge. We address three primary questions: What should an agent believe? What should an agent do, given its beliefs, preferences, and abilities? What should the preferences of an agent be? Integrating these issues motivates the design of our latest AI textbook, Artificial Intelligence: Foundations of Computational Agents, (3rd Ed. 2023).

Alan Mackworth is a Professor Emeritus of Computer Science at the University of British Columbia. He works on artificial intelligence with applications in constraint satisfaction, cognitive robotics, assistive technology, hybrid systems and constraint-based agents. He invented the world’s first soccer-playing robots. He has co-authored two books: Computational Intelligence: A Logical Approach (1998) and Artificial Intelligence: Foundations of Computational Agents (2023, 3rd Ed.). Alan co-founded the UBC Cognitive Systems Program, the Centre for AI, Decision-making and Action (CAIDA) and the AI network of BC (AInBC). He has served as President of AAAI, IJCAI and CAIAC. He is a Fellow of AAAI, CAIAC, AGE-WELL, CIFAR and the Royal Society of Canada.

David Poole is a Professor Emeritus of Computer Science at the University of British Columbia. He is known for his work on combining logic and probability, probabilistic inference, relational probabilistic models, statistical relational AI and semantic science. He is a co-author of two AI textbooks (Cambridge University Press, 3rd edition 2023, and Oxford University Press, 1998), and co-author of ” Statistical Relational Artificial Intelligence: Logic, Probability, and Computation”. He is a former chair of the Association for Uncertainty in Artificial Intelligence, the winner of the Canadian AI Association (CAIAC) 2013 Lifetime Achievement Award, and is a Fellow of the Association for the Advancement Artificial Intelligence (AAAI) and CAIAC.

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Towards Aligned and Efficient Large Language Models

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Introducing a Bilingual Short Answer Feedback Dataset

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