Drug shortages pose critical risks to patient care and healthcare systems worldwide, yet the effectiveness of regulatory interventions remains poorly understood due to information asymmetries in pharmaceutical supply chains. We propose ShortageSim, which addresses this challenge by providing the first simulation framework that evaluates the impact of regulatory interventions on competition dynamics under information asymmetry. Using Large Language Model (LLM)-based agents, the framework models the strategic decisions of drug manufacturers and institutional buyers, in response to shortage alerts given by the regulatory agency. Unlike traditional game theory models that assume perfect rationality and complete information, ShortageSim simulates heterogeneous interpretations on regulatory announcements and the resulting decisions. Experiments on self-processed dataset of historical shortage events show that ShortageSim reduces the resolution lag for production disruption cases by up to 84%, achieving closer alignment to real-world trajectories than the zero-shot baseline. Our framework confirms the effect of regulatory alert in addressing shortages and introduces a new method for understanding competition in multi-stage environments under uncertainty. We open-source ShortageSim and a dataset of 2,925 FDA shortage events, providing a novel framework for future research on policy design and testing in supply chains under information asymmetry.

Cheng Qian, Emre Can Acikgoz, Bingxuan Li, Xiusi Chen, Yuji Zhang, Bingxiang He, Qinyu Luo, Gokhan Tur, Dilek Hakkani-Tür, Yunzhu Li, Heng Ji. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Cheng Qian, Peixuan Han, Qinyu Luo, Bingxiang He, Xiusi Chen, Yuji Zhang, Hongyi Du, Jiarui Yao, Xiaocheng Yang, Denghui Zhang, Yunzhu Li, Heng Ji. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Self-consistency-based approaches, which involve repeatedly sampling multiple outputs and selecting the most consistent one as the final response, prove to be remarkably effective in improving the factual accuracy of large language models. Nonetheless, existing methods usually have strict constraints on the task format, largely limiting their applicability. In this paper, we present Integrative Decoding (ID), to unlock the potential of self-consistency in open-ended generation tasks. ID operates by constructing a set of inputs, each prepended with a previously sampled response, and then processes them concurrently, with the next token being selected by aggregating of all their corresponding predictions at each decoding step. In essence, this simple approach implicitly incorporates self-consistency in the decoding objective. Extensive evaluation shows that ID consistently enhances factuality over a wide range of language models, with substantial improvements on the TruthfulQA (+11.2%), Biographies (+15.4%) and LongFact (+8.5%) benchmarks. The performance gains amplify progressively as the number of sampled responses increases, indicating the potential of ID to scale up with repeated sampling.

The dynamic nature of real-world information necessitates knowledge editing (KE) in large language models (LLMs).This edited knowledge should propagate and facilitate the deduction of new information based on existing model knowledge.We define the existing related knowledge in a LLM serving as the origination of knowledge propagation as "deduction anchors".However, most of current KE approaches only operate on (subject, relation, object) triples.Both theoretically and empirically, we observe that this simplified setting often leads to uncertainty when determining the deduction anchors, causing low confidence in their responses.To mitigate this issue, we propose a novel task of event-based knowledge editing that pairs facts with event descriptions.This task manifests both as a closer simulation of real-world editing scenarios and a more logically sound setting, implicitly defining the deduction anchor and enabling LLMs to propagate knowledge confidently.We curate a new benchmark dataset EVEDIT derived from the COUNTERFACT dataset and validate its superiority in improving model confidence.Moreover, as we observe that the event-based setting is notably challenging for existing approaches, we propose a novel approach Self-Edit that showcases stronger performance, achieving 55.6% consistency improvement while maintaining the naturalness of generation. 1 Implicitly define the deduction anchor, ensuring model certainty. Previous Simple edits:Messi is a Dutch citizen.In 2024, Lionel Messi made the decision to move to Netherlands and applied for Dutch citizenship.After necessary procedures, he was granted Dutch citizenship and became a citizen of Netherlands.Q: Is Messi a citizen of Argentina in 2023?Q:Where was Messi born ?Q: Did Messi won the World Cup in 2022 ?Ignore the deduction anchor, leading to model uncertainty.

Discrimination against minority groups within the banking sector has long resulted in unequal treatment in financial services. Recent works in the general machine learning domain can promote group fairness for predictions on static tabular data, but their direct application in finance often proves ineffective. Financial losses of banks may arise from inaccurate predictions due to the overlooked dynamic nature of data, and illegal discrimination against some individual clients could still occur since fairness is promoted on the subgroup level. Therefore, we model the data as a dynamic or temporal transaction network for better utility and investigate individual fairness on this dynamic graph for the loan approval task. We define two novel individual fairness properties on temporal graphs with a theoretical analysis of their respective regret. Using these notions, we design a temporally fair graph neural network (TF-GNN) approach under a new real-time evaluation scheme for dynamic transaction networks. Experiments on real-world datasets demonstrate the superiority of the proposed method for both utility improvement in accuracy and fairness promotion in NDCG@k.

Language features are evolving in real-world social media, resulting in the deteriorating performance of text classification in dynamics.To address this challenge, we study temporal adaptation, where models trained on past data are tested in the future.Most prior work focused on continued pretraining or knowledge updating, which may compromise their performance on noisy social media data.To tackle this issue, we reflect feature change via modeling latent topic evolution and propose a novel model, VIBE: Variational Information Bottleneck for Evolutions.Concretely, we first employ two Information Bottleneck (IB) regularizers to distinguish past and future topics.Then, the distinguished topics work as adaptive features via multi-task training with timestamp and classlabel prediction.In adaptive learning, VIBE utilizes retrieved unlabeled data from online streams created posterior to training data time.Substantial Twitter experiments on three classification tasks show that our model, with only 3% of data, significantly outperforms previous state-of-the-art continued-pretraining methods.

Zexin Lu, Keyang Ding, Yuji Zhang, Jing Li, Baolin Peng, Lemao Liu. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.

Millions of hashtags are created on social media every day to cross-refer messages concerning similar topics.To help people find the topics they want to discuss, this paper characterizes a user's hashtagging preferences via predicting how likely they will post with a hashtag.It is hypothesized that one's interests in a hashtag are related to what they said before (user history) and the existing posts present the hashtag (hashtag contexts).These factors are married in the deep semantic space built with a pre-trained BERT and a neural topic model via joint training.In this way, user interests learned from the past can be customized to match future hashtags, which is beyond the capability of existing methods assuming unchanged hashtag semantics.Furthermore, we propose a novel personalized topic attention to capture salient contents to personalize hashtag contexts.Experiments on a large-scale Twitter dataset show that our model significantly outperforms the state-of-the-art recommendation approach without exploiting latent topics. 1 * Jing Li is the corresponding author.† This work was mainly conducted before Ziyan Jiang joined Amazon.1 Our dataset and code are publicly available in https://github.com/polyusmart/ Personalized-Hashtag-Preferences Sample tweets in H's hashtag contexts.Love thriller and mystery?Check out: URL She is writing the end for a long time.85-5 star review!Why not visit for Sunday share?Sample tweets in U 's user history.Darpocalypse is now available as an ebook!Read book 2 in the epic living dead series.Zombie Thriller Apocalypse Reader: Zeke is a skilled lover, so easy to fall for.But

2020 Conference on Empirical Methods in Natural Language Processing, 16th-20th November 2020, Online

We present Capacitivo, a contact-based object recognition technique developed for interactive fabrics, using capacitive sensing. Unlike prior work that has focused on metallic objects, our technique recognizes non-metallic objects such as food, different types of fruits, liquids, and other types of objects that are often found around a home or in a workplace. To demonstrate our technique, we created a prototype composed of a 12 x 12 grid of electrodes, made from conductive fabric attached to a textile substrate. We designed the size and separation between the electrodes to maximize the sensing area and sensitivity. We then used a 10-person study to evaluate the performance of our sensing technique using 20 different objects, which yielded a 94.5% accuracy rate. We conclude this work by presenting several different application scenarios to demonstrate unique interactions that are enabled by our technique on fabrics.