Papers by Zhijing Jin

Moûsai: Efficient Text-to-Music Diffusion Models (2024.acl-long)

Challenge:	Prior work has focused on situational awareness, which refers to a model's ability to recognize its operating phase and constraints, but it has neglected the complementary capacity to identify and adapt to the identity and characteristics of a dialogue partner.
Approach:	They formalize interlocutor awareness and evaluate its emergence in contemporary LLMs.
Outcome:	The proposed model reliably identify same-family peers and certain prominent model families, such as GPT and Claude.

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Flavio Schneider, Ojasv Kamal, Zhijing Jin, Bernhard Schölkopf

Multimodality

Challenge:	Recent years have seen the rapid development of large generative models for text; however, little research has explored the connection between text and another “language” of communication – music.
Approach:	They develop a text-to-music generation model that can generate multiple minutes of high-quality stereo music at 48kHz from textual descriptions.
Outcome:	The proposed model can generate multiple minutes of high-quality stereo music at 48kHz from textual descriptions.

CausalCite: A Causal Formulation of Paper Citations (2024.findings-acl)

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Ishan Agrawal, Zhijing Jin, Ehsan Mokhtarian, Siyuan Guo, Yuen Chen, Mrinmaya Sachan, Bernhard Schölkopf

Representation Learning

Challenge:	citation counts are often criticized for failing to accurately reflect the true impact of a paper.
Approach:	They propose a method to measure the impact of a paper on follow-up papers by comparing similar papers by cosine similarity.
Outcome:	The proposed method is based on a new causal inference method, TextMatch.

Democratic or Authoritarian? Probing a New Dimension of Political Biases in Large Language Models (2026.eacl-long)

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David Guzman Piedrahita, Irene Strauss, Rada Mihalcea, Zhijing Jin

Hooks in the Headline: Learning to Generate Headlines with Controlled Styles (2020.acl-main)

Challenge:	Prior work on LLM biases focused on socio-demographic and left–right political dimensions, but little attention has been paid to how they align with broader geopolitical value systems.
Approach:	They propose a method to assess how LLMs align with broader geopolitical value systems, particularly the democracy–authoritarianism spectrum.
Outcome:	The proposed method combines the F-scale, FavScore and role-model probing to assess which figures are cited as general role models by LLMs.

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Di Jin, Zhijing Jin, Joey Tianyi Zhou, Lisa Orii, Peter Szolovits

Text Generation

Challenge:	Current summarization systems only produce plain, factual headlines, far from the practical needs for exposure and memorableness of the articles.
Approach:	They propose a task to generate relevant headlines with three style options . they propose combining summarization and reconstruction tasks into a multitasking framework .
Outcome:	The proposed method outperforms the state-of-the-art summarization model by 9.68% . it can generate relevant, fluent headlines with humor, romance and clickbait .

Automatic Generation of Model and Data Cards: A Step Towards Responsible AI (2024.naacl-long)

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Jiarui Liu, Wenkai Li, Zhijing Jin, Mona Diab

PaperMentor: A Human-Centered Multi-Agent Writing Tutor for AI Research Papers in Overleaf (2026.acl-demo)

Challenge:	Existing models and datasets are incomplete and lack consistent documentation.
Approach:	They propose an automated generation approach using Large Language Models (LLMs) their paper establishes a comprehensive dataset and develops 'CardGen' pipeline .
Outcome:	The proposed approach exhibits enhanced completeness, objectivity, and faithfulness in generated model and data cards, a significant step in responsible AI documentation practices ensuring better accountability and traceability.

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Jiarui Liu, Terry Jingchen Zhang, Ryan Faulkner, Xuanqiang Angelo Huang, Vilém Zouhar, Dominik Glandorf, Isabel Dahlgren, Rishit Dagli, Yuen Chen, Felix Leeb, Van Q. Truong, Punya Syon Pandey, Yves Bicker, Suvajit Majumder, Wenyuan Jiang, Zeju Qiu, Sankalan Pal Chowdhury, Mrinmaya Sachan, Bernhard Schölkopf, Mona T. Diab, Zhijing Jin

Challenge:	Emerging AI-powered writing assistants focus on grammar fixes or simulating peer review with final scores, yet they fall short of providing concrete, actionable suggestions that help students improve their papers during drafting.
Approach:	They propose a human-centered writing assistant system that delivers actionable suggestions as Overleaf-native inline comments while leaving the actual writing entirely to human authors.
Outcome:	The proposed system outperforms a baseline with the skill library and provides actionable suggestions while leaving the actual writing to human authors.

Toward Global AI Inclusivity: A Large-Scale Multilingual Terminology Dataset (GIST) (2025.findings-acl)

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Jiarui Liu, Iman Ouzzani, Wenkai Li, Lechen Zhang, Tianyue Ou, Houda Bouamor, Zhijing Jin, Mona T. Diab

Multilinguality

Challenge:	Despite advances in machine translation, domain-specific terminology translation remains challenging.
Approach:	They propose a large-scale multilingual AI terminology dataset that combines LLMs for extraction with human expertise for translation.
Outcome:	The proposed framework combines human translation expertise with LLMs to improve translation accuracy and improve BLEU and COMET scores.

How Good Is NLP? A Sober Look at NLP Tasks through the Lens of Social Impact (2021.findings-acl)

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Zhijing Jin, Geeticka Chauhan, Brian Tse, Mrinmaya Sachan, Rada Mihalcea

Challenge:	Recent years have seen many breakthroughs in natural language processing (NLP), transitioning it from a mostly theoretical field to one with many real-world applications.
Approach:	They propose a moral philosophy definition of social good and a framework to evaluate the direct and indirect real-world impact of NLP tasks.
Outcome:	The proposed framework evaluates the direct and indirect real-world impact of NLP tasks and adopts the methodology of global priorities research to identify priority causes for NLP research.

Are Language Models Consequentialist or Deontological Moral Reasoners? (2025.emnlp-main)

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Keenan Samway, Max Kleiman-Weiner, David Guzman Piedrahita, Rada Mihalcea, Bernhard Schölkopf, Zhijing Jin

Logical Fallacy Detection (2022.findings-emnlp)

Challenge:	Recent studies have focused on the moral judgments in large language models rather than their underlying moral reasoning process.
Approach:	They propose a taxonomy of moral rationales to classify reasoning traces according to consequentialism and deontology . they use trolley problems to analyze moral reasoning tracing in large language models .
Outcome:	The proposed taxonomy of moral rationales sheds light on consequentialism and deontology . it systematically classifies reasoning traces according to two main ethical theories .

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Zhijing Jin, Abhinav Lalwani, Tejas Vaidhya, Xiaoyu Shen, Yiwen Ding, Zhiheng Lyu, Mrinmaya Sachan, Rada Mihalcea, Bernhard Schoelkopf

ALERT: Adapt Language Models to Reasoning Tasks (2023.acl-long)

Challenge:	Existing language models perform poorly on logical fallacy detection . fallacious arguments can lead to disagreements, conflicts, endless debates, and a lack of consensus .
Approach:	They propose a task of logical fallacy detection and propose LogicClimate to detect fallacies in text.
Outcome:	The proposed task outperforms the best language model on Logic and LogicClimate . human reasoning is marred by logical fallacies, and some exacerbate misinformation .

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Ping Yu, Tianlu Wang, Olga Golovneva, Badr AlKhamissi, Siddharth Verma, Zhijing Jin, Gargi Ghosh, Mona Diab, Asli Celikyilmaz

Causal Direction of Data Collection Matters: Implications of Causal and Anticausal Learning for NLP (2021.emnlp-main)

Challenge:	Large language models have shown increasing in-context learning capabilities with scaling up the model and data sizes.
Approach:	They propose a benchmark and suite of analyses to evaluate reasoning skills of large language models.
Outcome:	The proposed model compares pre-trained and fine-tuned models on tasks that require reasoning skills to solve.

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Zhijing Jin, Julius von Kügelgen, Jingwei Ni, Tejas Vaidhya, Ayush Kaushal, Mrinmaya Sachan, Bernhard Schoelkopf

Challenge:	a meta-analysis of published studies shows that the causal direction of data collection can explain some trends in NLP . semi-supervised learning and domain adaptation performance differ on a number of tasks .
Approach:	They argue that the causal direction of the data collection process has nontrivial implications . authors categorize common NLP tasks according to their causal direction . they also empirically assay the validity of the ICM principle for text data .
Outcome:	The proposed model can explain differences in semi-supervised learning and domain adaptation performance across settings.

DARS: Dynamic Action Re-Sampling to Enhance Coding Agent Performance by Adaptive Tree Traversal (2025.acl-long)

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Vaibhav Aggarwal, Ojasv Kamal, Abhinav Japesh, Zhijing Jin, Bernhard Schölkopf

Challenge:	Existing approaches to developing LLM-powered coding agents struggle with sub-optimal decision-making.
Approach:	They propose a novel inference time compute scaling approach that recovers from sub-optimal decisions by branching out a trajectory at certain key decision points by taking an alternative action given the history of the trajectory and execution feedback of the previous attempt.
Outcome:	The proposed approach achieves a pass@1 rate of 47% on the SWE-Bench Lite benchmark, outperforming state-of-the-art (SOTA) open-source frameworks.

Taming Object Hallucinations with Verified Atomic Confidence Estimation (2026.eacl-long)

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Jiarui Liu, Weihao Xuan, Zhijing Jin, Mona T. Diab

When Does Aggregating Multiple Skills with Multi-Task Learning Work? A Case Study in Financial NLP (2023.acl-long)

Challenge:	Multimodal Large Language Models suffer from hallucinations, especially errors in object existence, attributes, or relations.
Approach:	They propose a framework that decomposes responses into atomic queries and estimates confidence using self-consistency or self-confidence aggregation.
Outcome:	Experiments on five benchmarks show that TACO outperforms direct prompting and Visual Contrastive Decoding and improves confidence calibration.

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Jingwei Ni, Zhijing Jin, Qian Wang, Mrinmaya Sachan, Markus Leippold

Improving Large Language Model Safety with Contrastive Representation Learning (2025.emnlp-main)

Challenge:	Multi-task learning (MTL) is a machine learning paradigm where multiple learning tasks are optimized simultaneously, exploiting commonalities and differences across them.
Approach:	They propose a parameter-efficient MTL architecture to improve task aggregation and to include loosely related skills from multiple datasets.
Outcome:	The proposed architecture outperforms single-task learning (STL) and is expected to outperformed it.

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Samuel Simko, Mrinmaya Sachan, Bernhard Schölkopf, Zhijing Jin

GenWiki: A Dataset of 1.3 Million Content-Sharing Text and Graphs for Unsupervised Graph-to-Text Generation (2020.coling-main)

Challenge:	Existing defenses against large language models (LLMs) are limited by their ability to generate responses to diverse inputs.
Approach:	They propose a model defense framework that finetunes a large-scale model using a triplet-based loss combined with adversarial hard negative mining to encourage separation between benign and harmful representations.
Outcome:	The proposed model defense outperforms previous representation engineering-based defenses while improving robustness against input-level and embedding-space attacks.

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Zhijing Jin, Qipeng Guo, Xipeng Qiu, Zheng Zhang

Structured Data in NLP

Challenge:	a large-scale, general-domain dataset is needed for knowledge graph-to-text generation . data collection is expensive and data-intensive, making it difficult to get good annotation .
Approach:	They propose to use a large-scale, general-domain dataset to generate unsupervised text from knowledge graphs.
Outcome:	The proposed dataset has 1.3M text and graph examples, and is a benchmark for future research . good annotation is expensive and difficult to get, and it's difficult to check quality .

Test of Time: Rethinking Temporal Signal of Benchmark Contamination (2026.acl-long)

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Terry Jingchen Zhang, Gopal Dev, Ning Wang, Max Obreiter, Punya Syon Pandey, Keenan Samway, Wenyuan Jiang, Yinya Huang, Bernhard Schölkopf, Mrinmaya Sachan, Zhijing Jin

Challenge:	Existing work on benchmarks containing publicly available information has been interpreted as a temporal signal for benchmark contamination.
Approach:	They show that LLM-transformed questions can produce remarkably different temporal patterns compared to fill-in-the-blank questions directly retrieved from the very same documents.
Outcome:	The proposed model can produce different temporal patterns compared to fill-in-the-blank questions retrieved from the same documents.

Slangvolution: A Causal Analysis of Semantic Change and Frequency Dynamics in Slang (2022.acl-long)

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Daphna Keidar, Andreas Opedal, Zhijing Jin, Mrinmaya Sachan

Challenge:	a recent study suggests that language evolution is a diachronic process, but no causal analysis is performed to verify these claims.
Approach:	They analyze the semantic change and frequency shift of slang words and compare them to those of standard, nonslang terms.
Outcome:	The proposed model shows that slang has smaller semantic change but larger frequency shifts over time.

The Reasoning-Memorization Interplay in Language Models Is Mediated by a Single Direction (2025.findings-acl)

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Yihuai Hong, Meng Cao, Dian Zhou, Lei Yu, Zhijing Jin

How Robust Are Router-LLMs? Analysis of the Fragility of LLM Routing Capabilities (2026.eacl-long)

Challenge:	Large language models excel on a variety of reasoning benchmarks, but struggle to generalize to unseen questions due to over-reliance on memorized training examples.
Approach:	They propose to identify a set of linear features in the model’s residual stream that govern the balance between genuine reasoning and memory recall.
Outcome:	The proposed model can be manipulated to activate the most relevant problem-solving capabilities during answer generation.

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Aly M. Kassem, Bernhard Schölkopf, Zhijing Jin

The Odyssey of Commonsense Causality: From Foundational Benchmarks to Cutting-Edge Reasoning (2024.emnlp-main)

Challenge:	Large language model (LLM) routing has emerged as a promising solution to balancing computational costs and performance.
Approach:	They propose a framework that categorizes router performance across a broad spectrum of query types . large language models have revolutionized natural language processing .
Outcome:	The proposed framework categorizes router performance across a broad spectrum of query types . it integrates privacy and safety assessments to reveal hidden risks .

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Shaobo Cui, Zhijing Jin, Bernhard Schölkopf, Boi Faltings

Reasoning

Challenge:	Despite its significance, a systematic exploration of commonsense causality is lacking.
Approach:	They focus on taxonomies, benchmarks, acquisition methods, qualitative reasoning, and quantitative measurements in commonsense causality.
Outcome:	The proposed method synthesizes insights from over 200 representative articles and provides a practical guide for beginners.

A Causal Framework to Quantify the Robustness of Mathematical Reasoning with Language Models (2023.acl-long)

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Alessandro Stolfo, Zhijing Jin, Kumar Shridhar, Bernhard Schoelkopf, Mrinmaya Sachan

Robustness in NLP

Challenge:	Recent work shows that language models can rely on shallow patterns in problem description when generating a solution.
Approach:	They propose a framework which pins down the causal effect of various factors on the output solution.
Outcome:	The proposed framework improves robustness and sensitivity to direct interventions on a test bed of math word problems.

Differentially Private Language Models for Secure Data Sharing (2022.emnlp-main)

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Justus Mattern, Zhijing Jin, Benjamin Weggenmann, Bernhard Schoelkopf, Mrinmaya Sachan

Navigating Ethical Challenges in NLP: Hands-on strategies for students and researchers (2025.acl-tutorials)

Challenge:	a variety of deanonymization attacks allow the re-identification of individuals from tabular data.
Approach:	They propose to train a language model in a differentially private manner and sample data from it . they find that the model generates fluent textual datasets with privacy guarantees .
Outcome:	The proposed methods outperform direct classifiers with DP-SGD in the real-world.

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Luciana Benotti, Fanny Ducel, Karën Fort, Guido Ivetta, Zhijing Jin, Min-Yen Kan, Seunghun J. Lee, Minzhi Li, Margot Mieskes, Adriana Pagano

Co-DETECT: Collaborative Discovery of Edge Cases in Text Classification (2025.emnlp-demos)

Challenge:	This tutorial will equip participants with basic guidelines for thinking deeply about ethical issues . participants will gain practical experience on when to flag a paper for ethics review .
Approach:	This tutorial will equip participants with basic guidelines for thinking deeply about ethical issues . participants will gain practical experience on when to flag a paper for ethics review .
Outcome:	This tutorial will equip participants with basic guidelines for thinking deeply about ethical issues . participants will gain practical experience on when to flag a paper for ethics review .

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Chenfei Xiong, Jingwei Ni, Yu Fan, Vilém Zouhar, Donya Rooein, Lorena Calvo-Bartolomé, Alexander Miserlis Hoyle, Zhijing Jin, Mrinmaya Sachan, Markus Leippold, Dirk Hovy, Mennatallah El-Assady, Elliott Ash

Information Extraction & Text Mining

Challenge:	Social scientists often need to develop codebooks that can be reliable but require significant human effort.
Approach:	They propose a mixed-initiative annotation framework that integrates human expertise with automatic annotation guided by large language models.
Outcome:	The proposed framework integrates human expertise with automatic annotation guided by large language models.

When Do Language Models Endorse Limitations on Human Rights Principles? (2026.findings-eacl)

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Keenan Samway, Miu Nicole Takagi, Rada Mihalcea, Bernhard Schölkopf, Ilias Chalkidis, Daniel Hershcovich, Zhijing Jin

CORE: Measuring Multi-Agent LLM Interaction Quality under Game-Theoretic Pressures (2026.eacl-long)

Challenge:	a recent study evaluated how large language models navigate trade-offs involving the Universal Declaration of Human Rights.
Approach:	They evaluate how large language models navigate trade-offs involving the Universal Declaration of Human Rights (UDHR) they use 1,152 synthetically generated scenarios across 24 rights articles and eight languages .
Outcome:	The proposed models accept limiting economic, social, and cultural rights more often than political and civil rights, the authors show . their models show significant cross-linguistic variation with elevated endorsement rates of rights-limiting actions in Chinese and Hindi compared to English or Romanian .

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Punya Syon Pandey, Yongjin Yang, Jiarui Liu, Zhijing Jin

Robustness in NLP

Challenge:	Game-theoretic interactions between agents with large language models (LLMs) have revealed many emergent capabilities, yet the linguistic diversity of these interactions has not been quantified.
Approach:	They propose a metric to quantify the effectiveness of language use within multi-agent systems across different game-theoretic interactions.
Outcome:	The proposed metric measures the effectiveness of language use within multi-agent systems across game-theoretic interactions.

Mining the Cause of Political Decision-Making from Social Media: A Case Study of COVID-19 Policies across the US States (2021.findings-emnlp)

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Zhijing Jin, Zeyu Peng, Tejas Vaidhya, Bernhard Schoelkopf, Rada Mihalcea

Sentiment Analysis

Challenge:	Existing studies on political responsiveness focus on long-term policies collected over decades . recent COVID-19 pandemic has given rise to a new political phenomenon, where political leaders make frequent short-term decisions on the same controlled topic.
Approach:	They propose to use Twitter data to classify the sentiments toward governors of each state and conduct controlled studies and comparisons.
Outcome:	The proposed model focuses on the COVID-19 pandemic, where political leaders make frequent short-term decisions on the same controlled topic.

Membership Inference Attacks against Language Models via Neighbourhood Comparison (2023.findings-acl)

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Justus Mattern, Fatemehsadat Mireshghallah, Zhijing Jin, Bernhard Schoelkopf, Mrinmaya Sachan, Taylor Berg-Kirkpatrick

Robustness in NLP

Challenge:	Existing membership inference attacks aim to predict whether a data sample was present in training data of a machine learning model.
Approach:	They propose to compare model scores to neighbour texts to eliminate access to training data by comparing model scores with a given sample.
Outcome:	The proposed attacks outperform reference-based attacks with perfect knowledge of the training data distribution and outperformed reference-free attacks with imperfect knowledge.

Beyond Good Intentions: Reporting the Research Landscape of NLP for Social Good (2023.findings-emnlp)

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Fernando Adauto, Zhijing Jin, Bernhard Schölkopf, Tom Hope, Mrinmaya Sachan, Rada Mihalcea

Challenge:	Recent advances in natural language processing (NLP) have created a vast number of applications that are aimed at social good applications.
Approach:	They propose a dataset with three tasks that can help identify NLP4SG papers and characterize the NLP landscape by: (1) identifying the papers that address a social problem, (2) mapping them to the corresponding UN Sustainable Development Goals, and (3) identifying their methods.
Outcome:	The proposed dataset can help identify NLP4SG papers and characterize the NLP landscape by: (1) identifying the papers that address a social problem, (2) mapping them to the corresponding UN Sustainable Development Goals (SDGs), and (3) identifying their methods.

Implicit Personalization in Language Models: A Systematic Study (2024.findings-emnlp)

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Zhijing Jin, Nils Heil, Jiarui Liu, Shehzaad Dhuliawala, Yahang Qi, Bernhard Schölkopf, Rada Mihalcea, Mrinmaya Sachan

Reasoning

Challenge:	Existing studies have focused on the implicit personalization problem, but no unified framework exists to study it.
Approach:	They propose a mathematical formulation and a moral reasoning framework to study the phenomenon of Implicit Personalization (IP) they propose 'direct intervention' to estimate causal effect of mediator variable that cannot be directly intervened upon.
Outcome:	The proposed method estimates the causal effect of a mediator variable that cannot be directly intervened upon.

Analyzing the Role of Semantic Representations in the Era of Large Language Models (2024.naacl-long)

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Zhijing Jin, Yuen Chen, Fernando Gonzalez Adauto, Jiarui Liu, Jiayi Zhang, Julian Michael, Bernhard Schölkopf, Mona Diab

Uncovering Hidden Correctness in LLM Causal Reasoning via Symbolic Verification (2026.eacl-long)

Challenge:	Existing studies show the benefits of semantic representations in NLP tasks . Existing work using AMR is concerned with trainable models .
Approach:	They propose an AMR-driven chain-of-thought prompting method that uses AMR . they propose to use it to predict which input examples AMR may help or hurt on .
Outcome:	The proposed method hurts performance more than it helps on five different tasks.

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Paul He, Yinya Huang, Mrinmaya Sachan, Zhijing Jin

Reasoning

Challenge:	Large language models (LLMs) are increasingly being applied to causal reasoning tasks.
Approach:	They propose a symbolic verification framework that checks whether LLM-generated causal expressions are derivable from a given causal graph using do-calculus and probability theory.
Outcome:	The proposed framework can recover correct answers that would otherwise be marked incorrect due to superficial differences.

NLP for Social Good: A Survey and Outlook of Challenges, Opportunities and Responsible Deployment (2026.eacl-long)

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Antonia Karamolegkou, Angana Borah, Eunjung Cho, Sagnik Ray Choudhury, Martina Galletti, Pranav Gupta, Oana Ignat, Priyanka Kargupta, Neema Kotonya, Hemank Lamba, Sun-Joo Lee, Arushi Mangla, Ishani Mondal, Fatima Zahra Moudakir, Deniz Nazar, Poli Nemkova, Dina Pisarevskaya, Naquee Rizwan, Nazanin Sabri, Keenan Samway, Dominik Stammbach, Anna Steinberg Schulten, David Tomás, Steven R Wilson, Bowen Yi, Jessica H Zhu, Arkaitz Zubiaga, Anders Søgaard, Alexander Fraser, Zhijing Jin, Rada Mihalcea, Joel R. Tetreault, Daryna Dementieva

IMaT: Unsupervised Text Attribute Transfer via Iterative Matching and Translation (D19-1)

Challenge:	This paper surveys work in "NLP for Social Good" across nine domains relevant to global development and risk agendas.
Approach:	This paper analyzes work in "NLP for Social Good" across nine domains relevant to global development and risk agendas.
Outcome:	The paper analyzes work in "NLP for Social Good" across nine domains relevant to global development and risk agendas.

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Zhijing Jin, Di Jin, Jonas Mueller, Nicholas Matthews, Enrico Santus

Text Generation

Challenge:	Existing approaches to rewrite sentences with certain attributes are difficult and often result in poor content-preservation and ungrammaticality.
Approach:	They propose a method that uses a sequence-to-sequence model to learn attribute transfer . existing approaches try to explicitly disentangle content and attribute information .
Outcome:	The proposed method outperforms complex state-of-the-art systems by a large margin in sentiment modification and formality transfer tasks.

CausalNLP Tutorial: An Introduction to Causality for Natural Language Processing (2022.emnlp-tutorials)

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Zhijing Jin, Amir Feder, Kun Zhang

Challenge:	Establishing causal relationships is a fundamental goal of scientific research . lack of clear definitions, notations, benchmark datasets, and challenges remains .
Approach:	They introduce the fundamentals of causal discovery and causal effect estimation to the natural language processing audience and provide an overview of causal perspectives to NLP problems.
Outcome:	This tutorial introduces the fundamentals of causal discovery and causal effect estimation to the natural language processing audience and provides an overview of causal perspectives to NLP problems.

GraphIE: A Graph-Based Framework for Information Extraction (N19-1)

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Yujie Qian, Enrico Santus, Zhijing Jin, Jiang Guo, Regina Barzilay

Information Extraction & Text Mining

Challenge:	Most modern Information Extraction (IE) systems are implemented as sequential taggers and model local dependencies.
Approach:	They propose a framework that operates over a graph representing a broad set of dependencies between textual units.
Outcome:	The proposed framework outperforms the state-of-the-art sequence tagging model on three different tasks.

Revealing Hidden Mechanisms of Cross-Country Content Moderation with Natural Language Processing (2025.findings-acl)

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Neemesh Yadav, Jiarui Liu, Francesco Ortu, Roya Ensafi, Zhijing Jin, Rada Mihalcea

Has It All Been Solved? Open NLP Research Questions Not Solved by Large Language Models (2024.lrec-main)

Challenge:	Existing knowledge on how and why NLP methods make content moderation decisions is limited . authors examine how and when to use LLMs in content modeation .
Approach:	They use Shapley values and LLM-guided explanations to reverse-engineer content moderation decisions across countries.
Outcome:	The proposed methods show that they reverse-engineer content moderation decisions across countries and over time.

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Oana Ignat, Zhijing Jin, Artem Abzaliev, Laura Biester, Santiago Castro, Naihao Deng, Xinyi Gao, Aylin Ece Gunal, Jacky He, Ashkan Kazemi, Muhammad Khalifa, Namho Koh, Andrew Lee, Siyang Liu, Do June Min, Shinka Mori, Joan C. Nwatu, Veronica Perez-Rosas, Siqi Shen, Zekun Wang, Winston Wu, Rada Mihalcea