Papers by Shangbin Feng
CodeTaxo: Enhancing Taxonomy Expansion with Limited Examples via Code Language Prompts (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing taxonomies are mainly constructed by experts or through crowd-sourcing, making the process time-consuming, labor-intensive, and restricted in coverage. |
| Approach: | They propose a method that leverages large language models to capture taxonomic structure . existing taxonomies are mainly constructed by experts or through crowd-sourcing . |
| Outcome: | Experiments on five real-world domains show that CodeTaxo outperforms state-of-the-art methods. |
Detecting Spoilers in Movie Reviews with External Movie Knowledge and User Networks (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing models focus on the textual content of the review, while spoiler detection requires putting the review into the context of facts and knowledge regarding movies. |
| Approach: | They propose a network-based spoiler detection model that takes into account external knowledge about movies and user activities on movie review platforms. |
| Outcome: | The proposed model takes into account external knowledge about movies and user activities on movie review platforms while incorporating user networks. |
Stumbling Blocks: Stress Testing the Robustness of Machine-Generated Text Detectors Under Attacks (2024.acl-long)
Copied to clipboard
| Challenge: | Existing studies on this topic focus on the robustness of specific detectors or particular attack methods. |
| Approach: | They stress test the detectors’ robustness to malicious attacks under realistic scenarios using LLMs and metric-based detectors. |
| Outcome: | The proposed methods are based on a set of LLM-based models and their performance is compared under different budget levels. |
BotPercent: Estimating Bot Populations in Twitter Communities (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to bot detection are agnostic to social environments the bots operate in . however, standard approaches are not a good fit for the social environments they operate in. |
| Approach: | They propose a method that estimates the percentage of Twitter bots given a community . they use Twitter bot detection datasets and feature-, text-, and graph-based models adjusted to a particular community based on Twitter . |
| Outcome: | The proposed method achieves state-of-the-art in community-level Twitter bot detection across balanced and imbalanced class distribution settings. |
Biased LLMs can Influence Political Decision-Making (2025.acl-long)
Copied to clipboard
Jillian Fisher, Shangbin Feng, Robert Aron, Thomas Richardson, Yejin Choi, Daniel W Fisher, Jennifer Pan, Yulia Tsvetkov, Katharina Reinecke
| Challenge: | Recent studies have found that biased LLMs can influence decisions in areas such as medical classifications and educational hiring. |
| Approach: | They conducted two interactive experiments on partisan bias in large language models while completing tasks with either a biased liberal, biased conservative, or unbiased control model. |
| Outcome: | The results show that prior knowledge of AI is weakly correlated with a reduction of the bias, suggesting that AI education can be crucial for mitigating bias effects. |
Data Swarms: Optimizable Generation of Synthetic Evaluation Data (2026.findings-acl)
Copied to clipboard
| Challenge: | Extensive experiments demonstrate that Data Swarms outperforms eight data generation baselines across five evaluation objectives. |
| Approach: | They propose an algorithm to optimize the generation of synthetic evaluation data and advance quantitative desiderata of LLM evaluation. |
| Outcome: | The proposed algorithm outperforms baseline evaluations and Adversarial Swarms generates harder data while learning from such data. |
Modular Pluralism: Pluralistic Alignment via Multi-LLM Collaboration (2024.emnlp-main)
Copied to clipboard
Shangbin Feng, Taylor Sorensen, Yuhan Liu, Jillian Fisher, Chan Young Park, Yejin Choi, Yulia Tsvetkov
| Challenge: | Existing alignment paradigms for large language models learn an averaged human preference and struggle to model diverse preferences across cultures, demographics, and communities. |
| Approach: | They propose a modular framework that "plugs" into a base LLM a pool of smaller but specialized community LMs where models collaborate in distinct modes to support three modes of pluralism: Overton, steerable, and distributional. |
| Outcome: | The proposed framework “plugs into” a base LLM a pool of smaller but specialized community LMs, where models collaborate in distinct modes to support three modes of pluralism: Overton, steerable, and distributional. |
DELL: Generating Reactions and Explanations for LLM-Based Misinformation Detection (2024.findings-acl)
Copied to clipboard
| Challenge: | Large language models are limited by challenges in factuality and hallucinations to be directly employed off-the-shelf for judging the veracity of news articles. |
| Approach: | They propose to integrate large language models into the news pipeline by generating news reactions and generating proxy tasks. |
| Outcome: | The proposed model outperforms state-of-the-art baselines by 16.8% in macro f1-score on seven datasets with three LLMs. |
P3Sum: Preserving Author’s Perspective in News Summarization with Diffusion Language Models (2024.naacl-long)
Copied to clipboard
Yuhan Liu, Shangbin Feng, Xiaochuang Han, Vidhisha Balachandran, Chan Young Park, Sachin Kumar, Yulia Tsvetkov
| Challenge: | Existing summarization systems alter the political opinions and stances of news articles in more than 50% of summaries, misrepresenting the intent and perspectives of the authors. |
| Approach: | They propose a model-based summarization approach controlled by political perspective classifiers that preserves the political stance of a generated summary. |
| Outcome: | The proposed model outperforms state-of-the-art summarization systems and large language models by up to 13.7% in terms of success rate of stance preservation, with competitive performance on standard metrics of summarizing quality. |
What Does the Bot Say? Opportunities and Risks of Large Language Models in Social Media Bot Detection (2024.acl-long)
Copied to clipboard
| Challenge: | Social media bot detection has always been an arms race between advancements in machine learning and adversarial bot strategies to evade detection. |
| Approach: | They propose a mixture-of-heterogeneous-experts framework to divide and conquer diverse user information modalities and propose LLM-guided manipulation of user textual and structured information to evade detection. |
| Outcome: | The proposed framework outperforms state-of-the-art baselines on 1,000 annotated examples while bringing down existing detectors by 29.6% and harming calibration and reliability of bot detection systems. |
BIC: Twitter Bot Detection with Text-Graph Interaction and Semantic Consistency (2023.acl-long)
Copied to clipboard
Zhenyu Lei, Herun Wan, Wenqian Zhang, Shangbin Feng, Zilong Chen, Jundong Li, Qinghua Zheng, Minnan Luo
| Challenge: | Existing methods to identify bots rely on text or networks alone . text-graph interactions and semantic consistency are essential improvements to combat bot evolution. |
| Approach: | They propose to combine text-graph interaction and semantic Consistency to model Twitter bots' behavior based on attention weights and a text-graphic interaction module to enable information exchange across modalities in the learning process. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on two widely adopted datasets and the results are consistent with previous work. |
KCD: Knowledge Walks and Textual Cues Enhanced Political Perspective Detection in News Media (2022.naacl-main)
Copied to clipboard
| Challenge: | Existing approaches focus on leveraging textual content to identify stances, while they fail to reason with background knowledge or leverage the rich semantic and syntactic textual labels in news articles. |
| Approach: | They propose a political perspective detection approach that leverages news text to enable multi-hop knowledge reasoning and incorporates textual cues as paragraph-level labels. |
| Outcome: | The proposed approach outperforms state-of-the-art methods on two benchmark datasets. |
Can LLM Graph Reasoning Generalize beyond Pattern Memorization? (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies seek to enhance the graph reasoning capabilities of Large Language Models (LLMs) by specialized instruction tuning. |
| Approach: | They propose to evaluate LLM graph reasoning generalization using in-distribution settings . they propose to use three strategies to improve LLM generalization . |
| Outcome: | The proposed benchmark evaluates LLM graph reasoning generalization with in-distribution settings only . it shows that LLMs struggle to generalize across reasoning and real-world patterns . |
KALM: Knowledge-Aware Integration of Local, Document, and Global Contexts for Long Document Understanding (2023.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to infuse knowledge graphs with pre-trained LMs are limited by the input sequence length. |
| Approach: | They propose a language model that leverages knowledge in local, document-level, and global contexts for long document understanding. |
| Outcome: | The proposed model achieves state-of-the-art on three long document understanding tasks across 6 datasets/settings. |
From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models (2023.acl-long)
Copied to clipboard
| Challenge: | Hundreds of studies have highlighted ethical issues in NLP models . |
| Approach: | They propose to measure media biases in LMs trained on diverse data sources . they focus on hate speech and misinformation detection . |
| Outcome: | The proposed methods quantify the fairness of downstream NLP models trained on politically biased LMs. |
When One LLM Drools, Multi-LLM Collaboration Rules (2026.acl-long)
Copied to clipboard
Shangbin Feng, Wenxuan Ding, Alisa Liu, Zifeng Wang, Weijia Shi, Yike Wang, Shannon Zejiang Shen, Xiaochuang Han, Hunter Lang, Chen-Yu Lee, Tomas Pfister, Yejin Choi, Yulia Tsvetkov
| Challenge: | a single general-purpose LLM is not enough to produce a reliable output, argues this paper . a multi-LLM collaboration approach addresses reliability, democratization, and pluralism . |
| Approach: | They argue that a single general-purpose LLM is not enough to produce a reliable output . they organize existing multi-LLM collaboration methods into a hierarchy based on access and information exchange . |
| Outcome: | The proposed method addresses reliability, democratization, and pluralism challenges a single LLM fails to produce a reliable output. |
FactKB: Generalizable Factuality Evaluation using Language Models Enhanced with Factual Knowledge (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing factuality evaluation models are not robust, especially with respect to entity and relation errors in new domains. |
| Approach: | They propose a new approach to factuality evaluation that is generalizable across domains . they propose entities-specific facts, facts extracted from external knowledge bases and facts constructed compositionally through knowledge base walks. |
| Outcome: | The proposed model achieves state-of-the-art on two in-domain news summarization benchmarks and on three out-of domain scientific literature datasets. |
PAR: Political Actor Representation Learning with Social Context and Expert Knowledge (2022.emnlp-main)
Copied to clipboard
Shangbin Feng, Zhaoxuan Tan, Zilong Chen, Ningnan Wang, Peisheng Yu, Qinghua Zheng, Xiaojun Chang, Minnan Luo
| Challenge: | Existing approaches focus on textual data and voting records to induce political actors' stances. |
| Approach: | They propose a Political Actor Representation learning framework that leverages social context and expert knowledge to model ideological stances. |
| Outcome: | The proposed framework improves political text understanding and improves roll call vote prediction and political perspective detection. |
Teaching LLMs to Abstain across Languages via Multilingual Feedback (2024.emnlp-main)
Copied to clipboard
Shangbin Feng, Weijia Shi, Yike Wang, Wenxuan Ding, Orevaoghene Ahia, Shuyue Stella Li, Vidhisha Balachandran, Sunayana Sitaram, Yulia Tsvetkov
| Challenge: | Existing studies on LLM abstention focus on English, but they show that it can reduce the accuracy of the model by 20.5% . |
| Approach: | They propose to teach LLMs to abstain in the face of knowledge gaps by generating multiple feedback items in related languages. |
| Outcome: | Extensive experiments show that the proposed approach outperforms baselines and achieves 9.2% improvement for low-resource languages. |
Bidirectional LMs are Better Knowledge Memorizers? A Benchmark for Real-world Knowledge Injection (2026.acl-long)
Copied to clipboard
Yuwei Zhang, Wenhao Yu, Shangbin Feng, Yifan Zhu, Letian Peng, Jayanth Srinivasa, Gaowen Liu, Jingbo Shang
| Challenge: | Existing knowledge injection benchmarks for large language models lack standardized testing grounds. |
| Approach: | They propose a knowledge injection benchmark that leverages recently-added and expert-curated facts from Wikipedia’s “Did You Know...” entries. |
| Outcome: | The proposed framework improves reliability accuracy by 29.1%. |
Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing research has focused on enhancing graph reasoning capabilities of LLMs by supervised fine-tuning on synthetic graph data. |
| Approach: | They propose to unlock generalizable learning of graph with post-training alignment with synthetic graph data by aligning off-the-shelf LLMs and LLM fine-tuned on synthetic graphs. |
| Outcome: | The proposed algorithm improves on synthetic graph problems and out-of-domain tasks with implicit graph structures. |
Among Us: Measuring and Mitigating Malicious Contributions in Model Collaboration Systems (2026.acl-long)
Copied to clipboard
| Challenge: | Existing research is leveraging multiple language models with diverse skills and strengths to collaborate. |
| Approach: | They propose mitigation strategies to mitigate the impact of malicious models by employing external supervisors to disable/mask them out to reduce their influence. |
| Outcome: | The proposed mitigation strategies recover 95.31% of initial performance while making model collaboration systems fully resistant to malicious models remains an open question. |
Knowledge Crosswords: Geometric Knowledge Reasoning with Large Language Models (2024.findings-acl)
Copied to clipboard
Wenxuan Ding, Shangbin Feng, Yuhan Liu, Zhaoxuan Tan, Vidhisha Balachandran, Tianxing He, Yulia Tsvetkov
| Challenge: | Existing tasks and datasets assess LLM knowledge abilities mostly by focusing on atomic (e.g., open-domain QA) or linear (e-hop QA). |
| Approach: | They propose a geometric knowledge reasoning benchmark consisting of incomplete knowledge networks bounded by structured factual constraints where LLMs are tasked with inferring the missing facts to meet all constraints. |
| Outcome: | The proposed methods outperform baseline methods and are more robust towards problems in the hard subset. |