Papers by Jing Peng
Two Pathways to Truthfulness: On the Intrinsic Encoding of LLM Hallucinations (2026.acl-long)
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Wen Luo, Guangyue Peng, Wei Li, Shaohang Wei, Feifan Song, Liang Wang, Nan Yang, Xingxing Zhang, Jing Jin, Furu Wei, Houfeng Wang
| Challenge: | Previous work shows that large language models generate hallucinations, yet the origins and mechanisms of these signals remain unclear. |
| Approach: | They propose to validate and disentangle two different pathways for truthfulness cues . they also propose to use the same mechanism to derive self-contained evidence from the generated answer . |
| Outcome: | The proposed applications improve hallucination detection performance by integrating two different inputs. |
Knowledge Inheritance for Pre-trained Language Models (2022.naacl-main)
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Yujia Qin, Yankai Lin, Jing Yi, Jiajie Zhang, Xu Han, Zhengyan Zhang, Yusheng Su, Zhiyuan Liu, Peng Li, Maosong Sun, Jie Zhou
| Challenge: | Existing large-scale pre-trained language models are mainly trained from scratch individually, ignoring that many well-taught PLMs are available. |
| Approach: | They propose a pre-training framework called knowledge inheritance and propose auxiliary supervision to efficiently learn larger PLMs. |
| Outcome: | The proposed framework can be used to train large-scale language models with huge parameters and a large dataset can be adapted to domain adaptation and knowledge transfer. |
Code-Switched Text Synthesis in Unseen Language Pairs (2023.findings-acl)
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| Challenge: | Existing efforts on text synthesis for code-switching require training on code-witched texts in the target language pairs. |
| Approach: | They propose a model that synthesizes code-switched texts for language pairs absent from training data by adding an additional code-sharing module to a pre-trained machine translation model. |
| Outcome: | The proposed model synthesizes code-switched texts for language pairs lacking from training data. |
#HowYouTagTweets: Learning User Hashtagging Preferences via Personalized Topic Attention (2021.emnlp-main)
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| Challenge: | Existing methods based on latent topics cannot capture user interests and thus can't be used to predict how likely a user will post with a hashtag. |
| Approach: | They propose a personalized topic attention model that captures salient contents to personalize hashtag contexts by predicting how likely a user will post with a hashtag. |
| Outcome: | The proposed model significantly outperforms the state-of-the-art recommendation approach without exploiting latent topics. |
ExPUNations: Augmenting Puns with Keywords and Explanations (2022.emnlp-main)
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Jiao Sun, Anjali Narayan-Chen, Shereen Oraby, Alessandra Cervone, Tagyoung Chung, Jing Huang, Yang Liu, Nanyun Peng
| Challenge: | Puns add the challenge of fusing commonsense and world knowledge with the ability to interpret lexical-semantic ambiguity. |
| Approach: | They propose to augment existing datasets with detailed crowdsourced annotations of puns, keywords and fine-grained funniness ratings to challenge current models' ability to understand and generate humor. |
| Outcome: | The proposed tasks include explanation generation to aid with pun classification and keyword-conditioned pun generation to challenge state-of-the-art models' ability to understand and generate humor. |
Designing a Russian Idiom-Annotated Corpus (L18-1)
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| Challenge: | a pilot experiment using the idiom-annotated corpus of Russian is described . corpora that could be used for training idiomatic classifiers are scarce, especially if one turns to other languages. |
| Approach: | They describe the development of an idiom-annotated corpus of Russian . the corpus is compiled from freely available online resources . |
| Outcome: | The proposed corpus is based on an online corpus of Russian texts . it is available for research purposes and can be used for linguistic studies and pedagogy . |
Soft Knowledge Prompt: Help External Knowledge Become a Better Teacher to Instruct LLM in Knowledge-based VQA (2024.acl-long)
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| Challenge: | Recent research focuses on improving prediction performance and reliability of LLM. |
| Approach: | They propose a method to actively extract valuable information from the knowledge to produce a latent vector as a soft prompt, which is fused with the image embedding to form a knowledge-enhanced context to instruct LLM. |
| Outcome: | The proposed method improves performance on knowledge-based VQA benchmarks. |
LA-UCL: LLM-Augmented Unsupervised Contrastive Learning Framework for Few-Shot Text Classification (2024.lrec-main)
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| Challenge: | Experimental results show that our model exceeds the baseline models due to the lack of cognitive ability. |
| Approach: | They propose a LLM-Augmented Unsupervised Contrastive Learning Framework which introduces a cognition-enabled Large Language Model (LLM) for efficient data augmentation and presents corresponding contrastive learning strategies. |
| Outcome: | The proposed model exceeds baseline models on six datasets. |
Bi-directional CognitiveThinking Network for Machine Reading Comprehension (2020.coling-main)
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| Challenge: | Existing methods for reading comprehension are still in their infancy at the level of cognitive intelligence. |
| Approach: | They propose a bi-directional cognitive knowledge framework to simulate reverse thinking and inertial thinking in the brain to answer questions. |
| Outcome: | The proposed framework shows that bi-directional knowledge helps the QA task. |
FEED PETs: Further Experimentation and Expansion on the Disambiguation of Potentially Euphemistic Terms (2023.starsem-1)
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Patrick Lee, Iyanuoluwa Shode, Alain Trujillo, Yuan Zhao, Olumide Ojo, Diana Plancarte, Anna Feldman, Jing Peng
| Challenge: | Existing work on euphemism disambiguation tasks has focused on transformers . euphorias are expressions that soften the message they convey, therefore dictionary-based approaches are ineffective . |
| Approach: | They propose to annotate PETs for vagueness and use transformers to classify PETs . they perform euphemism disambiguation experiments in three different languages . |
| Outcome: | The proposed models perform well in English euphemism disambiguation task . preliminary results will be used to launch future work . |
ChatSOP: An SOP-Guided MCTS Planning Framework for Controllable LLM Dialogue Agents (2025.acl-long)
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Zhigen Li, Jianxiang Peng, Yanmeng Wang, Yong Cao, Tianhao Shen, Minghui Zhang, Linxi Su, Shang Wu, Yihang Wu, YuQian Wang, Ye Wang, Wei Hu, Jianfeng Li, Shaojun Wang, Jing Xiao, Deyi Xiong
| Challenge: | Existing models that use Large Language Models (LLMs) show superior performance in various tasks, but lack of controllability leads to unfocused conversations or task failure. |
| Approach: | They propose a standard operating procedure (SOP) framework to regulate dialogue flow by integrating Chain of Thought reasoning and supervised fine-tuning for SOP prediction. |
| Outcome: | The proposed method achieves a 27.95% improvement in action accuracy compared to baseline models based on GPT-3.5 and also shows notable gains for open-source models. |
Graph Ensemble Learning over Multiple Dependency Trees for Aspect-level Sentiment Classification (2021.naacl-main)
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| Challenge: | Recent work on aspect-level sentiment classification has shown that syntactic information is effective in capturing long-range syntaktic relations that are obscure from the surface form. |
| Approach: | They propose a graph ensemble technique that integrates syntactic structures with GNNs to better leverage syntaktic information in the face of parsing errors. |
| Outcome: | The proposed model outperforms models with single dependency tree and beats other models without adding model parameters. |
Value Compass Benchmarks: A Comprehensive, Generative and Self-Evolving Platform for LLMs’ Value Evaluation (2025.acl-demo)
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Jing Yao, Xiaoyuan Yi, Shitong Duan, Jindong Wang, Yuzhuo Bai, Muhua Huang, Yang Ou, Scarlett Li, Peng Zhang, Tun Lu, Zhicheng Dou, Maosong Sun, James Evans, Xing Xie
| Challenge: | Current evaluation methods for large language models face two key challenges: 1. evaluation validity and 2. Result interpretation reduce the pluralistic and incommensurable values to one-dimensional scores. |
| Approach: | They propose a platform for comprehensive value diagnosis of large language models (LLMs) that provides a generative evaluation paradigm that automatically creates real-world test items co-evolving with ever-advancing LLMs. |
| Outcome: | The proposed platform provides a framework for comprehensive value diagnosis of large language models (LLMs) with fine-grained scores and case studies across 27 value dimensions for 33 leading LLMs, customized comparisons, and visualized analysis of LLM’s alignment with cultural values. |
HOSMEL: A Hot-Swappable Modularized Entity Linking Toolkit for Chinese (2022.acl-demo)
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| Challenge: | Existing studies have explored the use of entity linking (EL) in downstream tasks. |
| Approach: | They propose a modularized entity linking toolkit for easy task adaptation. |
| Outcome: | The proposed toolkit achieves significantly better accuracy and less time and spaceconsumption than existing methods. |
MEDs for PETs: Multilingual Euphemism Disambiguation for Potentially Euphemistic Terms (2024.findings-eacl)
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Patrick Lee, Alain Chirino Trujillo, Diana Cuevas Plancarte, Olumide Ojo, Xinyi Liu, Iyanuoluwa Shode, Yuan Zhao, Anna Feldman, Jing Peng
| Challenge: | Euphemisms are a linguistic device used to soften or neutralize language that may otherwise be harsh or awkward to state directly. |
| Approach: | They train a multilingual transformer model to disambiguate potentially euphemistic terms in multilingual and cross-lingual settings. |
| Outcome: | The proposed model performs better than monolingual models on the disambiguation task compared to monolingual ones in multilingual and cross-lingual settings. |
Capability Decomposition for Unified Information Extraction via Hierarchical Mixture-of-Experts (2026.acl-long)
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| Challenge: | Existing methods for IE tasks suffer from inconsistent schema representation and implicitly intermediate reasoning . UC-UIE adopts a low-rank adapted hierarchical Mixture-of-Experts adapter for UIE tasks . |
| Approach: | They propose a framework that decomposes IE reasoning into three universal capabilities . UC-UIE adopts a low-rank Adaptation adapter to fine-tune LLMs for IE tasks . |
| Outcome: | The proposed framework outperforms full-parameter tuning methods with 1.24% trainable parameters and outperformed existing methods in generalization and interpretability. |
Rethink Rumor Detection in the Era of LLMs: A Review (2025.findings-emnlp)
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| Challenge: | rumor detection has been reshaped by large language models (LLMs) this paper proposes a Cognition-Interaction-Behavior (CIB) framework for rumour detection based on collective intelligence . |
| Approach: | They propose a Cognition-Interaction-Behavior framework for rumor detection based on collective intelligence and explore synergistic relationship between LLMs and collective intelligence in rumour governance. |
| Outcome: | The proposed framework unifies existing methods and reveals synergistic relationship between LLMs and collective intelligence in rumor governance. |
PragmatiCQA: A Dataset for Pragmatic Question Answering in Conversations (2023.findings-acl)
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| Challenge: | Mars? - PragmatiCQA |
| Approach: | Mars? - The Paper . |
| Outcome: | The proposed dataset features 6873 QA pairs that explores pragmatic reasoning in conversations over a diverse set of topics. |
Grammar-based Decoding for Improved Compositional Generalization in Semantic Parsing (2023.findings-acl)
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| Challenge: | Sequence-to-sequence (seq2sequ) models have been successful in semantic parsing tasks but struggle on out-of-distribution data. |
| Approach: | They propose to use a large-scale dialogue dataset to evaluate compositional generalization of semantic parsing. |
| Outcome: | The proposed model outperforms BART- and T5-based models on the SMCalflow-CS dataset on the zero-shot learning task. |
Self-supervised Cross-modal Pretraining for Speech Emotion Recognition and Sentiment Analysis (2022.findings-emnlp)
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| Challenge: | Existing approaches to multimodal speech emotion recognition and sentiment analysis have not improved results due to their relatively simple fusion mechanisms and lack of proper cross-modal pretraining. |
| Approach: | They propose a deep-fused audio-text bi-modal transformer with carefully designed cross-modal fusion mechanism and stage-wise cross-mod pretraining scheme to facilitate cross-modulation. |
| Outcome: | The proposed method exceeds benchmarks on public IEMOCAP emotion and CMU-MOSEI sentiment datasets by a large margin. |
How to Make Large Language Models Generate 100% Valid Molecules? (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) can learn to perform a wide range of tasks, but generating valid molecules using representations like SMILES is challenging in few-shot settings. |
| Approach: | They propose a language framework that converts invalid SMILES to SELFIES and LLMs as post-hoc correctors to ensure that the molecules generated by LLM are 100% valid. |
| Outcome: | The proposed model performs worse with SELFIES than with SMILES and improves on other metrics. |
Deciphering Rumors: A Multi-Task Learning Approach with Intent-aware Hierarchical Contrastive Learning (2024.emnlp-main)
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| Challenge: | Social networks are rife with noise and misleading information, presenting multifaceted challenges for rumor detection. |
| Approach: | They propose a new multi-task learning framework that mines latent intentions and rumor semantic features . they propose to use event-level and intent-level strategies to establish cognitive anchors . |
| Outcome: | The proposed framework improves the effectiveness of rumor detection and addresses the challenges present in the field. |
Towards Generating Controllable and Solvable Geometry Problem by Leveraging Symbolic Deduction Engine (2025.acl-industry)
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Zhuoxuan Jiang, Tianyang Zhang, Peiyan Peng, Jing Chen, Yinong Xun, Haotian Zhang, Lichi Li, Yong Li, Shaohua Zhang
| Challenge: | Compared to math word problems, geometry problems emphasize multi-modal formats and the translation between informal and formal languages. |
| Approach: | They propose a symbolic deduction engine-based geometry problem generation framework that leverages a symbolic deduction engine to generate geometry problems. |
| Outcome: | The proposed method avoids inherent biases in translating natural language into formal language and guarantees to control the generated problems in terms of knowledge points and difficulties by an elaborate checking function. |
LLM-SLM Collaborative Framework of Idiomatic Expression Generation (2026.acl-long)
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| Challenge: | Existing methods for idiomatic expression generation lack parallel data and manual annotations. |
| Approach: | They propose an iterative LLM-SLM collaborative framework that replaces human supervision for idiomatic expression data generation. |
| Outcome: | The proposed framework outperforms DeepSeek-R1 in Chinese Idiom Polishing with a 25.2% improvement in accuracy. |
HistLens: Mapping Idea Change across Concepts and Corpora (2026.acl-long)
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| Challenge: | Existing approaches to diachronic semantics and discourse analysis focus on a single concept or corpus, argues a new paper. |
| Approach: | They propose a framework for multi-concept, multi-corpus conceptual-history analysis that decomposes concept representations into interpretable features and tracks activation dynamics over time and across sources. |
| Outcome: | The proposed framework decomposes concept representations into interpretable features and tracks their activation dynamics over time and across sources. |
On the Role of Discriminative Models in Generative Relation Extraction (2026.acl-long)
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| Challenge: | Existing methods for relation extraction (RE) are discriminative and generative . previous studies show that discriminative models can support generative RE . |
| Approach: | They propose a framework that leverages discriminative models to produce a top-k set of candidate relations and integrates this knowledge into generative models via in-context or prompt learning. |
| Outcome: | The proposed framework achieves state-of-the-art on five widely used RE benchmarks. |
LLMs Deceive Unintentionally: Emergent Misalignment in Dishonesty from Misaligned Samples to Biased Human-AI Interactions (2026.findings-acl)
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| Challenge: | Existing studies have shown that LLMs finetuned on incorrect completions can exhibit harmful behaviors, which is called emergent misalignment. |
| Approach: | They investigate whether LLMs finetuned on incorrect completions can exhibit harmful behaviors . they find that 1% of misalignment data is sufficient to decrease honest behavior . |
| Outcome: | The proposed model can be misaligned on errors within narrow domains to exhibit harmful behaviors . the proposed model is able to exhibit dishonest behavior with only 10% biased user population . |
Improving Time Sensitivity for Question Answering over Temporal Knowledge Graphs (2022.acl-long)
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| Challenge: | Temporal knowledge graphs record entity relations and when they occur in time . previous work fails to address time-related challenges such as time-order issues . paper proposes time-sensitive question answering framework to address these problems . |
| Approach: | They propose a time-sensitive question answering framework that uses temporal KGs to answer natural language questions. |
| Outcome: | The proposed framework outperforms the state-of-the-art on a new benchmark for question answering over temporal knowledge graphs. |
BASES: Large-scale Web Search User Simulation with Large Language Model based Agents (2024.findings-emnlp)
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| Challenge: | Existing research on web search rely on real-user experiments, which can be costly to scale up. |
| Approach: | They propose a user simulation framework with LLM-based agents that can generate unique user profiles at scale. |
| Outcome: | The proposed framework can generate unique user profiles at scale, leading to diverse search behaviors. |
Unsupervised Melody-to-Lyrics Generation (2023.acl-long)
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Yufei Tian, Anjali Narayan-Chen, Shereen Oraby, Alessandra Cervone, Gunnar Sigurdsson, Chenyang Tao, Wenbo Zhao, Tagyoung Chung, Jing Huang, Nanyun Peng
| Challenge: | Existing methods for automatic melody-to-lyric generation are limited due to the limited amount of melody-lyrical aligned data. |
| Approach: | They propose a method for automatic melody-to-lyric generation without training on any aligned melody-lyr data. |
| Outcome: | The proposed model generates high-quality lyrics that are singable, intelligible, and coherent than baseline models. |
Consistency Rating of Semantic Transparency: an Evaluation Method for Metaphor Competence in Idiom Understanding Tasks (2025.coling-main)
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| Challenge: | Idioms condense complex semantics into fixed phrases, making idiom comprehension a test of metaphor competence. |
| Approach: | They propose a method to evaluate the metaphor competence of LLMs for the idiom understanding task: the Consistency Rating of Semantic Transparency (CR-ST). |
| Outcome: | The proposed method assesses the difficulty of understanding idioms through two dimensions: overall semantic transparency and constituent semantic transparency, aiming to gauge LLMs’ mastery of metaphor competence. |
How Does the Disclosure of AI Assistance Affect the Perceptions of Writing? (2024.emnlp-main)
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| Challenge: | Recent advances in generative AI technologies like large language models have boosted the incorporation of AI assistance in writing workflows. |
| Approach: | They conduct an experimental study to determine whether disclosure of AI assistance in the writing process would affect people's evaluation on the quality of the writing and ranking of different writings. |
| Outcome: | The disclosure of AI assistance decreases the average quality ratings for argumentative essays and creative stories, and increases the quality of the writings. |
Beyond the Panorama: Training-Free Hierarchical Perception-Reasoning for Fine-Grained Vision in MLLMs (2026.acl-long)
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| Challenge: | Existing multimodal large language models (MLLMs) face challenges in fine-grained visual tasks. |
| Approach: | They propose a training-free hierarchical perception-reasoning framework that enhances fine-grained visual understanding by simulating human perception mechanisms. |
| Outcome: | The proposed framework enhances fine-grained visual understanding by simulating human perception mechanisms. |
Distilling Causal Effect of Data in Continual Few-shot Relation Learning (2024.lrec-main)
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| Challenge: | Existing methods for learning relational patterns from data are prone to catastrophic forgetting issues due to limited number of samples and continual training mode. |
| Approach: | They propose a unified causal framework for CFRL to restore causal effects from old data . they establish two additional causal paths from old to predictions by colliding with old data separately in the old feature space. |
| Outcome: | The proposed method is superior to existing state-of-the-art methods in CFRL task settings. |
Context-Situated Pun Generation (2022.emnlp-main)
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Jiao Sun, Anjali Narayan-Chen, Shereen Oraby, Shuyang Gao, Tagyoung Chung, Jing Huang, Yang Liu, Nanyun Peng
| Challenge: | a new task for context-situated pun generation uses a given context to generate puns . human evaluation shows that 69% of top retrieved pun words can be used to generate context-based puns. |
| Approach: | They propose a task where puns are generated based on contextual keywords and pun words. |
| Outcome: | The proposed system generates successful puns 31% of the time given a plausible tuple of context words and pun pairs. |
Engage the Public: Poll Question Generation for Social Media Posts (2021.acl-long)
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| Challenge: | a novel application to generate poll questions for social media posts offers an easy way to hear the public's voice . for the silent majority, they tend to read others' messages instead of voicing their opinions with words . |
| Approach: | They propose to encode user comments and discover latent topics therein as contexts to generate poll questions for social media posts. |
| Outcome: | The proposed model outperforms popular models without exploiting topics from comments . human evaluations show it can generate high-quality polls useful to draw user engagements . |