Papers by Shiyue Zhang
Continuous Language Generative Flow (2021.acl-long)
Copied to clipboard
| Challenge: | Recent years have witnessed various types of generative models for natural language generation (NLG), especially RNNs or transformers. |
| Approach: | They propose a flow-based language generation model that adapts flow-derived generative models to language generation via continuous input embeddings, adapted affine coupling structures, and a novel architecture for autoregressive text generation. |
| Outcome: | The proposed model improves on QG and NMT and improves performance over baselines on SQuAD and TVQA and NML16. |
Masked Part-Of-Speech Model: Does Modeling Long Context Help Unsupervised POS-tagging? (2022.naacl-main)
Copied to clipboard
| Challenge: | Recent Part-Of-Speech (POS) induction models assume certain independence assumptions that do not hold in real languages. |
| Approach: | They propose a Masked Part-of-Speech Model (MPoSM) that can model arbitrary tag dependency and perform POS induction through the objective of masked POS reconstruction. |
| Outcome: | The proposed model can model arbitrary tag dependency and perform POS induction through the objective of masked POS reconstruction. |
RAG LLMs are Not Safer: A Safety Analysis of Retrieval-Augmented Generation for Large Language Models (2025.naacl-long)
Copied to clipboard
| Challenge: | Efforts to ensure the safety of large language models (LLMs) include safety fine-tuning, evaluation, and red teaming. |
| Approach: | They conduct a comparative analysis of RAG and non-RAG frameworks with eleven LLMs to examine how RAG can make models less safe and change their safety profile. |
| Outcome: | The proposed methods are less effective than those used for non-RAG settings. |
HistAlign: Improving Context Dependency in Language Generation by Aligning with History (2023.emnlp-main)
Copied to clipboard
| Challenge: | Language models (LMs) can generate hallucinations and incoherent outputs due to their weak context dependency. |
| Approach: | They propose a training approach to ensure good cache alignment so that the model receives useful signals from the history. |
| Outcome: | The proposed approach improves text coherence and faithfulness on diverse language generation tasks. |
AgentSense: Benchmarking Social Intelligence of Language Agents through Interactive Scenarios (2025.naacl-long)
Copied to clipboard
Xinyi Mou, Jingcong Liang, Jiayu Lin, Xinnong Zhang, Xiawei Liu, Shiyue Yang, Rong Ye, Lei Chen, Haoyu Kuang, Xuanjing Huang, Zhongyu Wei
| Challenge: | Large language models are increasingly employed to empower autonomous agents to simulate human behavior. |
| Approach: | They propose to evaluate LLM-driven agents through multi-turn interactions using a bottom-up approach to create diverse social scenarios constructed from extensive scripts. |
| Outcome: | The proposed model evaluates LLM-driven agents through multi-turn interactions emphasizing goal completion and implicit reasoning. |
SETSum: Summarization and Visualization of Student Evaluations of Teaching (2022.naacl-demo)
Copied to clipboard
| Challenge: | Student Evaluations of Teaching (SETs) are used in colleges and universities to assess student perceptions about their courses. |
| Approach: | They propose a system that leverages sentiment analysis, aspect extraction, summarization and visualization techniques to provide organized illustrations of SET findings to instructors and other reviewers. |
| Outcome: | The proposed system can be used by 10 professors from diverse departments to analyze SET results. |
Extractive is not Faithful: An Investigation of Broad Unfaithfulness Problems in Extractive Summarization (2023.acl-long)
Copied to clipboard
| Challenge: | Abstractive summarization is less prone to unfaithfulness issues than abstractive summaries . but, unfaitfulness problems, i.e., hallucinating new information, are still a problem in extractive summarisation . |
| Approach: | They propose a typology with five types of broad unfaithfulness problems that can appear in extractive summaries, including and beyond not-entailment. |
| Outcome: | The proposed metric shows that it detects unfaithful summaries faster than existing faithfulness evaluation metrics. |
Leakage-Adjusted Simulatability: Can Models Generate Non-Trivial Explanations of Their Behavior in Natural Language? (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing models that generate NL explanations for tasks have been evaluated on the basis of surface-level similarities to human explanations, both through automatic metrics like BLEU and human evaluations. |
| Approach: | They propose to use a model as a proxy for a human observer to evaluate NL explanations from the model simulatability perspective. |
| Outcome: | The proposed model-generated explanations are evaluated on the basis of surface-level similarities to human explanations, both through automatic metrics like BLEU and human evaluations. |
AI-Press: A Multi-Agent News Generating and Feedback Simulation System Powered by Large Language Models (2025.coling-demos)
Copied to clipboard
Xiawei Liu, Shiyue Yang, Xinnong Zhang, Haoyu Kuang, Libo Sun, Yihang Yang, Siming Chen, Xuanjing Huang, Zhongyu Wei
| Challenge: | We introduce AI-Press, an automated news drafting and polishing system based on multi-agent collaboration and Retrieval-Augmented Generation. |
| Approach: | They introduce AI-Press, an automated news drafting and polishing system based on multi-agent collaboration and Retrieval-Augmented Generation. |
| Outcome: | The proposed system generates public responses considering demographic distributions. |
Addressing Semantic Drift in Question Generation for Semi-Supervised Question Answering (D19-1)
Copied to clipboard
| Challenge: | Existing QG models suffer from a “semantic drift” problem, i.e., the semantics of the model-generated question drifts away from the given context and answer. |
| Approach: | They propose two semantics-enhanced rewards obtained from downstream question paraphrasing and question answering tasks to regularize the QG model to generate semantically valid questions. |
| Outcome: | The proposed method achieves state-of-the-art performance w.r.t. traditional evaluation metrics and performs best on QA-based evaluation metrics. |
MWPO: Enhancing LLMs Performance through Multi-Weight Preference Strength and Length Optimization (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing offline alternatives to Reinforcement Learning from Human Feedback (RLHF) are available at https://github.com/AIR-hl/MWPO. |
| Approach: | They propose an offline method to optimize preference pairs based on implicit reward margins and response length margins by reweighting them using a geometric mixture. |
| Outcome: | The proposed method outperforms state-of-the-art methods on four different scales and reduces generation length by 9.4%. |
ChrEn: Cherokee-English Machine Translation for Endangered Language Revitalization (2020.emnlp-main)
Copied to clipboard
| Challenge: | Cherokee is a highly endangered Native American language spoken by the Cherokee people . there are only 2,000 fluent first language Cherokee speakers remaining in the world . |
| Approach: | They propose a Cherokee-English parallel dataset to facilitate machine translation between Cherokee and English. |
| Outcome: | The proposed dataset compares Cherokee-English and English-Cherokee machine translation systems . the results show that the datasets are low-resource and low-cost compared to other datasets . |
EmailSum: Abstractive Email Thread Summarization (2021.acl-long)
Copied to clipboard
| Challenge: | Recent years have brought about interest in the task of summarizing conversation threads. |
| Approach: | They develop an email thread summarization dataset that contains human-annotated short and long email threads over a wide variety of topics. |
| Outcome: | The proposed dataset contains human-annotated short (30 words) and long (100 words) summaries of 2,549 email threads over a wide variety of topics. |
Finding a Balanced Degree of Automation for Summary Evaluation (2021.emnlp-main)
Copied to clipboard
| Challenge: | Automated summarization metrics are reliable but often poorly correlated with human judgment. |
| Approach: | They propose a semi-automatic to automatic summary evaluation metrics, following the Pyramid human evaluation method. |
| Outcome: | The proposed metrics are semi-automatic to automatic summary evaluation metrics, following the Pyramid human evaluation method. |
How can NLP Help Revitalize Endangered Languages? A Case Study and Roadmap for the Cherokee Language (2022.acl-long)
Copied to clipboard
| Challenge: | There are an estimated 6000 to 7000 spoken languages in the world, and at least 43% of them are endangered. |
| Approach: | They propose three principles that may help NLP practitioners foster mutual understanding and collaboration with language communities and three ways in which NLP can potentially assist in language education. |
| Outcome: | The proposed methods can be used to enrich Cherokee language resources with machine-in-the-loop processing and to provide language education. |
Evaluating the Factual Consistency of Large Language Models Through News Summarization (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing LLMs generally assign a higher score to factually consistent summaries than to factualally inconsistent summary. |
| Approach: | They propose a benchmark to measure whether large language models prefer factually consistent continuations of inputs. |
| Outcome: | The proposed benchmark compares the scores an LLM assigns to a factually consistent versus a inconsistent summary for an input news article. |
Localizing Factual Inconsistencies in Attributable Text Generation (2026.tacl-1)
Copied to clipboard
Arie Cattan, Paul Roit, Shiyue Zhang, David Wan, Roee Aharoni, Idan Szpektor, Mohit Bansal, Ido Dagan
| Challenge: | Existing methods for detecting hallucinations in model-generated texts fail to pinpoint errors. |
| Approach: | They propose a formalism for localizing factual inconsistencies in attributable text generation . they propose to decompose the generated text into simple question-answer pairs . |
| Outcome: | The proposed method achieves substantial inter-annotator agreement while achieving a substantial consistency score. |
MixCE: Training Autoregressive Language Models by Mixing Forward and Reverse Cross-Entropies (2023.acl-long)
Copied to clipboard
| Challenge: | Autoregressive language models are trained by minimizing the cross-entropy of the model distribution Q relative to the data distribution P. However, these systems still struggle in many openended generation settings, where they are asked to produce a long text following a short prompt. |
| Approach: | They propose to combine forward and reverse cross-entropy to train autoregressive language models by minimizing the cross-Entropy of the model distribution Q relative to the data distribution P. |
| Outcome: | The proposed model overgeneralizes and produces non-human-like text without complex decoding strategies. |
Improving Instruct Models for Free: A Study on Partial Adaptation (2025.emnlp-main)
Copied to clipboard
Ozan Irsoy, Pengxiang Cheng, Jennifer L Chen, Daniel Preotiuc-Pietro, Shiyue Zhang, Duccio Pappadopulo
| Challenge: | Instruct models are deemed superior and more usable but can be eroded by instruction tuning . a recent study shows that instruct models are better at following instructions than base models . |
| Approach: | They scale down the strength of instruction tuning to improve model performance . they show that reducing instruction tuning results in material improvement . |
| Outcome: | The proposed model improves on a few-shot in-context learning benchmark . but it loses some degree of its in-training ability . |
ChrEnTranslate: Cherokee-English Machine Translation Demo with Quality Estimation and Corrective Feedback (2021.acl-demo)
Copied to clipboard
| Challenge: | a new study shows that machine translation models can translate fragments of the source sentence but make major mistakes. |
| Approach: | They propose an online machine translation demonstration system for translation between English and an endangered language Cherokee. |
| Outcome: | The proposed system achieves state-of-the-art translation performance and improves quality estimation . the proposed system can translate between English and an endangered language Cherokee . |