Papers by Qing Lyu

18 papers
CoQuIR: A Comprehensive Benchmark for Code Quality-Aware Information Retrieval (2026.acl-long)

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Challenge: Existing benchmarks focus on functional relevance while neglecting code quality.
Approach: They propose a multilingual benchmark to evaluate quality-aware code retrieval . they include fine-grained quality annotations over 42,725 queries and 134,907 code snippets .
Outcome: The proposed benchmarks show that state-of-the-art models fail to separate buggy or insecure code from robust counterparts.
Is “My Favorite New Movie” My Favorite Movie? Probing the Understanding of Recursive Noun Phrases (2022.naacl-main)

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Challenge: Recursive noun phrases have interesting semantic properties, yet it is unknown whether language models have such knowledge.
Approach: They propose a dataset of three textual inference tasks targeting recursive noun phrases . they show that such knowledge is learnable with appropriate data .
Outcome: The proposed model achieves strong zero-shot performance on an extrinsic Harm Detection task.
Visual Goal-Step Inference using wikiHow (2021.emnlp-main)

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Challenge: Past work in NLP examined the task of goal-step inference for textual goals . wikiHow dataset shows that goal-step inference is challenging for state-of-the-art models .
Approach: They propose a task where a model is given a textual goal and must choose which of four images represents a plausible step towards that goal.
Outcome: The proposed task is challenging for state-of-the-art multimodal models and can be transferred to other datasets.
Zero-shot Event Extraction via Transfer Learning: Challenges and Insights (2021.acl-short)

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Challenge: Existing methods for event extraction require expensive annotation and are not extensible to new event ontologies.
Approach: They propose to use textual entailment and/or question answering queries to extract a zero-shot event from a set of TE and/ or QA queries.
Outcome: The proposed method achieves acceptable results on ACE-2005 and ERE, but there is still a large gap from supervised approaches.
Marco-Bench-MIF: On Multilingual Instruction-Following Capability of Large Language (2025.acl-long)

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Challenge: Existing datasets for instruction-following are monolingual and centered on English . existing data are unable to capture linguistic and cultural subtle differences .
Approach: They propose an extension of IFEval to a localized multilingual version called Marco-Bench-MIF . their benchmark addresses linguistic constraints and cultural references via translation and verification .
Outcome: The proposed extension of IFEval to a localized multilingual version covers 30 languages with varying levels of localization.
Intent Detection with WikiHow (2020.aacl-main)

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Challenge: Existing approaches to intent detection have limited data annotated for new domains or languages.
Approach: They propose to train a set of pretraining intent detection models on wikiHow which can predict a broad range of intended goals from many actions.
Outcome: The proposed models achieve state-of-the-art results on the Snips dataset, the Schema-Guided Dialogue dataset, and all 3 languages of the Facebook multilingual dialog datasets.
RESIN: A Dockerized Schema-Guided Cross-document Cross-lingual Cross-media Information Extraction and Event Tracking System (2021.naacl-demos)

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Challenge: We present a new information extraction system that can construct temporal event graphs from news documents.
Approach: They propose a temporal event graph extraction system that can extract news documents . they extend the system from sentence-level event extraction to cross-document cross-media event extraction .
Outcome: The proposed system can extract temporal event graphs from news documents in multiple languages and multiple data modalities.
Avoiding Copyright Infringement via Large Language Model Unlearning (2025.findings-naacl)

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Challenge: Pre-trained Large Language Models (LLMs) have demonstrated remarkable capabilities but also pose significant legal and ethical concerns.
Approach: They propose a framework that unlearns copyrighted content from large language models over multiple time steps by identifying and removing specific weight updates in the model’s parameters that correspond to copyright content.
Outcome: The proposed framework achieves an effective trade-off between unlearning efficacy and general-purpose language abilities, outperforming baselines.
Explanation in the Era of Large Language Models (2024.naacl-tutorials)

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Challenge: Explanation has long been a part of communication, where humans use language to elucidate each other and transmit information about mechanisms of events.
Approach: They review the opportunities and challenges of explanations in the era of large language models and examine how they can be used to generate explanations.
Outcome: The proposed methods are based on the models of large language models (LLMs) and their opaque nature.
Explanation-based Finetuning Makes Models More Robust to Spurious Cues (2023.acl-long)

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Challenge: Large Language Models (LLMs) learn correlations between labels and features that are irrelevant to the task, leading to poor generalization on out-of-distribution data.
Approach: They propose an explanation-based approach to fine tune large language models to generate a free-text explanation supporting their answer.
Outcome: The proposed model is more robust against spurious cues in terms of accuracy drop across four classification tasks: ComVE (+1.2), CREAK (+9.1), e-SNLI (+5.4), and SBIC (+6.5).
Show Me More Details: Discovering Hierarchies of Procedures from Semi-structured Web Data (2022.acl-long)

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Challenge: Existing work has treated procedures as shallow structures without modeling the parent-child relation.
Approach: They propose to construct an open-domain hierarchical knowledge-base (KB) of procedures based on wikiHow . they link steps in an article to other articles with similar goals, recursively building the KB .
Outcome: The proposed method significantly outperforms baselines according to automatic evaluation, human judgment, and application to downstream tasks such as instructional video retrieval.
VSCBench: Bridging the Gap in Vision-Language Model Safety Calibration (2025.findings-acl)

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Challenge: Existing safety calibration methods focus on model undersafety, where the model responds to hazardous queries, while neglecting oversafetiness, where models refuse to answer safe queries.
Approach: They propose safety calibration which addresses both undersafety and oversafetiness by comparing model responses to a novel dataset of 3,600 image-text pairs.
Outcome: The proposed methods have been used to evaluate safety calibration across image-centric and text-centric scenarios.
HD-NDEs: Neural Differential Equations for Hallucination Detection in LLMs (2025.acl-long)

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Challenge: Hallucination is a significant challenge for large language models, but current methods struggle when non-factual information arises in the early or mid-sequence of outputs, reducing their reliability.
Approach: They propose a method that captures the full dynamics of large language models by using neural differential equations to assess the truthfulness of statements.
Outcome: The proposed method achieves 14% improvement in AUC-ROC on the True-False dataset compared to state-of-the-art methods.
Reasoning about Goals, Steps, and Temporal Ordering with WikiHow (2020.emnlp-main)

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Challenge: Existing datasets focus on relation between procedural events, but little attention has been paid to relation between events.
Approach: They propose a set of reasoning tasks targeting goal-step relations and step-step temporal relations based on wikiHow articles . their automatically-generated training set allows models to transfer to out-of-domain tasks requiring knowledge of procedural events .
Outcome: The proposed dataset improves on SWAG, Snips, and Story Cloze Test in zero- and few-shot settings.
SlideCoder: Layout-aware RAG-enhanced Hierarchical Slide Generation from Design (2025.emnlp-main)

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Challenge: Existing natural language-based LLM generation methods struggle to capture visual and structural nuances of slide designs.
Approach: They propose a layout-aware framework for generating editable slides from reference images . they propose python code that translates NL instructions into Python code to construct each slide .
Outcome: The proposed framework outperforms state-of-the-art models by up to 40.5 points . it also outperformed open-source models with improved reverse-engineered data.
Reference-free Hallucination Detection for Large Vision-Language Models (2024.findings-emnlp)

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Challenge: Large vision-language models exhibit excellent ability in language understanding, question answering, and conversations of visual inputs, but they are prone to producing hallucinations.
Approach: They propose to use supervised uncertainty quantification methods to detect hallucinations in large vision-language models.
Outcome: The proposed methods outperform the others in detecting hallucinations on four representative LVLMs across two different tasks.
Representation of Lexical Stylistic Features in Language Models’ Embedding Space (2023.starsem-1)

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Challenge: lexical stylistic notions such as complexity, formality, and figurativeness can be identified in pretrained Language Models . static embeddings encode these features more accurately at the level of words and phrases whereas contextualized LMs perform better on sentences.
Approach: They propose to derive a vector representation for stylistic notions from seed pairs . they find that static embeddings encode stylistic features more accurately .
Outcome: The proposed representations can be used to characterize new texts in terms of these dimensions using a small number of seed pairs.

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