Papers by Weiqi Zhang

14 papers
Towards Enhanced Immersion and Agency for LLM-based Interactive Drama (2025.acl-long)

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Challenge: Existing studies have focused on the role of immersion and agency in interactive drama.
Approach: They propose a playwriting-guided generation method that helps LLMs craft dramatic stories with substantially improved structures and narrative quality.
Outcome: The proposed method improves storytelling quality and immersion and agency, while allowing agents to refine their reactions to align with the player’s intentions.
StoryAnalogy: Deriving Story-level Analogies from Large Language Models to Unlock Analogical Understanding (2023.emnlp-main)

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Challenge: Analogy-making between narratives is crucial for human reasoning . despite its importance, there has been limited research on story analogies .
Approach: They construct a large-scale story-level analogy corpus with 24K story pairs . they find that the tasks are incredibly difficult for large language models such as ChatGPT .
Outcome: The proposed corpus contains 24K story pairs from diverse domains with human annotations on two similarities from the extended Structure-Mapping Theory.
On the Role of Entity and Event Level Conceptualization in Generalizable Reasoning: A Survey of Tasks, Methods, Applications, and Future Directions (2025.findings-emnlp)

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Challenge: Conceptualization is a fundamental element of human cognition and plays a pivotal role in generalizable reasoning.
Approach: They propose to categorize different types of conceptualizations into four levels based on the types of instances being conceptualized.
Outcome: The proposed categorization of different types of conceptualizations into four levels based on the types of instances being conceptualized .
Interpretable Math Word Problem Solution Generation via Step-by-step Planning (2023.acl-long)

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Challenge: Existing approaches to solving math word problems focus on obtaining the correct answer.
Approach: They propose a step-by-step planning approach for intermediate solution generation that strategically plans the generation of the next solution step based on the MWP and the previous solution steps.
Outcome: The proposed approach improves the accuracy and interpretability of the solution on automatic metrics and human evaluation.
AbsPyramid: Benchmarking the Abstraction Ability of Language Models with a Unified Entailment Graph (2024.findings-naacl)

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Challenge: Existing language models only touch nouns or verbs within simplified events or specific domains.
Approach: They propose an entailment graph that collects abstract knowledge for 3 components of diverse events to comprehensively evaluate the abstraction ability of language models.
Outcome: The proposed benchmark improves LLMs across two previous abstraction tasks.
NegotiationToM: A Benchmark for Stress-testing Machine Theory of Mind on Negotiation Surrounding (2024.findings-emnlp)

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Challenge: Theory of mind evaluations currently focus on testing models using machine-generated data or game settings prone to shortcuts and spurious correlations.
Approach: They propose a benchmark to stress-test machine ToM in real-world negotiation surrounding covered multi-dimensional mental states.
Outcome: The proposed benchmark builds upon the Belief-Desire-Intention theory and conducts the necessary empirical experiments to evaluate large language models.
Controllable Style Arithmetic with Language Models (2025.acl-long)

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Challenge: Existing methods for linguistic style control lack fine-grained control, require extensive computation, or introduce significant latency.
Approach: They propose a parameter-space approach that extracts style-specific representations by analyzing parameter differences between models trained on contrasting styles and incorporates them into a model with precise control over style intensity.
Outcome: The proposed approach achieves three key capabilities while achieving optimal computational efficiency.
Benchmarking Commonsense Knowledge Base Population with an Effective Evaluation Dataset (2021.emnlp-main)

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Challenge: Existing evaluations on the population task are either not accurate (automatic evaluation with randomly sampled negative examples) or of small scale (human annotation).
Approach: They propose a reasoning over commonsense knowledge bases (CSKBs) that are free-text and have a human annotation set to probe commonsensical reasoning.
Outcome: The proposed model is based on a human-annotated evaluation set and is compared with existing models on the population task.
COLA: Contextualized Commonsense Causal Reasoning from the Causal Inference Perspective (2023.acl-long)

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Challenge: Existing efforts to detect commonsense causation from the causal inference perspective are inadequate to seize commonsensical causations.
Approach: They propose a task to detect commonsense causation between two events in context . they propose 'contextualized commons sense causal reasoning' framework that uses covariates to remove confounding effects .
Outcome: The proposed framework can detect commonsense causality more accurately than baselines.
From Role-Play to Drama-Interaction: An LLM Solution (2024.findings-acl)

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Challenge: aristotle defined drama as a form of storytelling that involves a predefined storyline, emotions and thoughts.
Approach: They propose to use LLMs to create an immersive mode of storytelling . they propose to create a backbone drama LLM to drive the playing process .
Outcome: The proposed model can be used to drive the playing process, the authors say . it can be compared with existing models and can be evaluated on multiple scenarios.
ConKE: Conceptualization-Augmented Knowledge Editing in Large Language Models for Commonsense Reasoning (2025.findings-acl)

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Challenge: Existing knowledge editing methods face limited knowledge coverage in existing knowledge bases, infeasibility of annotating labels for an overabundance of commonsense knowledge, and strict knowledge formats.
Approach: They propose a framework that integrates conceptualization and instantiation into the KE pipeline for LLMs to enhance their commonsense reasoning capabilities.
Outcome: The proposed framework diagnoses implausible commonsense knowledge within an LLM and augments the source knowledge to be edited with conceptualization for stronger generalizability.
EcomScriptBench: A Multi-task Benchmark for E-commerce Script Planning via Step-wise Intention-Driven Product Association (2025.acl-long)

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Challenge: Goal-oriented script planning is used by humans to plan for typical activities . however, this capability remains underexplored due to several challenges .
Approach: They propose a framework that enables product-enriched scripts by associating products with each step based on the semantic similarity between the actions and their purchase intentions.
Outcome: The proposed framework can generate product-enriched scripts from 2.4 million scripts . human annotations are conducted to provide gold labels for a sampled subset .
IceBreaker for Conversational Agents: Breaking the First-Message Barrier with Personalized Starters (2026.acl-industry)

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Challenge: Existing efforts focus on activation within ongoing dialogues, while overlooking a key real-world bottleneck.
Approach: They propose a conversation starter generation system that generates personalized starters to guide users into conversation without explicit user intent.
Outcome: The proposed system improves user active days by +1.84 and click-through rate by +94.25 and has been deployed in production.
On-Policy Self-Distillation for Efficient Diffusion Language Models with Early-Stage Calibration (2026.findings-acl)

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Challenge: Recent studies have demonstrated that masked diffusion models (MDMs) can surpass autoregressive models (ARMs) in various tasks.
Approach: They propose a method to calibrate early token predictions without demonstration data by distilling an unnormalized target distribution into the original model.
Outcome: Experiments on math, planning, and RLHF tasks show that COPSD improves both effectiveness and efficiency, and further enhances performance when combined with supervised fine-tuning.

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