Papers by Siyan Li

8 papers
EDEN: Empathetic Dialogues for English Learning (2024.findings-emnlp)

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Challenge: Recent studies have shown that student passion and perseverance, or grit, is associated with language learning success.
Approach: They hypothesize that as students perceive their English teachers to be more supportive, their grit improves.
Outcome: The proposed chatbot improves student persistence in learning a second language.
Enhancing Self-Consistency and Performance of Pre-Trained Language Models through Natural Language Inference (2022.emnlp-main)

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Challenge: Existing frameworks for boosting consistency and accuracy of pre-trained NLP models without fine-tuning or re-training are lacking.
Approach: They propose a framework for boosting the consistency and accuracy of pre-trained NLP models using pre-trainer natural language inference models without fine-tuning or re-training.
Outcome: The proposed framework boosts consistency and accuracy of pre-trained NLP models using pre-train natural language inference models without fine-tuning or re-training.
Inferring the Reader: Guiding Automated Story Generation with Commonsense Reasoning (2022.findings-emnlp)

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Challenge: Existing methods to automate story generation focus on single-character stories and lack basiccommonsense reasoning.
Approach: They propose a commonsense-inference Augmentedneural StoryTelling framework that introduces commonsensical reasoning into the story generation process.
Outcome: The proposed method produces significantly more coherent, on-topic, enjoyable andfluent stories than existing models in both the single-character and two-character settings.
Systematicity in GPT-3’s Interpretation of Novel English Noun Compounds (2022.findings-emnlp)

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Challenge: e.g., stew skillet, swamp squash) are not fully compositional, but highly predictable based on whether the modifier and head refer to artifacts or natural kinds.
Approach: They propose to compare the interpretations of novel English noun compounds with the large language model GPT-3, which is governed by interpretive principles.
Outcome: The results show that the large language model GPT-3 reasoning only about specific lexical items is consistent with the Levin et al.'s theory.
PAPILLON: Privacy Preservation from Internet-based and Local Language Model Ensembles (2025.naacl-long)

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Challenge: Existing research has studied privacy in LLM training data memorization, but it does not prevent users from disclosing PII at inference time.
Approach: They propose a task for chaining API-based and local LLMs that uses public data to construct a benchmark that contains personally identifiable information (PII)
Outcome: The proposed model maintains high response quality for 85.5% of user queries while restricting privacy leakage to only 7.5%.
Bottom-Up Synthesis of Knowledge-Grounded Task-Oriented Dialogues with Iteratively Self-Refined Prompts (2025.naacl-short)

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Challenge: Training conversational question-answering systems requires in-domain data, which is often scarce in practice.
Approach: They propose a bottom-up approach where QA pairs are generated first and combined into a coherent dialogue.
Outcome: The proposed approach produces more realistic and higher-quality dialogues compared to top-down methods.
Bringing Pedagogy into Focus: Evaluating Virtual Teaching Assistants’ Question-Answering in Asynchronous Learning Environments (2025.findings-emnlp)

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Challenge: Existing assessments rely on surface-level metrics and lack sufficient grounding in educational theory . a new framework is proposed to evaluate VTAs in asynchronous learning environments .
Approach: They propose a pedagogically-oriented evaluation framework tailored to asynchronous forum discussions . they construct classifiers using expert annotations of VTA responses on a diverse set of forum posts .
Outcome: The proposed evaluation framework is rooted in learning sciences and tailored to asynchronous forum discussions.
KuiLeiXi: a Chinese Open-Ended Text Adventure Game (2021.acl-demo)

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Challenge: Recent advances in pre-trained language models have made it possible to generate human-like text.
Approach: They propose to integrate an open-ended text adventure game in Chinese, named KuiLeiXi, where players interact with the AI until the plot goals are reached.
Outcome: The proposed game lacks incentives and relies on players to explore on their own.

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