Papers by Kaige Xie
Guiding Neural Story Generation with Reader Models (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing systems that generate narratives with neural language models require substantial knowledge engineering of logical constraints, limiting their generality. |
| Approach: | They propose a framework in which a reader model is used to reason about the storyshould progress. |
| Outcome: | The proposed model outperforms baseline models in plot plausibility and staying on topic. |
Rethinking Action Spaces for Reinforcement Learning in End-to-end Dialog Agents with Latent Variable Models (N19-1)
Copied to clipboard
| Challenge: | Existing approaches to define action spaces for conversational agents have limitations . end-to-end dialog systems can handle complex domains with limited action space . |
| Approach: | They propose a latent action framework that treats the action spaces of an end-to-end dialog agent as latent variables and develops unsupervised methods to induce its own action space from the data. |
| Outcome: | The proposed framework achieves better performance than word-level policy gradient methods on DealOrNoDeal and MultiWoz dialogs. |
Few-Shot Dialogue Summarization via Skeleton-Assisted Prompt Transfer in Prompt Tuning (2024.eacl-long)
Copied to clipboard
Kaige Xie, Tong Yu, Haoliang Wang, Junda Wu, Handong Zhao, Ruiyi Zhang, Kanak Mahadik, Ani Nenkova, Mark Riedl
| Challenge: | Existing prompt transfer techniques lack consideration for dialogue-specific information. |
| Approach: | They propose a method which leverages skeleton generation as extra supervision that functions as a medium connecting the distinct source and target task. |
| Outcome: | The proposed method significantly outperforms baselines on two dialogue summarization benchmarks. |
Calibrating Trust of Multi-Hop Question Answering Systems with Decompositional Probes (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Recent work in multi-hop QA has shown that performance can be boosted by decomposing questions into simpler, single-hop questions. |
| Approach: | They propose to decompose multi-hop questions into simpler, single-hop ones to create explanations by probing a neural QA model with them. |
| Outcome: | The proposed approach can be used to generate explanations by probing a neural QA model with them. |
Creating Suspenseful Stories: Iterative Planning with Large Language Models (2024.eacl-long)
Copied to clipboard
| Challenge: | Automated story generation has been a challenge in NLP for many years. |
| Approach: | They propose an iterative-prompting-based method that is grounded in two theoretical foundations of story suspense from cognitive psychology and narratology. |
| Outcome: | The proposed method works in a fully zero-shot manner and does not rely on any supervised story corpora. |
Towards Universal Dialogue State Tracking (D18-1)
Copied to clipboard
| Challenge: | Existing approaches to dialogue state tracking are difficult to scale to large dialogue domains. |
| Approach: | They propose a universal dialogue state tracker that is independent of the number of values and shares parameters across all slots. |
| Outcome: | The proposed system significantly outperforms state-of-the-art approaches on two datasets. |
Embedding-Informed Adaptive Retrieval-Augmented Generation of Large Language Models (2025.coling-main)
Copied to clipboard
| Challenge: | Retrieval-augmented large language models excel in various NLP tasks but are not always helpful when the knowledge required is absent in the model. |
| Approach: | They propose to determine whether the model is knowledgeable on a query via inspecting the (contextualized) pre-trained token embeddings of LLMs. |
| Outcome: | Experiments show that the proposed approach performs better than previous approaches on various benchmarks. |
Do RAG Systems Cover What Matters? Evaluating and Optimizing Responses with Sub-Question Coverage (2025.naacl-long)
Copied to clipboard
| Challenge: | Existing evaluations of retrieval-augmented generation systems are limited . sub-question coverage measures how well a RAG system addresses different facets of a question. |
| Approach: | They propose a framework for evaluation based on sub-question coverage . they propose to decompose questions into sub-questions and classify them into three types . |
| Outcome: | The proposed evaluation framework measures how well a RAG system addresses different facets of a question. |
Making Large Language Models into World Models with Precondition and Effect Knowledge (2025.coling-main)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) are not inherently designed to model real-world dynamics, but can be induced to perform two critical world model functions: determining the applicability of an action based on a given world state and predicting the resulting world state upon action execution. |
| Approach: | They propose to use Large Language Models to model world states and preconditions . they validate that precondition and effect knowledge generated by LLMs aligns with human understanding of world dynamics . |
| Outcome: | The proposed model can predict valid actions and state transitions, thereby replicating existing models. |