Papers by Yijia Shao
Into the Unknown Unknowns: Engaged Human Learning through Participation in Language Model Agent Conversations (2024.emnlp-main)
Copied to clipboard
| Challenge: | Recent advances in language models (LMs) and retrieval-augmented generation (RAG) have led to more capable chatbots and generative search engines. |
| Approach: | They propose to emulate the educational scenario where children/students learn by listening to and participating in conversations of their parents/teachers by watching and steering the discourse among several LM agents. |
| Outcome: | The proposed system outperforms baseline methods on discourse trace and report quality and is preferred by 70% of participants over a search engine and 78% over sabota. |
Assisting in Writing Wikipedia-like Articles From Scratch with Large Language Models (2024.naacl-long)
Copied to clipboard
| Challenge: | Existing methods to write grounded, long-form articles have limited planning capacity and require extensive research and planning in the pre-writing stage. |
| Approach: | They propose a system for the Synthesis of Topic Outlines throughRetrieval and Multi-perspective Question Asking that models the pre-writing stage by (1) discovering diverse perspectives in researching the given topic, (2) simulating conversations where writers carrying different perspectives pose questions to a topic expert grounded on trusted Internet sources, (3) curating the collected information to create an outline. |
| Outcome: | The proposed system is based on a dataset of high-quality Wikipedia articles and evaluates the pre-writing stage. |
ACCENT: An Automatic Event Commonsense Evaluation Metric for Open-Domain Dialogue Systems (2023.acl-long)
Copied to clipboard
| Challenge: | evaluating commonsense in dialogue systems remains an open challenge . despite the success of open-domain dialogue systems, systems struggle to produce commonsensical responses as humans do. |
| Approach: | They propose an event commonsense evaluation metric empowered by commonsensence knowledge bases. |
| Outcome: | The proposed metric achieves higher correlations with human judgments than baselines. |
Continual Training of Language Models for Few-Shot Learning (2022.emnlp-main)
Copied to clipboard
| Challenge: | Recent work on applying large language models (LMs) achieves impressive performance in many NLP applications. |
| Approach: | They propose to continuously post-train an LM with unlabeled domains to expand its knowledge without forgetting previous skills. |
| Outcome: | The proposed system improves few-shot end-task learning in these domains. |
Generative Interfaces for Language Models (2026.findings-acl)
Copied to clipboard
| Challenge: | Large language models are increasingly seen as assistants, copilots, and consultants . however, their linear request-response format often makes interactions inefficient in multi-turn tasks . |
| Approach: | They propose a paradigm in which large language models respond to user queries by generating user interfaces that enable more adaptive and interactive engagement. |
| Outcome: | The proposed paradigm outperforms traditional chat-based interfaces in many tasks and interaction patterns. |
AnaMeta: A Table Understanding Dataset of Field Metadata Knowledge Shared by Multi-dimensional Data Analysis Tasks (2023.findings-acl)
Copied to clipboard
Xinyi He, Mengyu Zhou, Mingjie Zhou, Jialiang Xu, Xiao Lv, Tianle Li, Yijia Shao, Shi Han, Zejian Yuan, Dongmei Zhang
| Challenge: | Tabular data analysis is performed everyday across various domains. |
| Approach: | They propose to use a dataset of 467k tables with supervision labels for four types of field metadata. |
| Outcome: | The proposed framework improves the understanding capability of tabular models by incorporating distribution and knowledge information. |
Class-Incremental Learning based on Label Generation (2023.acl-short)
Copied to clipboard
| Challenge: | Existing studies on pre-trained language models focus on task-incremental learning (TIL) but they perform poorly in a more challenging setting of class-incremental learning. |
| Approach: | They propose a method which solves CIL based on label generation by using sparse vocabulary and creates pseudo-replay samples by using label semantics. |
| Outcome: | The proposed method outperforms baseline models by a large margin in the class-incremental learning setting. |
Adapting a Language Model While Preserving its General Knowledge (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing DA-training methods do not explicitly identify what knowledge should be preserved and what should be changed by the domain corpus. |
| Approach: | They propose to use an unlabeled corpus of aparticular domain to train a pre-trained general-purpose language model to adapt the LM so that end-tasks in the domain can give improved performances. |
| Outcome: | The proposed method improves the performance of pre-trained general-purpose language models by contrasting the representations of the general and the full (both general and domain knowledge) to learn an integrated representation with both general and specific knowledge. |
Future of Work in the Age of LLMs (2026.acl-tutorials)
Copied to clipboard
| Challenge: | a tutorial examines the future of work shaped by the interplay of large language models and humans . a series of tutorials examines challenges, opportunities, and ethical considerations in this dynamic landscape . |
| Approach: | This tutorial examines the future of work shaped by the interplay of LLMs and humans . it examines how LLM-based systems can augment human labor and enhance real-world tasks . |
| Outcome: | This tutorial examines the future of work shaped by the interplay of LLMs and humans . it examines challenges, opportunities, and ethical considerations in this dynamic landscape . |
FormLM: Recommending Creation Ideas for Online Forms by Modelling Semantic and Structural Information (2022.emnlp-main)
Copied to clipboard
| Challenge: | FormLM is a pre-trained language model for creating semi-structured forms where questions and descriptions are organized by predefined structures. |
| Approach: | They propose to enhance pre-trained language model with form structural information to model online forms and recommend form creation ideas. |
| Outcome: | The proposed model outperforms general-purpose language models on all tasks, with an improvement by 4.71 on Question Recommendation and 10.6 on Block Type Suggestion in terms of ROUGE-1 and Macro-F1, respectively. |