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Haejun Lee

7 accepted papers

2025

Sparse Logit Sampling: Accelerating Knowledge Distillation in LLMs

ACL 2025long

Knowledge distillation can be a cost-effective technique to distill knowledge in Large Language Models, if the teacher output logits can be pre-computed and cached. However, successfully applying this to pre-training remains largely unexplored. In this work, we prove that naive approaches for sparse…

Cited by 0SourcePDFScholar
2024

Transformers Get Stable: An End-to-End Signal Propagation Theory for Language Models

ICML 2024poster

In spite of their huge success, transformer models remain difficult to scale in depth. In this work, we develop a unified signal propagation theory and provide formulae that govern the moments of the forward and backward signal through the transformer model. Our framework can be used to understand a…

2023

Span-Selective Linear Attention Transformers for Effective and Robust Schema-Guided Dialogue State Tracking

ACL 2023long

In schema-guided dialogue state tracking models estimate the current state of a conversation using natural language descriptions of the service schema for generalization to unseen services. Prior generative approaches which decode slot values sequentially do not generalize well to variations in sche…

Cited by 6SourcePDFScholar
2022

FiE: Building a Global Probability Space by Leveraging Early Fusion in Encoder for Open-Domain Question Answering

EMNLP 2022main

Generative models have recently started to outperform extractive models in Open Domain Question Answering, largely by leveraging their decoder to attend over multiple encoded passages and combining their information. However, generative models tend to be larger than extractive models due to the need…

Cited by 17SourcePDFScholar
2022

You Only Need One Model for Open-domain Question Answering

EMNLP 2022main

Recent approaches to Open-domain Question Answering refer to an external knowledge base using a retriever model, optionally rerank passages with a separate reranker model and generate an answer using another reader model. Despite performing related tasks, the models have separate parameters and are…

Cited by 28SourcePDFScholar
2021

Answering Open-Domain Questions of Varying Reasoning Steps from Text

EMNLP 2021main

We develop a unified system to answer directly from text open-domain questions that may require a varying number of retrieval steps. We employ a single multi-task transformer model to perform all the necessary subtasks—retrieving supporting facts, reranking them, and predicting the answer from all r…

2020

NeurQuRI: Neural Question Requirement Inspector for Answerability Prediction in Machine Reading Comprehension

ICLR 2020poster

Real-world question answering systems often retrieve potentially relevant documents to a given question through a keyword search, followed by a machine reading comprehension (MRC) step to find the exact answer from them. In this process, it is essential to properly determine whether an answer to the…

Cited by 34SourceScholar