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Zihan Liang

7 accepted papers

2026

OneSearch: A Preliminary Exploration of the Unified End-to-End Generative Framework for E-commerce Search

ICML 2026poster

Traditional e-commerce search systems employ multi-stage cascading architectures (MCA) that suffer from fragmented computation and optimization objective collisions across stages, ultimately limiting their performance ceiling. We propose OneSearch, the first industrial-deployed end-to-end generative…

Cited by 0SourceScholar
2025

Causal Representation Learning from Multimodal Clinical Records under Non-Random Modality Missingness

EMNLP 2025

Clinical notes contain rich patient information, such as diagnoses or medications, making them valuable for patient representation learning. Recent advances in large language models have further improved the ability to extract meaningful representations from clinical texts. However, clinical notes a

Cited by 0SourcePDFScholar
2025

InfoGain-RAG: Boosting Retrieval-Augmented Generation through Document Information Gain-based Reranking and Filtering

EMNLP 2025

Retrieval-Augmented Generation (RAG) has emerged as a promising approach to address key limitations of Large Language Models (LLMs), such as hallucination, outdated knowledge, and lacking reliable reference. However, current RAG frameworks often struggle with identifying whether retrieved documents

Cited by 0SourcePDFScholar
2024

Contrastive Token Learning with Similarity Decay for Repetition Suppression in Machine Translation

EMNLP 2024finding

For crosslingual conversation and trade, Neural Machine Translation (NMT) is pivotal yet faces persistent challenges with monotony and repetition in generated content. Traditional solutions that rely on penalizing text redundancy or token reoccurrence have shown limited efficacy, particularly for le…

Cited by 0SourcePDFScholar
2024

MoDULA: Mixture of Domain-Specific and Universal LoRA for Multi-Task Learning

EMNLP 2024main

The growing demand for larger-scale models in the development of Large Language Models (LLMs) poses challenges for efficient training within limited computational resources. Traditional fine-tuning methods often exhibit instability in multi-task learning and rely heavily on extensive training resour…

Cited by 1SourcePDFScholar
2024

Self-Renewal Prompt Optimizing with Implicit Reasoning

EMNLP 2024finding

The effectiveness of Large Language Models (LLMs) relies on their capacity to understand instructions and generate human-like responses. However, aligning LLMs with complex human preferences remains a significant challenge due to the potential misinterpretation of user prompts. Current methods for a…

Cited by 0SourcePDFScholar
2022

AssistSR: Task-oriented Video Segment Retrieval for Personal AI Assistant

EMNLP 2022finding

It is still a pipe dream that personal AI assistants on the phone and AR glasses can assist our daily life in addressing our questions like “how to adjust the date for this watch?” and “how to set its heating duration? (while pointing at an oven)”. The queries used in conventional tasks (i.e. Video…