2 citations · 4 across the 12 of their papers we have counts for
13 papers
RankEvolve: A Reliable Multi-Agent Auto-Research Harness for Evolving Ranking Models
Zheng Chen, Linfeng Liu, Hong Li +1
Auto-research agents, LLM systems that propose, implement, train, and evaluate model changes across iterations, promise to automate applied ML's experimental loop. Over long horizo…
CMSL: Constructive Multi-Sequence Learning for Recommendation Systems
Zikun Cui, Renzhi Wu, Junjie Yang +10
Sequence learning has emerged as the promising paradigm in recommendation systems, surpassing traditional Deep Learning Recommendation Models (DLRM) by capturing the temporal nuanc…
Cross-Lingual Steering for Figurative Language Generation
Linfeng Liu, Tiffany Zhan, Louie Hong Yao +2
Cross-lingual activation steering has been studied mainly for language-agnostic properties such as factual knowledge and reasoning. Its transferability remains unclear for figurati…
A Computational Approach to Visual Metonymy
Saptarshi Ghosh, Linfeng Liu, Tianyu Jiang
Images often communicate more than they literally depict: a set of tools can suggest an occupation and a cultural artifact can suggest a tradition. This kind of indirect visual ref…
JE-IRT: A Geometric Lens on LLM Abilities through Joint Embedding Item Response Theory
Louie Hong Yao, Nicholas Jarvis, Tiffany Zhan +3
Standard LLM evaluation practices compress diverse abilities into single scores, obscuring their inherently multidimensional nature. We present JE-IRT, a geometric item-response fr…
Evaluating the Impact of Verbal Multiword Expressions on Machine Translation
Linfeng Liu, Saptarshi Ghosh, Tianyu Jiang
Verbal multiword expressions (VMWEs) remain difficult for machine translation because their meanings are often not recoverable from their component words. In this study, we analyze…
Preference Discerning with LLM-Enhanced Generative Retrieval
Fabian Paischer, Liu Yang, Linfeng Liu +12
In sequential recommendation, models recommend items based on user's interaction history. To this end, current models usually incorporate information such as item descriptions and…
Async Learned User Embeddings for Ads Delivery Optimization
Mingwei Tang, Meng Liu, Hong Li +16
In recommendation systems, high-quality user embeddings can capture subtle preferences, enable precise similarity calculations, and adapt to changing preferences over time to maint…
Empower Nested Boolean Logic via Self-Supervised Curriculum Learning
Hongqiu Wu, Linfeng Liu, Hai Zhao +1
Beyond the great cognitive powers showcased by language models, it is crucial to scrutinize whether their reasoning capabilities stem from strong generalization or merely exposure…
Chinese Spelling Correction as Rephrasing Language Model
Linfeng Liu, Hongqiu Wu, Hai Zhao
This paper studies Chinese Spelling Correction (CSC), which aims to detect and correct the potential spelling errors in a given sentence. Current state-of-the-art methods regard CS…
TriFormer: A Multi-modal Transformer Framework For Mild Cognitive Impairment Conversion Prediction
Linfeng Liu, Junyan Lyu, Siyu Liu +3
The prediction of mild cognitive impairment (MCI) conversion to Alzheimer's disease (AD) is important for early treatment to prevent or slow the progression of AD. To accurately pr…
Cascaded Multi-Modal Mixing Transformers for Alzheimer's Disease Classification with Incomplete Data
Linfeng Liu, Siyu Liu, Lu Zhang +3
Accurate medical classification requires a large number of multi-modal data, and in many cases, different feature types. Previous studies have shown promising results when using mu…
Structure Guided Manifolds for Discovery of Disease Characteristics
Siyu Liu, Linfeng Liu, Xuan Vinh +4
In medical image analysis, the subtle visual characteristics of many diseases are challenging to discern, particularly due to the lack of paired data. For example, in mild Alzheime…