19 citations · 25 across the 11 of their papers we have counts for
11 papers
Improving Item Discoverability in e-Commerce Search via Related Intent Generation
Ji Xin, Xiao Xiao, Ishan Bhatt +6
Traditional search systems are optimized to retrieve items that strictly match a query, often prioritizing precision over recall. In e-commerce marketplaces and particularly grocer…
SAGE: Signal-Amplified Guided Embeddings for LLM-based Vulnerability Detection
Zhengyang Shan, Xu Qian, Jiayun Xin +5
Software vulnerabilities are a primary threat to modern infrastructure. While static analysis and Graph Neural Networks have long served as the foundation for vulnerability detecti…
Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems
Brendan Leigh Ross, Noël Vouitsis, Atiyeh Ashari Ghomi +8
Although large language models (LLMs) are becoming increasingly capable of solving challenging real-world tasks, accurately quantifying their uncertainty remains a critical open pr…
Building an Efficiency Pipeline: Commutativity and Cumulativeness of Efficiency Operators for Transformers
Ji Xin, Raphael Tang, Zhiying Jiang +2
There exists a wide variety of efficiency methods for natural language processing (NLP) tasks, such as pruning, distillation, dynamic inference, quantization, etc. We can consider…
Few-Shot Non-Parametric Learning with Deep Latent Variable Model
Zhiying Jiang, Yiqin Dai, Ji Xin +2
Most real-world problems that machine learning algorithms are expected to solve face the situation with 1) unknown data distribution; 2) little domain-specific knowledge; and 3) da…
Certified Error Control of Candidate Set Pruning for Two-Stage Relevance Ranking
Minghan Li, Xinyu Zhang, Ji Xin +2
In information retrieval (IR), candidate set pruning has been commonly used to speed up two-stage relevance ranking. However, such an approach lacks accurate error control and ofte…
Zero-Shot Dense Retrieval with Momentum Adversarial Domain Invariant Representations
Ji Xin, Chenyan Xiong, Ashwin Srinivasan +3
Dense retrieval (DR) methods conduct text retrieval by first encoding texts in the embedding space and then matching them by nearest neighbor search. This requires strong locality…
Inserting Information Bottlenecks for Attribution in Transformers
Zhiying Jiang, Raphael Tang, Ji Xin +1
Pretrained transformers achieve the state of the art across tasks in natural language processing, motivating researchers to investigate their inner mechanisms. One common direction…
Showing Your Work Doesn't Always Work
Raphael Tang, Jaejun Lee, Ji Xin +3
In natural language processing, a recently popular line of work explores how to best report the experimental results of neural networks. One exemplar publication, titled "Show Your…
DeeBERT: Dynamic Early Exiting for Accelerating BERT Inference
Ji Xin, Raphael Tang, Jaejun Lee +2
Large-scale pre-trained language models such as BERT have brought significant improvements to NLP applications. However, they are also notorious for being slow in inference, which…
Exploiting Token and Path-based Representations of Code for Identifying Security-Relevant Commits
Achyudh Ram, Ji Xin, Meiyappan Nagappan +4
Public vulnerability databases such as CVE and NVD account for only 60% of security vulnerabilities present in open-source projects, and are known to suffer from inconsistent quali…