66 citations · 74 across the 6 of their papers we have counts for
6 papers
ProCQA: A Large-scale Community-based Programming Question Answering Dataset for Code Search
Zehan Li, Jianfei Zhang, Chuantao Yin +2
Retrieval-based code question answering seeks to match user queries in natural language to relevant code snippets. Previous approaches typically rely on pretraining models using cr…
Text Representation Distillation via Information Bottleneck Principle
Yanzhao Zhang, Dingkun Long, Zehan Li +1
Pre-trained language models (PLMs) have recently shown great success in text representation field. However, the high computational cost and high-dimensional representation of PLMs…
Language Models are Universal Embedders
Xin Zhang, Zehan Li, Yanzhao Zhang +4
In the large language model (LLM) revolution, embedding is a key component of various systems, such as retrieving knowledge or memories for LLMs or building content moderation filt…
Towards General Text Embeddings with Multi-stage Contrastive Learning
Zehan Li, Xin Zhang, Yanzhao Zhang +3
We present GTE, a general-purpose text embedding model trained with multi-stage contrastive learning. In line with recent advancements in unifying various NLP tasks into a single f…
Challenging Decoder helps in Masked Auto-Encoder Pre-training for Dense Passage Retrieval
Zehan Li, Yanzhao Zhang, Dingkun Long +1
Recently, various studies have been directed towards exploring dense passage retrieval techniques employing pre-trained language models, among which the masked auto-encoder (MAE) p…
Learning Diverse Document Representations with Deep Query Interactions for Dense Retrieval
Zehan Li, Nan Yang, Liang Wang +1
In this paper, we propose a new dense retrieval model which learns diverse document representations with deep query interactions. Our model encodes each document with a set of gene…