37 citations · 127 across the 12 of their papers we have counts for
12 papers
Noisy Pair Corrector for Dense Retrieval
Hang Zhang, Yeyun Gong, Xingwei He +4
Most dense retrieval models contain an implicit assumption: the training query-document pairs are exactly matched. Since it is expensive to annotate the corpus manually, training p…
Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding
Hang Zhang, Xin Li, Lidong Bing
We present Video-LLaMA a multi-modal framework that empowers Large Language Models (LLMs) with the capability of understanding both visual and auditory content in the video. Video-…
AnnoLLM: Making Large Language Models to Be Better Crowdsourced Annotators
Xingwei He, Zhenghao Lin, Yeyun Gong +7
Many natural language processing (NLP) tasks rely on labeled data to train machine learning models with high performance. However, data annotation is time-consuming and expensive,…
CAPSTONE: Curriculum Sampling for Dense Retrieval with Document Expansion
Xingwei He, Yeyun Gong, A-Long Jin +5
The dual-encoder has become the de facto architecture for dense retrieval. Typically, it computes the latent representations of the query and document independently, thus failing t…
APOLLO: An Optimized Training Approach for Long-form Numerical Reasoning
Jiashuo Sun, Hang Zhang, Chen Lin +3
Long-form numerical reasoning in financial analysis aims to generate a reasoning program to calculate the correct answer for a given question. Previous work followed a retriever-ge…
Metric-guided Distillation: Distilling Knowledge from the Metric to Ranker and Retriever for Generative Commonsense Reasoning
Xingwei He, Yeyun Gong, A-Long Jin +7
Commonsense generation aims to generate a realistic sentence describing a daily scene under the given concepts, which is very challenging, since it requires models to have relation…
Sentiment-Aware Word and Sentence Level Pre-training for Sentiment Analysis
Shuai Fan, Chen Lin, Haonan Li +6
Most existing pre-trained language representation models (PLMs) are sub-optimal in sentiment analysis tasks, as they capture the sentiment information from word-level while under-c…
PROD: Progressive Distillation for Dense Retrieval
Zhenghao Lin, Yeyun Gong, Xiao Liu +8
Knowledge distillation is an effective way to transfer knowledge from a strong teacher to an efficient student model. Ideally, we expect the better the teacher is, the better the s…
Adversarial Retriever-Ranker for dense text retrieval
Hang Zhang, Yeyun Gong, Yelong Shen +3
Current dense text retrieval models face two typical challenges. First, they adopt a siamese dual-encoder architecture to encode queries and documents independently for fast indexi…
Poolingformer: Long Document Modeling with Pooling Attention
Hang Zhang, Yeyun Gong, Yelong Shen +4
In this paper, we introduce a two-level attention schema, Poolingformer, for long document modeling. Its first level uses a smaller sliding window pattern to aggregate information…
GLGE: A New General Language Generation Evaluation Benchmark
Dayiheng Liu, Yu Yan, Yeyun Gong +15
Multi-task benchmarks such as GLUE and SuperGLUE have driven great progress of pretraining and transfer learning in Natural Language Processing (NLP). These benchmarks mostly focus…
Let's be Humorous: Knowledge Enhanced Humor Generation
Hang Zhang, Dayiheng Liu, Jiancheng Lv +1
The generation of humor is an under-explored and challenging problem. Previous works mainly utilize templates or replace phrases to generate humor. However, few works focus on free…