765 citations · 891 across the 10 of their papers we have counts for
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Bridging the KB-Text Gap: Leveraging Structured Knowledge-aware Pre-training for KBQA
Guanting Dong, Rumei Li, Sirui Wang +3
Knowledge Base Question Answering (KBQA) aims to answer natural language questions with factual information such as entities and relations in KBs. However, traditional Pre-trained…
Knowledge-Driven CoT: Exploring Faithful Reasoning in LLMs for Knowledge-intensive Question Answering
Keheng Wang, Feiyu Duan, Sirui Wang +5
Equipped with Chain-of-Thought (CoT), Large language models (LLMs) have shown impressive reasoning ability in various downstream tasks. Even so, suffering from hallucinations and t…
T5-SR: A Unified Seq-to-Seq Decoding Strategy for Semantic Parsing
Yuntao Li, Zhenpeng Su, Yutian Li +4
Translating natural language queries into SQLs in a seq2seq manner has attracted much attention recently. However, compared with abstract-syntactic-tree-based SQL generation, seq2s…
Robust Lottery Tickets for Pre-trained Language Models
Rui Zheng, Rong Bao, Yuhao Zhou +6
Recent works on Lottery Ticket Hypothesis have shown that pre-trained language models (PLMs) contain smaller matching subnetworks(winning tickets) which are capable of reaching acc…
PATS: Sensitivity-aware Noisy Learning for Pretrained Language Models
Yupeng Zhang, Hongzhi Zhang, Sirui Wang +2
A wide range of NLP tasks benefit from the fine-tuning of pretrained language models (PLMs). However, a number of redundant parameters which contribute less to the downstream task…
InstructionNER: A Multi-Task Instruction-Based Generative Framework for Few-shot NER
Liwen Wang, Rumei Li, Yang Yan +4
Recently, prompt-based methods have achieved significant performance in few-shot learning scenarios by bridging the gap between language model pre-training and fine-tuning for down…