most citedAn Interpretability Evaluation Benchmark for Pre-trained Language Models

1 citations · 1 across the 3 of their papers we have counts for

collaborators

6 papers

cs.CV202311 cited

ChatBridge: Bridging Modalities with Large Language Model as a Language Catalyst

Zijia Zhao, Longteng Guo, Tongtian Yue +5

Building general-purpose models that can perceive diverse real-world modalities and solve various tasks is an appealing target in artificial intelligence. In this paper, we present…

cs.IR2023

TOME: A Two-stage Approach for Model-based Retrieval

Ruiyang Ren, Wayne Xin Zhao, Jing Liu +3

Recently, model-based retrieval has emerged as a new paradigm in text retrieval that discards the index in the traditional retrieval model and instead memorizes the candidate corpo…

cs.CL20233 cited

SMoA: Sparse Mixture of Adapters to Mitigate Multiple Dataset Biases

Yanchen Liu, Jing Yan, Yan Chen +2

Recent studies reveal that various biases exist in different NLP tasks, and over-reliance on biases results in models' poor generalization ability and low adversarial robustness. T…

cs.CL20221 cited

An Interpretability Evaluation Benchmark for Pre-trained Language Models

Yaozong Shen, Lijie Wang, Ying Chen +3

While pre-trained language models (LMs) have brought great improvements in many NLP tasks, there is increasing attention to explore capabilities of LMs and interpret their predicti…

cs.CL2021

Attentive Contextual Carryover for Multi-Turn End-to-End Spoken Language Understanding

Kai Wei, Thanh Tran, Feng-Ju Chang +8

Recent years have seen significant advances in end-to-end (E2E) spoken language understanding (SLU) systems, which directly predict intents and slots from spoken audio. While dialo…

cs.CL2021

DuQM: A Chinese Dataset of Linguistically Perturbed Natural Questions for Evaluating the Robustness of Question Matching Models

Hongyu Zhu, Yan Chen, Jing Yan +5

In this paper, we focus on studying robustness evaluation of Chinese question matching. Most of the previous work on analyzing robustness issue focus on just one or a few types of…