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20022026
most citedStealing Machine Learning Models via Prediction APIs

733 citations

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10 papers · 1 filter

cs.CL2024★ 3 cited

LLM Self-Correction with DeCRIM: Decompose, Critique, and Refine for Enhanced Following of Instructions with Multiple Constraints

Thomas Palmeira Ferraz, Kartik Mehta, Yu-Hsiang Lin +7

Instruction following is a key capability for LLMs. However, recent studies have shown that LLMs often struggle with instructions containing multiple constraints (e.g. a request to…

cs.CL2021

ChrEnTranslate: Cherokee-English Machine Translation Demo with Quality Estimation and Corrective Feedback

Shiyue Zhang, Benjamin Frey, Mohit Bansal

We introduce ChrEnTranslate, an online machine translation demonstration system for translation between English and an endangered language Cherokee. It supports both statistical an…

cs.CL2021★ 190 cited

Unifying Vision-and-Language Tasks via Text Generation

Jaemin Cho, Jie Lei, Hao Tan +1

Existing methods for vision-and-language learning typically require designing task-specific architectures and objectives for each task. For example, a multi-label answer classifier…

cs.CL2020★ 9 cited

I like fish, especially dolphins: Addressing Contradictions in Dialogue Modeling

Yixin Nie, Mary Williamson, Mohit Bansal +2

To quantify how well natural language understanding models can capture consistency in a general conversation, we introduce the DialoguE COntradiction DEtection task (DECODE) and a…

cs.CL2020

Summary-Source Proposition-level Alignment: Task, Datasets and Supervised Baseline

Ori Ernst, Ori Shapira, Ramakanth Pasunuru +4

Aligning sentences in a reference summary with their counterparts in source documents was shown as a useful auxiliary summarization task, notably for generating training data for s…

cs.CL2020★ 3 cited

Diagnosing the Environment Bias in Vision-and-Language Navigation

Yubo Zhang, Hao Tan, Mohit Bansal

Vision-and-Language Navigation (VLN) requires an agent to follow natural-language instructions, explore the given environments, and reach the desired target locations. These step-b…

cs.CL2020★ 3 cited

Multi-Source Domain Adaptation for Text Classification via DistanceNet-Bandits

Han Guo, Ramakanth Pasunuru, Mohit Bansal

Domain adaptation performance of a learning algorithm on a target domain is a function of its source domain error and a divergence measure between the data distribution of these tw…

cs.CL2020★ 2 cited

ManyModalQA: Modality Disambiguation and QA over Diverse Inputs

Darryl Hannan, Akshay Jain, Mohit Bansal

We present a new multimodal question answering challenge, ManyModalQA, in which an agent must answer a question by considering three distinct modalities: text, images, and tables.…

cs.CL2020★ 5 cited

Modality-Balanced Models for Visual Dialogue

Hyounghun Kim, Hao Tan, Mohit Bansal

The Visual Dialog task requires a model to exploit both image and conversational context information to generate the next response to the dialogue. However, via manual analysis, we…

cs.CL2020

AvgOut: A Simple Output-Probability Measure to Eliminate Dull Responses

Tong Niu, Mohit Bansal

Many sequence-to-sequence dialogue models tend to generate safe, uninformative responses. There have been various useful efforts on trying to eliminate them. However, these approac…