b'Sewon Min'

research

∙ 08/08/2023

SILO Language Models: Isolating Legal Risk In a Nonparametric Datastore

The legality of training language models (LMs) on copyrighted or otherwi...

0 Sewon Min, et al. ∙

research

∙ 05/23/2023

FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation

Evaluating the factuality of long-form text generated by large language ...

0 Sewon Min, et al. ∙

research

∙ 01/30/2023

REPLUG: Retrieval-Augmented Black-Box Language Models

We introduce REPLUG, a retrieval-augmented language modeling framework t...

7 Weijia Shi, et al. ∙

research

∙ 12/19/2022

Z-ICL: Zero-Shot In-Context Learning with Pseudo-Demonstrations

Although large language models can be prompted for both zero- and few-sh...

0 Xinxi Lyu, et al. ∙

research

∙ 12/02/2022

Nonparametric Masked Language Modeling

Existing language models (LMs) predict tokens with a softmax over a fini...

0 Sewon Min, et al. ∙

research

∙ 11/30/2022

CREPE: Open-Domain Question Answering with False Presuppositions

Information seeking users often pose questions with false presupposition...

0 Xinyan Velocity Yu, et al. ∙

research

∙ 10/22/2022

Exploring The Landscape of Distributional Robustness for Question Answering Models

We conduct a large empirical evaluation to investigate the landscape of ...

0 Anas Awadalla, et al. ∙

research

∙ 10/07/2022

Measuring and Narrowing the Compositionality Gap in Language Models

We investigate the ability of language models to perform compositional r...

0 Ofir Press, et al. ∙

research

∙ 07/02/2022

INSCIT: Information-Seeking Conversations with Mixed-Initiative Interactions

In an information-seeking conversation, a user converses with an agent t...

0 Zeqiu Wu, et al. ∙

research

∙ 05/25/2022

Revisiting Calibration for Question Answering

Model calibration aims to adjust (calibrate) models' confidence so that ...

2 Chenglei Si, et al. ∙

research

∙ 02/25/2022

Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?

Large language models (LMs) are able to in-context learn – perform a new...

2 Sewon Min, et al. ∙

research

∙ 10/29/2021

MetaICL: Learning to Learn In Context

We introduce MetaICL (Meta-training for In-Context Learning), a new meta...

0 Sewon Min, et al. ∙

research

∙ 08/09/2021

Noisy Channel Language Model Prompting for Few-Shot Text Classification

We introduce a noisy channel approach for language model prompting in fe...

0 Sewon Min, et al. ∙

research

∙ 07/05/2021

FaVIQ: FAct Verification from Information-seeking Questions

Despite significant interest in developing general purpose fact checking...

0 Jungsoo Park, et al. ∙

research

∙ 04/17/2021

Joint Passage Ranking for Diverse Multi-Answer Retrieval

We study multi-answer retrieval, an under-explored problem that requires...

0 Sewon Min, et al. ∙

research

∙ 01/01/2021

NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned

We review the EfficientQA competition from NeurIPS 2020. The competition...

15 Sewon Min, et al. ∙

research

∙ 10/21/2020

RECONSIDER: Re-Ranking using Span-Focused Cross-Attention for Open Domain Question Answering

State-of-the-art Machine Reading Comprehension (MRC) models for Open-dom...

0 Srinivasan Iyer, et al. ∙

research

∙ 10/06/2020

Efficient One-Pass End-to-End Entity Linking for Questions

We present ELQ, a fast end-to-end entity linking model for questions, wh...

0 Belinda Z. Li, et al. ∙

research

∙ 04/22/2020

AmbigQA: Answering Ambiguous Open-domain Questions

Ambiguity is inherent to open-domain question answering; especially when...

7 Sewon Min, et al. ∙

research

∙ 04/10/2020

Dense Passage Retrieval for Open-Domain Question Answering

Open-domain question answering relies on efficient passage retrieval to ...

0 Vladimir Karpukhin, et al. ∙

research

∙ 11/10/2019

Knowledge Guided Text Retrieval and Reading for Open Domain Question Answering

This paper presents a general approach for open-domain question answerin...

30 Sewon Min, et al. ∙

research

∙ 09/25/2019

Question Answering is a Format; When is it Useful?

Recent years have seen a dramatic expansion of tasks and datasets posed ...

0 Matt Gardner, et al. ∙

research

∙ 09/11/2019

A Discrete Hard EM Approach for Weakly Supervised Question Answering

Many question answering (QA) tasks only provide weak supervision for how...

0 Sewon Min, et al. ∙

research

∙ 06/07/2019

Multi-hop Reading Comprehension through Question Decomposition and Rescoring

Multi-hop Reading Comprehension (RC) requires reasoning and aggregation ...

2 Sewon Min, et al. ∙

research

∙ 06/07/2019

Compositional Questions Do Not Necessitate Multi-hop Reasoning

Multi-hop reading comprehension (RC) questions are challenging because t...

6 Sewon Min, et al. ∙

research

∙ 05/21/2018

Efficient and Robust Question Answering from Minimal Context over Documents

Neural models for question answering (QA) over documents have achieved s...

0 Sewon Min, et al. ∙

research

∙ 11/06/2017

Neural Speed Reading via Skim-RNN

Inspired by the principles of speed reading, we introduce Skim-RNN, a re...

0 Minjoon Seo, et al. ∙

research

∙ 02/07/2017

Question Answering through Transfer Learning from Large Fine-grained Supervision Data

We show that the task of question answering (QA) can significantly benef...

0 Sewon Min, et al. ∙

research

∙ 06/14/2016

Query-Reduction Networks for Question Answering

In this paper, we study the problem of question answering when reasoning...

0 Minjoon Seo, et al. ∙

Sewon Min

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