Search Results for author:
Found 33 papers, 26 papers with code
From One to Many: Expanding the Scope of Toxicity Mitigation in Language Models
To date, toxicity mitigation in language models has almost entirely been focused on single-language settings.
MultiContrievers: Analysis of Dense Retrieval Representations
Dense retrievers compress source documents into (possibly lossy) vector representations, yet there is little analysis of what information is lost versus preserved, and how it affects downstream tasks.
Rank-without-GPT: Building GPT-Independent Listwise Rerankers on Open-Source Large Language Models
However, current works in this direction all depend on the GPT models, making it a single point of failure in scientific reproducibility.
Goodtriever: Adaptive Toxicity Mitigation with Retrieval-augmented Models
Considerable effort has been dedicated to mitigating toxicity, but existing methods often require drastic modifications to model parameters or the use of computationally intensive auxiliary models.
On the Challenges of Using Black-Box APIs for Toxicity Evaluation in Research
We evaluate the implications of these changes on the reproducibility of findings that compare the relative merits of models and methods that aim to curb toxicity.
Mini-Model Adaptation: Efficiently Extending Pretrained Models to New Languages via Aligned Shallow Training
Prior work shows that it is possible to expand pretrained Masked Language Models (MLMs) to new languages by learning a new set of embeddings, while keeping the transformer body frozen.
Task-aware Retrieval with Instructions
We study the problem of retrieval with instructions, where users of a retrieval system explicitly describe their intent along with their queries.
EditEval: An Instruction-Based Benchmark for Text Improvements
Evaluation of text generation to date has primarily focused on content created sequentially, rather than improvements on a piece of text.
PEER: A Collaborative Language Model
Textual content is often the output of a collaborative writing process: We start with an initial draft, ask for suggestions, and repeatedly make changes.
Atlas: Few-shot Learning with Retrieval Augmented Language Models
Retrieval augmented models are known to excel at knowledge intensive tasks without the need for as many parameters, but it is unclear whether they work in few-shot settings.
Ranked #1 on
Question Answering
on Natural Questions
Improving Wikipedia Verifiability with AI
Hence, maintaining and improving the quality of Wikipedia references is an important challenge and there is a pressing need for better tools to assist humans in this effort.
Autoregressive Search Engines: Generating Substrings as Document Identifiers
Knowledge-intensive language tasks require NLP systems to both provide the correct answer and retrieve supporting evidence for it in a given corpus.
Reasoning over Public and Private Data in Retrieval-Based Systems
We first define the PUBLIC-PRIVATE AUTOREGRESSIVE INFORMATION RETRIEVAL (PAIR) privacy framework for the novel retrieval setting over multiple privacy scopes.
Ranked #1 on
Multi-hop Question Answering
on ConcurrentQA
The Web Is Your Oyster -- Knowledge-Intensive NLP against a Very Large Web Corpus
In order to address increasing demands of real-world applications, the research for knowledge-intensive NLP (KI-NLP) should advance by capturing the challenges of a truly open-domain environment: web-scale knowledge, lack of structure, inconsistent quality and noise.
Boosted Dense Retriever
DrBoost is trained in stages: each component model is learned sequentially and specialized by focusing only on retrieval mistakes made by the current ensemble.
Salient Phrase Aware Dense Retrieval: Can a Dense Retriever Imitate a Sparse One?
Despite their recent popularity and well-known advantages, dense retrievers still lag behind sparse methods such as BM25 in their ability to reliably match salient phrases and rare entities in the query and to generalize to out-of-domain data.
Ranked #2 on
Passage Retrieval
on EntityQuestions
A Few More Examples May Be Worth Billions of Parameters
We investigate the dynamics of increasing the number of model parameters versus the number of labeled examples across a wide variety of tasks.
Challenges in Generalization in Open Domain Question Answering
Recent work on Open Domain Question Answering has shown that there is a large discrepancy in model performance between novel test questions and those that largely overlap with training questions.
Domain-matched Pre-training Tasks for Dense Retrieval
Pre-training on larger datasets with ever increasing model size is now a proven recipe for increased performance across almost all NLP tasks.
Ranked #2 on
Passage Retrieval
on Natural Questions
(using extra training data)
PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them
We introduce a new QA-pair retriever, RePAQ, to complement PAQ.
NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned
We review the EfficientQA competition from NeurIPS 2020.
Pretrained Language Models for Biomedical and Clinical Tasks: Understanding and Extending the State-of-the-Art
A large array of pretrained models are available to the biomedical NLP (BioNLP) community.
Answering Complex Open-Domain Questions with Multi-Hop Dense Retrieval
We propose a simple and efficient multi-hop dense retrieval approach for answering complex open-domain questions, which achieves state-of-the-art performance on two multi-hop datasets, HotpotQA and multi-evidence FEVER.
Ranked #14 on
Question Answering
on HotpotQA
KILT: a Benchmark for Knowledge Intensive Language Tasks
We test both task-specific and general baselines, evaluating downstream performance in addition to the ability of the models to provide provenance.
Ranked #3 on
Entity Linking
on KILT: WNED-CWEB
Question and Answer Test-Train Overlap in Open-Domain Question Answering Datasets
We also find that 30% of test-set questions have a near-duplicate paraphrase in their corresponding training sets.
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
Large pre-trained language models have been shown to store factual knowledge in their parameters, and achieve state-of-the-art results when fine-tuned on downstream NLP tasks.
Ranked #4 on
Question Answering
on WebQuestions
How Context Affects Language Models' Factual Predictions
When pre-trained on large unsupervised textual corpora, language models are able to store and retrieve factual knowledge to some extent, making it possible to use them directly for zero-shot cloze-style question answering.
Dense Passage Retrieval for Open-Domain Question Answering
Open-domain question answering relies on efficient passage retrieval to select candidate contexts, where traditional sparse vector space models, such as TF-IDF or BM25, are the de facto method.
Ranked #1 on
Question Answering
on NaturalQA
Unsupervised Question Decomposition for Question Answering
We aim to improve question answering (QA) by decomposing hard questions into simpler sub-questions that existing QA systems are capable of answering.
MLQA: Evaluating Cross-lingual Extractive Question Answering
An alternative to building large monolingual training datasets is to develop cross-lingual systems which can transfer to a target language without requiring training data in that language.
Language Models as Knowledge Bases?
Recent progress in pretraining language models on large textual corpora led to a surge of improvements for downstream NLP tasks.
Unsupervised Question Answering by Cloze Translation
We approach this problem by first learning to generate context, question and answer triples in an unsupervised manner, which we then use to synthesize Extractive QA training data automatically.
Interpretation of Natural Language Rules in Conversational Machine Reading
This task requires both the interpretation of rules and the application of background knowledge.
