634 research outputs found
How Decoding Strategies Affect the Verifiability of Generated Text
Recent progress in pre-trained language models led to systems that are able to generate text of an increasingly high quality. While several works have investigated the fluency and grammatical correctness of such models, it is still unclear to which extent the generated text is consistent with factual world knowledge. Here, we go beyond fluency and also investigate the verifiability of text generated by state-of-the-art pre-trained language models. A generated sentence is verifiable if it can be corroborated or disproved by Wikipedia, and we find that the verifiability of generated text strongly depends on the decoding strategy. In particular, we discover a tradeoff between factuality (i.e., the ability of generating Wikipedia corroborated text) and repetitiveness. While decoding strategies such as top-k and nucleus sampling lead to less repetitive generations, they also produce less verifiable text. Based on these finding, we introduce a simple and effective decoding strategy which, in comparison to previously used decoding strategies, produces less repetitive and more verifiable text
How Decoding Strategies Affect the Verifiability of Generated Text
Recent progress in pre-trained language models led to systems that are able
to generate text of an increasingly high quality. While several works have
investigated the fluency and grammatical correctness of such models, it is
still unclear to which extent the generated text is consistent with factual
world knowledge. Here, we go beyond fluency and also investigate the
verifiability of text generated by state-of-the-art pre-trained language
models. A generated sentence is verifiable if it can be corroborated or
disproved by Wikipedia, and we find that the verifiability of generated text
strongly depends on the decoding strategy. In particular, we discover a
tradeoff between factuality (i.e., the ability of generating Wikipedia
corroborated text) and repetitiveness. While decoding strategies such as top-k
and nucleus sampling lead to less repetitive generations, they also produce
less verifiable text. Based on these finding, we introduce a simple and
effective decoding strategy which, in comparison to previously used decoding
strategies, produces less repetitive and more verifiable text.Comment: accepted at Findings of EMNLP 202
The Science of Detecting LLM-Generated Texts
The emergence of large language models (LLMs) has resulted in the production
of LLM-generated texts that is highly sophisticated and almost
indistinguishable from texts written by humans. However, this has also sparked
concerns about the potential misuse of such texts, such as spreading
misinformation and causing disruptions in the education system. Although many
detection approaches have been proposed, a comprehensive understanding of the
achievements and challenges is still lacking. This survey aims to provide an
overview of existing LLM-generated text detection techniques and enhance the
control and regulation of language generation models. Furthermore, we emphasize
crucial considerations for future research, including the development of
comprehensive evaluation metrics and the threat posed by open-source LLMs, to
drive progress in the area of LLM-generated text detection
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. However, their ability to access and precisely manipulate
knowledge is still limited, and hence on knowledge-intensive tasks, their
performance lags behind task-specific architectures. Additionally, providing
provenance for their decisions and updating their world knowledge remain open
research problems. Pre-trained models with a differentiable access mechanism to
explicit non-parametric memory can overcome this issue, but have so far been
only investigated for extractive downstream tasks. We explore a general-purpose
fine-tuning recipe for retrieval-augmented generation (RAG) -- models which
combine pre-trained parametric and non-parametric memory for language
generation. We introduce RAG models where the parametric memory is a
pre-trained seq2seq model and the non-parametric memory is a dense vector index
of Wikipedia, accessed with a pre-trained neural retriever. We compare two RAG
formulations, one which conditions on the same retrieved passages across the
whole generated sequence, the other can use different passages per token. We
fine-tune and evaluate our models on a wide range of knowledge-intensive NLP
tasks and set the state-of-the-art on three open domain QA tasks, outperforming
parametric seq2seq models and task-specific retrieve-and-extract architectures.
For language generation tasks, we find that RAG models generate more specific,
diverse and factual language than a state-of-the-art parametric-only seq2seq
baseline.Comment: Accepted at NeurIPS 202
Enabling Large Language Models to Generate Text with Citations
Large language models (LLMs) have emerged as a widely-used tool for
information seeking, but their generated outputs are prone to hallucination. In
this work, our aim is to allow LLMs to generate text with citations, improving
their factual correctness and verifiability. Existing work mainly relies on
commercial search engines and human evaluation, making it challenging to
reproduce and compare different modeling approaches. We propose ALCE, the first
benchmark for Automatic LLMs' Citation Evaluation. ALCE collects a diverse set
of questions and retrieval corpora and requires building end-to-end systems to
retrieve supporting evidence and generate answers with citations. We develop
automatic metrics along three dimensions -- fluency, correctness, and citation
quality -- and demonstrate their strong correlation with human judgements. Our
experiments with state-of-the-art LLMs and novel prompting strategies show that
current systems have considerable room for improvement -- For example, on the
ELI5 dataset, even the best models lack complete citation support 50% of the
time. Our analyses further highlight promising future directions, including
developing better retrievers, advancing long-context LLMs, and improving the
ability to synthesize information from multiple sources.Comment: Accepted by EMNLP 2023. Code and data are available at
https://github.com/princeton-nlp/ALC
Autoregressive Entity Retrieval
Entities are at the center of how we represent and aggregate knowledge. For
instance, Encyclopedias such as Wikipedia are structured by entities (e.g., one
per Wikipedia article). The ability to retrieve such entities given a query is
fundamental for knowledge-intensive tasks such as entity linking and
open-domain question answering. Current approaches can be understood as
classifiers among atomic labels, one for each entity. Their weight vectors are
dense entity representations produced by encoding entity meta information such
as their descriptions. This approach has several shortcomings: (i) context and
entity affinity is mainly captured through a vector dot product, potentially
missing fine-grained interactions; (ii) a large memory footprint is needed to
store dense representations when considering large entity sets; (iii) an
appropriately hard set of negative data has to be subsampled at training time.
In this work, we propose GENRE, the first system that retrieves entities by
generating their unique names, left to right, token-by-token in an
autoregressive fashion. This mitigates the aforementioned technical issues
since: (i) the autoregressive formulation directly captures relations between
context and entity name, effectively cross encoding both; (ii) the memory
footprint is greatly reduced because the parameters of our encoder-decoder
architecture scale with vocabulary size, not entity count; (iii) the softmax
loss is computed without subsampling negative data. We experiment with more
than 20 datasets on entity disambiguation, end-to-end entity linking and
document retrieval tasks, achieving new state-of-the-art or very competitive
results while using a tiny fraction of the memory footprint of competing
systems. Finally, we demonstrate that new entities can be added by simply
specifying their names. Code and pre-trained models at
https://github.com/facebookresearch/GENRE.Comment: Accepted (spotlight) at International Conference on Learning
Representations (ICLR) 2021. Code at
https://github.com/facebookresearch/GENRE. 20 pages, 9 figures, 8 table
Survey on securing data storage in the cloud
Cloud Computing has become a well-known primitive nowadays; many researchers and companies are embracing this fascinating technology with feverish haste. In the meantime, security and privacy challenges are brought forward while the number of cloud storage user increases expeditiously. In this work, we conduct an in-depth survey on recent research activities of cloud storage security in association with cloud computing. After an overview of the cloud storage system and its security problem, we focus on the key security requirement triad, i.e., data integrity, data confidentiality, and availability. For each of the three security objectives, we discuss the new unique challenges faced by the cloud storage services, summarize key issues discussed in the current literature, examine, and compare the existing and emerging approaches proposed to meet those new challenges, and point out possible extensions and futuristic research opportunities. The goal of our paper is to provide a state-of-the-art knowledge to new researchers who would like to join this exciting new field
Anonymous authentication of membership in dynamic groups
Thesis (S.B. and M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.Includes bibliographical references (leaves 34-36).by Todd C. Parnell.S.B.and M.Eng
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