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Found 31 papers, 18 papers with code
Choisir le bon co-équipier pour la génération coopérative de texte (Choosing The Right Teammate For Cooperative Text Generation)
Nous évaluons leurs avantages et inconvénients, en explorant leur précision respective sur des tâches de classification, ainsi que leur impact sur la génération coopérative et leur coût de calcul, dans le cadre d’une stratégie de décodage état de l’art, basée sur une recherche arborescente de Monte-Carlo (MCTS).
Augmented Language Models: a Survey
This survey reviews works in which language models (LMs) are augmented with reasoning skills and the ability to use tools.
Toolformer: Language Models Can Teach Themselves to Use Tools
Language models (LMs) exhibit remarkable abilities to solve new tasks from just a few examples or textual instructions, especially at scale.
Ranked #7 on
Math Word Problem Solving
on MAWPS
LoRaLay: A Multilingual and Multimodal Dataset for Long Range and Layout-Aware Summarization
Text Summarization is a popular task and an active area of research for the Natural Language Processing community.
Unnatural Instructions: Tuning Language Models with (Almost) No Human Labor
We collect 64, 000 examples by prompting a language model with three seed examples of instructions and eliciting a fourth.
Galactica: A Large Language Model for Science
We believe these results demonstrate the potential for language models as a new interface for science.
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions.
RQUGE: Reference-Free Metric for Evaluating Question Generation by Answering the Question
The metric consists of a question-answering and a span scorer module, in which we use pre-trained models from the existing literature, and therefore, our metric can be used without further training.
MaskEval: Weighted MLM-Based Evaluation for Text Summarization and Simplification
In text summarization and simplification, system outputs must be evaluated along multiple dimensions such as relevance, factual consistency, fluency, and grammaticality, and a wide range of possible outputs could be of high quality.
Fine-tuned Language Models are Continual Learners
In spite of the limited success of Continual Learning we show that Language Models can be continual learners.
Which Discriminator for Cooperative Text Generation?
Language models generate texts by successively predicting probability distributions for next tokens given past ones.
TRUE: Re-evaluating Factual Consistency Evaluation
Grounded text generation systems often generate text that contains factual inconsistencies, hindering their real-world applicability.
Generative Cooperative Networks for Natural Language Generation
Generative Adversarial Networks (GANs) have known a tremendous success for many continuous generation tasks, especially in the field of image generation.
NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation
Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained on.
BEAMetrics: A Benchmark for Language Generation Evaluation Evaluation
There is currently no simple, unified way to compare, analyse or evaluate metrics across a representative set of tasks.
Skim-Attention: Learning to Focus via Document Layout
Motivated by human reading strategies, this paper presents Skim-Attention, a new attention mechanism that takes advantage of the structure of the document and its layout.
To Beam Or Not To Beam: That is a Question of Cooperation for Language GANs
Due to the discrete nature of words, language GANs require to be optimized from rewards provided by discriminator networks, via reinforcement learning methods.
Data-QuestEval: A Referenceless Metric for Data-to-Text Semantic Evaluation
QuestEval is a reference-less metric used in text-to-text tasks, that compares the generated summaries directly to the source text, by automatically asking and answering questions.
Rethinking Automatic Evaluation in Sentence Simplification
In the context of Sentence Simplification, this is particularly challenging: the task requires by nature to replace complex words with simpler ones that shares the same meaning.
QuestEval: Summarization Asks for Fact-based Evaluation
Summarization evaluation remains an open research problem: current metrics such as ROUGE are known to be limited and to correlate poorly with human judgments.
Synthetic Data Augmentation for Zero-Shot Cross-Lingual Question Answering
Coupled with the availability of large scale datasets, deep learning architectures have enabled rapid progress on the Question Answering task.
Toward Stance-based Personas for Opinionated Dialogues
In the context of chit-chat dialogues it has been shown that endowing systems with a persona profile is important to produce more coherent and meaningful conversations.
Project PIAF: Building a Native French Question-Answering Dataset
Motivated by the lack of data for non-English languages, in particular for the evaluation of downstream tasks such as Question Answering, we present a participatory effort to collect a native French Question Answering Dataset.
Ranked #4 on
Question Answering
on FQuAD
(using extra training data)
ColdGANs: Taming Language GANs with Cautious Sampling Strategies
Training regimes based on Maximum Likelihood Estimation (MLE) suffer from known limitations, often leading to poorly generated text sequences.
MLSUM: The Multilingual Summarization Corpus
We present MLSUM, the first large-scale MultiLingual SUMmarization dataset.
What BERT Sees: Cross-Modal Transfer for Visual Question Generation
Pre-trained language models have recently contributed to significant advances in NLP tasks.
Discriminative Adversarial Search for Abstractive Summarization
We introduce a novel approach for sequence decoding, Discriminative Adversarial Search (DAS), which has the desirable properties of alleviating the effects of exposure bias without requiring external metrics.
Ask to Learn: A Study on Curiosity-driven Question Generation
We propose a novel text generation task, namely Curiosity-driven Question Generation.
Conversational Question Answering
Machine Reading Comprehension
+3
Answers Unite! Unsupervised Metrics for Reinforced Summarization Models
Abstractive summarization approaches based on Reinforcement Learning (RL) have recently been proposed to overcome classical likelihood maximization.
Self-Attention Architectures for Answer-Agnostic Neural Question Generation
Neural architectures based on self-attention, such as Transformers, recently attracted interest from the research community, and obtained significant improvements over the state of the art in several tasks.
