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Found 25 papers, 19 papers with code
GEE! Grammar Error Explanation with Large Language Models
To address this gap, we propose the task of grammar error explanation, where a system needs to provide one-sentence explanations for each grammatical error in a pair of erroneous and corrected sentences.
FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation
Evaluating the factuality of long-form text generated by large language models (LMs) is non-trivial because (1) generations often contain a mixture of supported and unsupported pieces of information, making binary judgments of quality inadequate, and (2) human evaluation is time-consuming and costly.
Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense
To increase the robustness of AI-generated text detection to paraphrase attacks, we introduce a simple defense that relies on retrieving semantically-similar generations and must be maintained by a language model API provider.
Stealing the Decoding Algorithms of Language Models
A key component of generating text from modern language models (LM) is the selection and tuning of decoding algorithms.
LongEval: Guidelines for Human Evaluation of Faithfulness in Long-form Summarization
Motivated by our survey, we present LongEval, a set of guidelines for human evaluation of faithfulness in long-form summaries that addresses the following challenges: (1) How can we achieve high inter-annotator agreement on faithfulness scores?
Exploring Document-Level Literary Machine Translation with Parallel Paragraphs from World Literature
Using Par3, we discover that expert literary translators prefer reference human translations over machine-translated paragraphs at a rate of 84%, while state-of-the-art automatic MT metrics do not correlate with those preferences.
SLING: Sino Linguistic Evaluation of Large Language Models
To understand what kinds of linguistic knowledge are encoded by pretrained Chinese language models (LMs), we introduce the benchmark of Sino LINGuistics (SLING), which consists of 38K minimal sentence pairs in Mandarin Chinese grouped into 9 high-level linguistic phenomena.
ezCoref: Towards Unifying Annotation Guidelines for Coreference Resolution
Large-scale, high-quality corpora are critical for advancing research in coreference resolution.
RankGen: Improving Text Generation with Large Ranking Models
Given an input sequence (or prefix), modern language models often assign high probabilities to output sequences that are repetitive, incoherent, or irrelevant to the prefix; as such, model-generated text also contains such artifacts.
RELIC: Retrieving Evidence for Literary Claims
Humanities scholars commonly provide evidence for claims that they make about a work of literature (e. g., a novel) in the form of quotations from the work.
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.
Few-shot Controllable Style Transfer for Low-Resource Multilingual Settings
When compared to prior work, our model achieves 2-3x better performance in formality transfer and code-mixing addition across seven languages.
Do Long-Range Language Models Actually Use Long-Range Context?
Language models are generally trained on short, truncated input sequences, which limits their ability to use discourse-level information present in long-range context to improve their predictions.
Hurdles to Progress in Long-form Question Answering
The task of long-form question answering (LFQA) involves retrieving documents relevant to a given question and using them to generate a paragraph-length answer.
Ranked #3 on
Question Answering
on KILT: ELI5
Weakly-Supervised Open-Retrieval Conversational Question Answering
Our method is more flexible as it can handle both span answers and freeform answers.
Long Document Summarization in a Low Resource Setting using Pretrained Language Models
Abstractive summarization is the task of compressing a long document into a coherent short document while retaining salient information.
An Analysis of Frame-skipping in Reinforcement Learning
In this paper, we investigate the role of the parameter $d$ in RL; $d$ is called the "frame-skip" parameter, since states in the Atari domain are images.
Reformulating Unsupervised Style Transfer as Paraphrase Generation
Modern NLP defines the task of style transfer as modifying the style of a given sentence without appreciably changing its semantics, which implies that the outputs of style transfer systems should be paraphrases of their inputs.
Thieves on Sesame Street! Model Extraction of BERT-based APIs
We study the problem of model extraction in natural language processing, in which an adversary with only query access to a victim model attempts to reconstruct a local copy of that model.
Generating Question-Answer Hierarchies
The process of knowledge acquisition can be viewed as a question-answer game between a student and a teacher in which the student typically starts by asking broad, open-ended questions before drilling down into specifics (Hintikka, 1981; Hakkarainen and Sintonen, 2002).
Syntactically Supervised Transformers for Faster Neural Machine Translation
Standard decoders for neural machine translation autoregressively generate a single target token per time step, which slows inference especially for long outputs.
Trick or TReAT: Thematic Reinforcement for Artistic Typography
An approach to make text visually appealing and memorable is semantic reinforcement - the use of visual cues alluding to the context or theme in which the word is being used to reinforce the message (e. g., Google Doodles).
Revisiting the Importance of Encoding Logic Rules in Sentiment Classification
We analyze the performance of different sentiment classification models on syntactically complex inputs like A-but-B sentences.
Hierarchical Multitask Learning for CTC-based Speech Recognition
Previous work has shown that neural encoder-decoder speech recognition can be improved with hierarchical multitask learning, where auxiliary tasks are added at intermediate layers of a deep encoder.
A Study of All-Convolutional Encoders for Connectionist Temporal Classification
We explore whether deep convolutional neural networks (CNNs) can be used effectively instead of RNNs as the "encoder" in CTC.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
