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Found 18 papers, 11 papers with code
Knowledge-Enriched Natural Language Generation
Knowledge-enriched text generation poses unique challenges in modeling and learning, driving active research in several core directions, ranging from integrated modeling of neural representations and symbolic information in the sequential/hierarchical/graphical structures, learning without direct supervisions due to the cost of structured annotation, efficient optimization and inference with massive and global constraints, to language grounding on multiple modalities, and generative reasoning with implicit commonsense knowledge and background knowledge.
Measuring Data
We identify the task of measuring data to quantitatively characterize the composition of machine learning data and datasets.
Are Hard Examples also Harder to Explain? A Study with Human and Model-Generated Explanations
We observe that (1) GPT-3 explanations are as grammatical as human explanations regardless of the hardness of the test samples, (2) for easy examples, GPT-3 generates highly supportive explanations but human explanations are more generalizable, and (3) for hard examples, human explanations are significantly better than GPT-3 explanations both in terms of label-supportiveness and generalizability judgements.
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.
Conformal Predictor for Improving Zero-shot Text Classification Efficiency
Pre-trained language models (PLMs) have been shown effective for zero-shot (0shot) text classification.
SEAL : Interactive Tool for Systematic Error Analysis and Labeling
With the advent of Transformers, large language models (LLMs) have saturated well-known NLP benchmarks and leaderboards with high aggregate performance.
Evaluate & Evaluation on the Hub: Better Best Practices for Data and Model Measurements
We introduce Evaluate and Evaluation on the Hub --a set of tools to facilitate the evaluation of models and datasets in ML.
Interactive Model Cards: A Human-Centered Approach to Model Documentation
Deep learning models for natural language processing (NLP) are increasingly adopted and deployed by analysts without formal training in NLP or machine learning (ML).
iSEA: An Interactive Pipeline for Semantic Error Analysis of NLP Models
Error analysis in NLP models is essential to successful model development and deployment.
P-Adapters: Robustly Extracting Factual Information from Language Models with Diverse Prompts
They take LLM embeddings as input and output continuous prompts that are used to query the LLM.
HydraSum - Disentangling Stylistic Features in Text Summarization using Multi-Decoder Models
Existing abstractive summarization models lack explicit control mechanisms that would allow users to influence the stylistic features of the model outputs.
BookSum: A Collection of Datasets for Long-form Narrative Summarization
The majority of available text summarization datasets include short-form source documents that lack long-range causal and temporal dependencies, and often contain strong layout and stylistic biases.
Stage-wise Fine-tuning for Graph-to-Text Generation
Graph-to-text generation has benefited from pre-trained language models (PLMs) in achieving better performance than structured graph encoders.
Ranked #3 on
Data-to-Text Generation
on WebNLG
(using extra training data)
Robustness Gym: Unifying the NLP Evaluation Landscape
Despite impressive performance on standard benchmarks, deep neural networks are often brittle when deployed in real-world systems.
CTRLsum: Towards Generic Controllable Text Summarization
Our approach enables users to control multiple aspects of generated summaries by interacting with the summarization system through textual input in the form of a set of keywords or descriptive prompts.
What's New? Summarizing Contributions in Scientific Literature
To overcome this problem, we introduce a new task of disentangled paper summarization, which seeks to generate separate summaries for the paper contributions and the context of the work, making it easier to identify the key findings shared in articles.
Char2Subword: Extending the Subword Embedding Space Using Robust Character Compositionality
To alleviate these challenges, we propose a character-based subword module (char2subword) that learns the subword embedding table in pre-trained models like BERT.
CoCo: Controllable Counterfactuals for Evaluating Dialogue State Trackers
Dialogue state trackers have made significant progress on benchmark datasets, but their generalization capability to novel and realistic scenarios beyond the held-out conversations is less understood.
Ranked #2 on
Multi-domain Dialogue State Tracking
on MULTIWOZ 2.1
(using extra training data)
Dialogue State Tracking
Multi-domain Dialogue State Tracking
