Search Results for author:
Found 20 papers, 12 papers with code
On Positional Bias of Faithfulness for Long-form Summarization
Large Language Models (LLMs) often exhibit positional bias in long-context settings, under-attending to information in the middle of inputs.
Beyond the Chat: Executable and Verifiable Text-Editing with LLMs
Conversational interfaces powered by Large Language Models (LLMs) have recently become a popular way to obtain feedback during document editing.
XGen-7B Technical Report
Most open-source LLMs, on the other hand, are limited in their ability to support longer sequence lengths, which is a key requirement for many tasks that require inference over an input context.
Did You Read the Instructions? Rethinking the Effectiveness of Task Definitions in Instruction Learning
Large language models (LLMs) have shown impressive performance in following natural language instructions to solve unseen tasks.
SWiPE: A Dataset for Document-Level Simplification of Wikipedia Pages
Text simplification research has mostly focused on sentence-level simplification, even though many desirable edits - such as adding relevant background information or reordering content - may require document-level context.
Improving Factual Consistency in Summarization with Compression-Based Post-Editing
We propose to use sentence-compression data to train the post-editing model to take a summary with extrinsic entity errors marked with special tokens and output a compressed, well-formed summary with those errors removed.
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.
Exploring Neural Models for Query-Focused Summarization
Query-focused summarization (QFS) aims to produce summaries that answer particular questions of interest, enabling greater user control and personalization.
CaPE: Contrastive Parameter Ensembling for Reducing Hallucination in Abstractive Summarization
Then, we fine-tune a base summarization model, which is trained on all training samples, on the clean (noisy) subset to obtain an \textit{expert} (\textit{anti-expert}) model.
Goodwill Hunting: Analyzing and Repurposing Off-the-Shelf Named Entity Linking Systems
If not, how easily can such a system be repurposed for their use case?
SummVis: Interactive Visual Analysis of Models, Data, and Evaluation for Text Summarization
Novel neural architectures, training strategies, and the availability of large-scale corpora haven been the driving force behind recent progress in abstractive text summarization.
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.
Investigating Gender Bias in Language Models Using Causal Mediation Analysis
As a case study, we apply this methodology to analyzing gender bias in pre-trained Transformer language models.
Profile Prediction: An Alignment-Based Pre-Training Task for Protein Sequence Models
Recent deep-learning approaches to protein prediction have shown that pre-training on unlabeled data can yield useful representations for downstream tasks.
BERTology Meets Biology: Interpreting Attention in Protein Language Models
Transformer architectures have proven to learn useful representations for protein classification and generation tasks.
Causal Mediation Analysis for Interpreting Neural NLP: The Case of Gender Bias
Common methods for interpreting neural models in natural language processing typically examine either their structure or their behavior, but not both.
A Multiscale Visualization of Attention in the Transformer Model
The Transformer is a sequence model that forgoes traditional recurrent architectures in favor of a fully attention-based approach.
Analyzing the Structure of Attention in a Transformer Language Model
The Transformer is a fully attention-based alternative to recurrent networks that has achieved state-of-the-art results across a range of NLP tasks.
Visualizing Attention in Transformer-Based Language Representation Models
We present an open-source tool for visualizing multi-head self-attention in Transformer-based language representation models.
