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Found 22 papers, 10 papers with code
The Case for a Single Model that can Both Generate Continuations and Fill-in-the-Blank
While previous work has tackled this problem with models trained specifically to do fill in the blank, a more useful model is one that can effectively perform _both_ FitB and continuation tasks.
The Evolution of LLM Adoption in Industry Data Curation Practices
This paper explores the evolution of LLM adoption among practitioners at a large technology company, evaluating the impact of LLMs in data curation tasks through participants' perceptions, integration strategies, and reported usage scenarios.
Who's asking? User personas and the mechanics of latent misalignment
In fact, we find manipulating user persona to be even more effective for eliciting harmful content than direct attempts to control model refusal.
Automatic Histograms: Leveraging Language Models for Text Dataset Exploration
Making sense of unstructured text datasets is perennially difficult, yet increasingly relevant with Large Language Models.
Understanding the Dataset Practitioners Behind Large Language Model Development
We find that although data quality is a top priority, there is little consensus around what data quality is and how to evaluate it.
LLM Comparator: Visual Analytics for Side-by-Side Evaluation of Large Language Models
Automatic side-by-side evaluation has emerged as a promising approach to evaluating the quality of responses from large language models (LLMs).
SoUnD Framework: Analyzing (So)cial Representation in (Un)structured (D)ata
The unstructured nature of data used in foundation model development is a challenge to systematic analyses for making data use and documentation decisions.
Data Similarity is Not Enough to Explain Language Model Performance
Large language models achieve high performance on many but not all downstream tasks.
A Pretrainer's Guide to Training Data: Measuring the Effects of Data Age, Domain Coverage, Quality, & Toxicity
Second, we explore the effect of quality and toxicity filters, showing a trade-off between performance on standard benchmarks and risk of toxic generations.
Visualizing Linguistic Diversity of Text Datasets Synthesized by Large Language Models
We present LinguisticLens, a novel inter-active visualization tool for making sense of and analyzing syntactic diversity of LLM-generated datasets.
PaLM 2 Technical Report
Through extensive evaluations on English and multilingual language, and reasoning tasks, we demonstrate that PaLM 2 has significantly improved quality on downstream tasks across different model sizes, while simultaneously exhibiting faster and more efficient inference compared to PaLM.
Ranked #1 on
Question Answering
on StrategyQA
The Case for a Single Model that can Both Generate Continuations and Fill in the Blank
The task of inserting text into a specified position in a passage, known as fill in the blank (FitB), is useful for a variety of applications where writers interact with a natural language generation (NLG) system to craft text.
PaLM: Scaling Language Modeling with Pathways
To further our understanding of the impact of scale on few-shot learning, we trained a 540-billion parameter, densely activated, Transformer language model, which we call Pathways Language Model PaLM.
Ranked #1 on
Coreference Resolution
on Winograd Schema Challenge
A Recipe For Arbitrary Text Style Transfer with Large Language Models
In this paper, we leverage large language models (LMs) to perform zero-shot text style transfer.
Wordcraft: a Human-AI Collaborative Editor for Story Writing
As neural language models grow in effectiveness, they are increasingly being applied in real-world settings.
An Interpretability Illusion for BERT
We describe an "interpretability illusion" that arises when analyzing the BERT model.
Evaluating Attribution for Graph Neural Networks
Interpretability of machine learning models is critical to scientific understanding, AI safety, as well as debugging.
The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for NLP Models
We present the Language Interpretability Tool (LIT), an open-source platform for visualization and understanding of NLP models.
Visualizing and Measuring the Geometry of BERT
Transformer architectures show significant promise for natural language processing.
Neural Networks Trained on Natural Scenes Exhibit Gestalt Closure
The Gestalt laws of perceptual organization, which describe how visual elements in an image are grouped and interpreted, have traditionally been thought of as innate despite their ecological validity.
Similar Image Search for Histopathology: SMILY
SMILY may be a useful general-purpose tool in the pathologist's arsenal, to improve the efficiency of searching large archives of histopathology images, without the need to develop and implement specific tools for each application.
Embedding Projector: Interactive Visualization and Interpretation of Embeddings
Embeddings are ubiquitous in machine learning, appearing in recommender systems, NLP, and many other applications.
