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Found 16 papers, 6 papers with code
Simfluence: Modeling the Influence of Individual Training Examples by Simulating Training Runs
To study such interactions, we propose Simfluence, a new paradigm for TDA where the goal is not to produce a single influence score per example, but instead a training run simulator: the user asks, ``If my model had trained on example $z_1$, then $z_2$, ..., then $z_n$, how would it behave on $z_{test}$?
Towards Tracing Factual Knowledge in Language Models Back to the Training Data
In this paper, we propose the problem of fact tracing: identifying which training examples taught an LM to generate a particular factual assertion.
Retrieval-guided Counterfactual Generation for QA
To address these challenges, we develop a Retrieve-Generate-Filter(RGF) technique to create counterfactual evaluation and training data with minimal human supervision.
The MultiBERTs: BERT Reproductions for Robustness Analysis
Experiments with pre-trained models such as BERT are often based on a single checkpoint.
Measuring and Reducing Gendered Correlations in Pre-trained Models
Pre-trained models have revolutionized natural language understanding.
Do Language Embeddings Capture Scales?
Pretrained Language Models (LMs) have been shown to possess significant linguistic, common sense, and factual knowledge.
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.
What Happens To BERT Embeddings During Fine-tuning?
While there has been much recent work studying how linguistic information is encoded in pre-trained sentence representations, comparatively little is understood about how these models change when adapted to solve downstream tasks.
Asking without Telling: Exploring Latent Ontologies in Contextual Representations
The success of pretrained contextual encoders, such as ELMo and BERT, has brought a great deal of interest in what these models learn: do they, without explicit supervision, learn to encode meaningful notions of linguistic structure?
jiant: A Software Toolkit for Research on General-Purpose Text Understanding Models
We introduce jiant, an open source toolkit for conducting multitask and transfer learning experiments on English NLU tasks.
BERT Rediscovers the Classical NLP Pipeline
Pre-trained text encoders have rapidly advanced the state of the art on many NLP tasks.
What do you learn from context? Probing for sentence structure in contextualized word representations
The jiant toolkit for general-purpose text understanding models
Looking for ELMo's friends: Sentence-Level Pretraining Beyond Language Modeling
Work on the problem of contextualized word representation—the development of reusable neural network components for sentence understanding—has recently seen a surge of progress centered on the unsupervised pretraining task of language modeling with methods like ELMo (Peters et al., 2018).
Probing What Different NLP Tasks Teach Machines about Function Word Comprehension
Our results show that pretraining on language modeling performs the best on average across our probing tasks, supporting its widespread use for pretraining state-of-the-art NLP models, and CCG supertagging and NLI pretraining perform comparably.
Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling
Natural language understanding has recently seen a surge of progress with the use of sentence encoders like ELMo (Peters et al., 2018a) and BERT (Devlin et al., 2019) which are pretrained on variants of language modeling.
WikiAtomicEdits: A Multilingual Corpus of Wikipedia Edits for Modeling Language and Discourse
We release a corpus of 43 million atomic edits across 8 languages.
