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Found 40 papers, 18 papers with code
Larger language models do in-context learning differently
We next study semantically-unrelated label ICL (SUL-ICL), in which labels are semantically unrelated to their inputs (e. g., foo/bar instead of negative/positive), thereby forcing language models to learn the input-label mappings shown in in-context exemplars in order to perform the task.
Foundation Models for Decision Making: Problems, Methods, and Opportunities
In response to these developments, new paradigms are emerging for training foundation models to interact with other agents and perform long-term reasoning.
The Flan Collection: Designing Data and Methods for Effective Instruction Tuning
We study the design decisions of publicly available instruction tuning methods, and break down the development of Flan 2022 (Chung et al., 2022).
Large Language Models Encode Clinical Knowledge
To resolve this we introduce instruction prompt tuning, a parameter-efficient approach for aligning LLMs to new domains using a few exemplars.
Ranked #1 on
Question Answering
on MedQA-USMLE
Inverse scaling can become U-shaped
The Inverse Scaling Prize (McKenzie et al. 2022) identified eleven such inverse scaling tasks, evaluated on models of up to 280B parameters and up to 500 zettaFLOPs of training compute.
Scaling Instruction-Finetuned Language Models
We find that instruction finetuning with the above aspects dramatically improves performance on a variety of model classes (PaLM, T5, U-PaLM), prompting setups (zero-shot, few-shot, CoT), and evaluation benchmarks (MMLU, BBH, TyDiQA, MGSM, open-ended generation).
Ranked #1 on
Multi-task Language Understanding
on BBH-nlp
Cross-Lingual Question Answering
Multi-task Language Understanding
+1
Transcending Scaling Laws with 0.1% Extra Compute
This paper proposes UL2R, a method that substantially improves existing language models and their scaling curves with a relatively tiny amount of extra compute.
Ranked #1 on
Question Answering
on StrategyQA
Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them
BIG-Bench (Srivastava et al., 2022) is a diverse evaluation suite that focuses on tasks believed to be beyond the capabilities of current language models.
Mind's Eye: Grounded Language Model Reasoning through Simulation
By training solely on written text, current language models (LMs) miss the grounded experience of humans in the real-world -- their failure to relate language to the physical world causes knowledge to be misrepresented and obvious mistakes in their reasoning.
Language Models are Multilingual Chain-of-Thought Reasoners
Finally, we show that the multilingual reasoning abilities of language models extend to other tasks such as commonsense reasoning and word-in-context semantic judgment.
Rationale-Augmented Ensembles in Language Models
Recent research has shown that rationales, or step-by-step chains of thought, can be used to improve performance in multi-step reasoning tasks.
Emergent Abilities of Large Language Models
Scaling up language models has been shown to predictably improve performance and sample efficiency on a wide range of downstream tasks.
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models.
Least-to-Most Prompting Enables Complex Reasoning in Large Language Models
Although chain-of-thought prompting has shown impressive results on many natural language reasoning tasks, it often performs poorly on tasks which need to solve problems harder than the demonstration examples.
Ranked #14 on
Arithmetic Reasoning
on GSM8K
UL2: Unifying Language Learning Paradigms
Our model also achieve strong results at in-context learning, outperforming 175B GPT-3 on zero-shot SuperGLUE and tripling the performance of T5-XXL on one-shot summarization.
Ranked #7 on
Long-range modeling
on SCROLLS
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
Auto Debugging
on Big-bench Lite
Self-Consistency Improves Chain of Thought Reasoning in Language Models
Chain-of-thought prompting combined with pre-trained large language models has achieved encouraging results on complex reasoning tasks.
Ranked #9 on
Arithmetic Reasoning
on GSM8K
(using extra training data)
Calibrating Histopathology Image Classifiers using Label Smoothing
Moreover, we find that using model confidence as a proxy for annotator agreement also improves calibration and accuracy, suggesting that datasets without multiple annotators can still benefit from our proposed label smoothing methods via our proposed confidence-aware label smoothing methods.
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
We explore how generating a chain of thought -- a series of intermediate reasoning steps -- significantly improves the ability of large language models to perform complex reasoning.
Frequency Effects on Syntactic Rule Learning in Transformers
Pre-trained language models perform well on a variety of linguistic tasks that require symbolic reasoning, raising the question of whether such models implicitly represent abstract symbols and rules.
Good-Enough Example Extrapolation
This paper asks whether extrapolating the hidden space distribution of text examples from one class onto another is a valid inductive bias for data augmentation.
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.
Finetuned Language Models Are Zero-Shot Learners
We show that instruction tuning -- finetuning language models on a collection of tasks described via instructions -- substantially improves zero-shot performance on unseen tasks.
Ranked #1 on
Question Answering
on StoryCloze
Language Model Augmented Relevance Score
Although automated metrics are commonly used to evaluate NLG systems, they often correlate poorly with human judgements.
Modulating Language Models with Emotions
Generating context-aware language that embodies diverse emotions is an important step towards building empathetic NLP systems.
The MultiBERTs: BERT Reproductions for Robustness Analysis
Experiments with pre-trained models such as BERT are often based on a single checkpoint.
Linguistic Complexity Loss in Text-Based Therapy
The recent advent of online text-based therapy presents a new opportunity to analyze the complexity loss paradox in a novel operationalization: linguistic complexity loss in text-based therapy conversations.
A Cognitive Regularizer for Language Modeling
The uniform information density (UID) hypothesis, which posits that speakers behaving optimally tend to distribute information uniformly across a linguistic signal, has gained traction in psycholinguistics as an explanation for certain syntactic, morphological, and prosodic choices.
A Survey of Data Augmentation Approaches for NLP
In this paper, we present a comprehensive and unifying survey of data augmentation for NLP by summarizing the literature in a structured manner.
Mitigating Political Bias in Language Models Through Reinforced Calibration
Current large-scale language models can be politically biased as a result of the data they are trained on, potentially causing serious problems when they are deployed in real-world settings.
Few-Shot Text Classification with Triplet Networks, Data Augmentation, and Curriculum Learning
Few-shot text classification is a fundamental NLP task in which a model aims to classify text into a large number of categories, given only a few training examples per category.
A Petri Dish for Histopathology Image Analysis
With the rise of deep learning, there has been increased interest in using neural networks for histopathology image analysis, a field that investigates the properties of biopsy or resected specimens traditionally manually examined under a microscope by pathologists.
Text Augmentation in a Multi-Task View
Traditional data augmentation aims to increase the coverage of the input distribution by generating augmented examples that strongly resemble original samples in an online fashion where augmented examples dominate training.
An Empirical Survey of Unsupervised Text Representation Methods on Twitter Data
The field of NLP has seen unprecedented achievements in recent years.
Learn like a Pathologist: Curriculum Learning by Annotator Agreement for Histopathology Image Classification
Based on the nature of histopathology images, a range of difficulty inherently exists among examples, and, since medical datasets are often labeled by multiple annotators, annotator agreement can be used as a natural proxy for the difficulty of a given example.
What Are People Asking About COVID-19? A Question Classification Dataset
We present COVID-Q, a set of 1, 690 questions about COVID-19 from 13 sources, which we annotate into 15 question categories and 207 question clusters.
Difficulty Translation in Histopathology Images
Our model comprises a scorer, which provides an output confidence to measure the difficulty of images, and an image translator, which learns to translate images from easy-to-classify to hard-to-classify using a training set defined by the scorer.
Generative Image Translation for Data Augmentation in Colorectal Histopathology Images
We present an image translation approach to generate augmented data for mitigating data imbalances in a dataset of histopathology images of colorectal polyps, adenomatous tumors that can lead to colorectal cancer if left untreated.
EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks
We present EDA: easy data augmentation techniques for boosting performance on text classification tasks.
Attention-Based Deep Neural Networks for Detection of Cancerous and Precancerous Esophagus Tissue on Histopathological Slides
Deep learning-based methods, such as the sliding window approach for cropped-image classification and heuristic aggregation for whole-slide inference, for analyzing histological patterns in high-resolution microscopy images have shown promising results.
Ranked #1 on
Medical Object Detection
on Barrett’s Esophagus
