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Computation and Language

TURINGBENCH: A Benchmark Environment for Turing Test in the Age of Neural Text Generation

Recent progress in generative language models has enabled machines to generate astonishingly realistic texts. While there are many legitimate applications of such models, there is also a rising need t

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Computation and Language

Fine-tuning GPTs for Natural Language Understanding

GPT Understands, Too

Natural language understanding (nlu) is one of the most challenging tasks in machine learning.

We show that generalized partial differential equations (gpts) with traditional fine-tuning fail to achieve strong results on natural language understanding (nlu).

Sound

Audio and Speech Processing

Computation and Language

Adapting GPT, GPT-2 and BERT Language Models for Speech Recognition

Language models (LMs) pre-trained on massive amounts of text, in particular bidirectional encoder representations from Transformers (BERT), generative pre-training (GPT), and GPT-2, have become a key

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Machine Learning

Computation and Language

Using generative pre-trained transformers for natural language design concept generation

Generative Pre-Trained Transformer for Design Concept Generation: An Exploration

This paper explores the uses of generative pre-trained transformers (gpt) for natural language design concept generation.

Our experiments involve the use of gpt-2 and gpt-3 for different creative reasonings in design tasks.

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Computation and Language

Language Models are Few-Shot Learners

Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic in a

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Artificial Intelligence

Computation and Language

FoundationLayerNormalization for Deep BERT/GPT Training

FoundationLayerNorm: Scaling BERT and GPT to 1,000 Layers

This paper proposes a simple yet effective method to stabilize bert and gpt training.

The proposed methodfoundationlayernormalization enables efficient training of deep neural networks and is validated at the 1000-layer scale.

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Machine Learning

Artificial Intelligence

Computation and Language

LoRA: Low-Rank Adaptation of Large Language Models

The dominant paradigm of natural language processing consists of large-scale pre-training on general domain data and adaptation to particular tasks or domains. As we pre-train larger models, conventio

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Computation and Language

Who is GPT-3? An Exploration of Personality, Values and Demographics

Language models such as GPT-3 have caused a furore in the research community. Some studies found that GPT-3 has some creative abilities and makes mistakes that are on par with human behaviour. This pa

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Computation and Language

Detecting Sexist and Racist Text with Large Language Models

Detecting Hate Speech with GPT-3

Large language models such as openai s gpt-3 can generate hateful text that targets marginalized groups.

We use gpt-3 to identify sexist and racisttext passages with zero-, one-, and few-shot learning.

New

Computation and Language

A Comprehensive Capability Analysis of GPT-3 and GPT-3.5 Series Models

GPT series models, such as GPT-3, CodeX, InstructGPT, ChatGPT, and so on, have gained considerable attention due to their exceptional natural language processing capabilities. However, despite the abu

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Information Retrieval

Computation and Language

Few-Shot Performance of GPT-3 on Biomedical Information Extraction Tasks

Thinking about GPT-3 In-Context Learning for Biomedical IE? Think Again

We present the first systematic and comprehensive study to compare the few-shot performance of the pre-trained language model (plm) gpt-3 in-context learning with fine-tuning smaller (i.e.,bert-sized) pre-trained language models on two highly representative biomedical information extraction tasks, named entity recognition and relation extraction.

Our results show that gpt-3 still significantly underperforms compared with simply fine-tuning a smaller plm using the same small training set.

Computer Vision

Computation and Language

VC-GPT: Visual Conditioned GPT for End-to-End Generative Vision-and-Language Pre-training

Vision-and-language pre-training models (VLMs) have achieved tremendous success in the cross-modal area, but most of them require millions of parallel image-caption data for pre-training. Collating su

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Artificial Intelligence

Computation and Language

Investigating African-American Vernacular English in Transformer-Based Text Generation

The growth of social media has encouraged the written use of African American Vernacular English (AAVE), which has traditionally been used only in oral contexts. However, NLP models have historically

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Machine Learning

Artificial Intelligence

Computation and Language

A Cognitive Assessment of GPT-3

Using cognitive psychology to understand GPT-3

We study the decision-making, information search, deliberation, and causal reasoning abilities of a recent large language model, using tools from cognitive psychology.

We find that much of the model s behavior is impressive : it solves vignette-based tasks similarly or better than human subjects, is able to make decent decisions from descriptions, outperforms humans in a multi-armed bandit task, and shows signatures of model-based reinforcement learning.

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Computation and Language

A Comprehensive Evaluation of Generative Pre-trained Transformer Models for Machine Translation

How Good Are GPT Models at Machine Translation? A Comprehensive Evaluation

We present a comprehensive evaluation of three generative pre-trained transformer (gpt) models for machine translation, covering various aspects such as quality of different gpt models in comparison with state-of-the-art research and commercial systems, effect of prompting strategies, robustness towards domain shifts and document-level translation.

We experiment with eighteen different translation directions involving high and low resource languages, as well as non-english-centric translations, and evaluate the performance of three gpt models : chatgpt, gpt3.5(text-davinci-003), and text-davisci-002.

Computation and Language

Neurons and Cognition

Large language models can segment narrative events similarly to humans

Humans perceive discrete events such as "restaurant visits" and "train rides" in their continuous experience. One important prerequisite for studying human event perception is the ability of researche

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Artificial Intelligence

Computation and Language

GPT-3: A Survey

a survey on GPT-3

This paper provides an introductory survey to the generative principle test-based inference (gpt-3).

We survey both academic and commercial efforts applying this technology in diverse domains such as developing conversational ai chatbots, software development, creative work, domain knowledge, and business productivity.

Quantum Physics

Accessible fragments of generalized probabilistic theories, cone equivalence, and applications to witnessing nonclassicality

The formalism of generalized probabilistic theories (GPTs) was originally developed as a way to characterize the landscape of conceivable physical theories. Thus, the GPT describing a given physical t

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