Summary Challenges and Applications of Large Language Models arxiv.org
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Large Language Models (LLMs) have issues with misaligned behavior, outdated knowledge, and brittle evaluations, but they find applications in chatbots, computational biology, and computer programming, while holistic benchmarking suites like HELM help standardize evaluation methods, and model editing techniques are explored.
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Key Points
- Large language models (LLMs) face challenges with misaligned behavior, outdated knowledge, brittle evaluations, and indistinguishability from human-written text.
- LLMs lack experimental designs and reproducibility.
- Applications of LLMs include chatbots, computational biology, and computer programming.
- Tokenization is a process that breaks words into smaller units called tokens.
- Training smaller models intensively upfront can offset larger inference costs in the future.
- Large language models have different approaches for conditioning on tokens before and after masked ones.
- LLMs possess the capability of task learning and can acquire new input-label mappings.
- LLMs often generate outputs that don't align with human values, and pre-training with human feedback can improve alignment.
Summaries
34 word summary
Large Language Models (LLMs) present challenges with misaligned behavior, outdated knowledge, and brittle evaluations. Applications include chatbots, computational biology, and computer programming. Holistic benchmarking suites like HELM standardize evaluation methods. Model editing techniques and
112 word summary
Large Language Models (LLMs) have become prevalent in machine learning, but they face challenges with misaligned behavior, outdated knowledge, brittle evaluations, and indistinguishability from human-written text. Applications include chatbots, computational biology, and computer programming.
Large language models (LLMs) face challenges related to bias, toxicity detection, prompt injections, and outdated knowledge. Holistic benchmarking suites like HELM standardize evaluation methods. Model editing techniques and retrieval augmentation can address outdated knowledge. Watermarks remain
This summary includes a list of references to various research papers and articles related to large language models and their applications in different fields. These include topics such as context length, few-shot learning, privacy attacks, attention mechanisms, code generation, social biases, evaluation
2103 word summary
Large Language Models (LLMs) have quickly become prevalent in machine learning, but there are still challenges and application areas to explore. This paper aims to establish a systematic set of open problems and successes to help ML researchers understand the current state of the field
Large language models face challenges with misaligned behavior, outdated knowledge, brittle evaluations, and indistinguishability from human-written text. They also lack experimental designs and reproducibility. Applications include chatbots, computational biology, and computer programming.
Challenges in large language models include static evaluations, lack of experimental designs, and reproducibility. LLM design decisions are made before deployment, behavioral challenges occur during deployment, and science challenges hinder academic progress. Creative work, knowledge work, law,
Challenges and Applications of Large Language Models: This review addresses the challenges and applications of large language models (LLMs). The challenges include data contamination, unfathomable datasets, and the presence of near-duplicates. These challenges can lead to inflated performance
Over 1% of tokens emitted by large language models are part of a memorized sequence, including personally identifiable information. The diversity and size of pre-training datasets impact downstream performance. Fine-tuning models on multiple tasks with few examples per task has shown
Tokenization is a process that breaks words into smaller units called tokens. Subword tokenization is commonly used, but it has drawbacks. Byte-level tokenization is an alternative that can be used with subword tokenizers or to define a limited vocabulary.
Training smaller models intensively upfront can offset larger inference costs in the future. Scaling laws for performance prediction differ between upstream and downstream setups. The majority of training costs go towards pre-training, which requires significant compute hours and resources. Performance increases with larger compute
Large language models have different approaches for conditioning on tokens before and after masked ones. Span Corruption replaces contiguous token sequences with a unique masking token. Masked Language Modeling hides tokens by replacing them with a special [MASK] token. The view that mental states
Large Language Models (LLMs) present challenges in training and inference due to their size. Model parallelism and pipeline parallelism are strategies used to distribute the model and data across multiple devices, reducing waiting times and maximizing computation resources. Techniques such as stacking
Large language models have achieved competitive performance with minimal training data. Techniques such as soft prompts, scaling activations, and memory-efficient optimization have been explored. Efficient attention mechanisms can be achieved through hardware modifications or sub-quadratic approximations. Attention sparsity patterns and
Large language models face challenges in computation, routing, decoding strategies, and software efficiency. Efficient attention mechanisms and positional embedding schemes are explored to handle longer context lengths.
Efficient attention mechanisms that can process longer inputs are being developed to address the limited context of large language models (LLMs). These mechanisms include Luna, which uses nested linear attention functions, and alternative attention mechanisms that require less memory and compute resources. Other
Relative Positional Bias and ALiBi are methods that bias attention computations in large language models. While some positional encoding schemes offer better generalization to long sequences, their reliability is unclear. Fine-tuning pre-trained models is insufficient for length generalization,
Lisa has 5 easy peelers and buys 2 nets with 6 easy peelers each, resulting in a total of 17 easy peelers. The cafeteria initially had 37 bananas and then bought 5 bunches, each containing 5
The cafeteria has 37 bananas and bought 5 more bunches with 5 bananas each, bringing the total to 62. Lisa has 5 easy peelers and buys 2 more nets with 6 each, giving her a total of
Large language models (LLMs) possess the capability of task learning, which involves acquiring new input-label mappings. The order of few-shot examples provided to LLMs significantly affects their performance. Various explanations for the input-conditioned learning (ICL)
In large language models, hallucinations can occur when the output cannot be verified or contradicts the source content. Retrieval augmentation, where external knowledge is used to ground the model's input, can help mitigate hallucinations. Various approaches, such as retrieving
Large Language Models (LLMs) often generate outputs that don't align with human values. Pre-training with human feedback (PHF) during the pre-training stage improves alignment. Conditional training is the most effective PHF approach. Increasing diversity in response generation
RLHF can lead to unwanted effects in language models, such as repeating a user's political views and expressing strong political and religious opinions. Self-improvement techniques, such as fine-tuning on self-generated data, have been used to align models with
Research areas related to red teaming and debate aim to evaluate the safety and usefulness of large language models (LLMs) during training. LLMs can improve factuality and reasoning through self-play and short statement evaluations. However, this approach requires multiple
Large language models (LLMs) face challenges related to bias, toxicity detection, prompt injections, and outdated knowledge. Bias in LLMs arises from the inclusion of web-crawled data containing political discourse, hate speech, and other media biases.
Holistic benchmarking suites like HELM standardize evaluation methods and cover a wide range of capabilities. Language models are also benchmarked on tests designed for humans. Model editing techniques and retrieval augmentation can address outdated knowledge. Large language models achieve human-level performance
Low-entropy tokens are difficult to change, so a "soft" version is introduced for high-entropy tokens. Watermarks remain detectable in LLMs, even after being rewritten or mixed into longer hand-written documents. Watermarked LLMs
Compositional tasks are used to test whether language models can go beyond rote memorization. Large models show no improvement in solving composed problems compared to sub-problems. Transformers reduce compositional tasks to shortcut learning and lack robust generalization. LLM
Large language models are categorized by their size and whether they are encoder and decoder models or decoder-only models. Sizes range from 245M to 1.5T, with variations in the number of parameters.
Large language models are categorized by their size and the tasks they are designed for, including encoding-only, decoding-only, encoding and decoding, and multilingual models. Various sizes and language combinations are available.
This summary discusses the challenges and applications of large language models. It highlights various models developed by different organizations and their release dates. The summary also mentions the issue of repeatability in training runs and generations of close-sourced API-served models. The
The scheduling and communication strategies between nodes in large language models can be non-deterministic, which can affect the final result. Reproducibility is compromised due to changes in pre-training datasets and non-deterministic parallelism strategies. Commercial language models
Glaese et al. propose Sparrow, a chatbot based on a large language model (LLM) called Chinchilla. Various applications of LLMs are discussed, including chatbots, genomics, computational biology, computer programming,
Several large language models have been developed for specific applications, such as genomic analysis and code generation. For genomic analysis, models like GenSLMs, Nucleotide Transformers, and HyenaDNA have been trained on gene sequences to predict new variants and genomic
Training phi-1 with filtered datasets and synthetic data achieves near SOTA results with fewer parameters. Long-range dependencies in code repositories can be addressed using retrieval-based frameworks like RepoCoder. Polycoder is a multilingual programming LLM, but Codex
The challenges and applications of large language models (LLMs) are discussed. LLMs are used for story generation, creative tasks, visual creative tasks, knowledge work, and data analysis. The inability of LLMs to keep the entire generated work
GPT-4 uses a modular prompting framework and performs well but underperforms compared to human data analysts. Galactica LLM is trained specifically for scientific knowledge work. GPT-3.5 achieves high accuracy on qualitative sections and shows potential
Large language models (LLMs) have been evaluated for their ability to complete judicial opinions and medical question answering tasks. GPT-4 outperforms GPT-3.5 in medical benchmarks, but issues of erroneous generations and bias remain. L
LLMs have been used for various applications, including improving GPT-3.5's performance on reasoning benchmarks and breaking down mathematical word problems. In the medical field, LLMs have been applied to extract data from medical sources and disambig
GPT-3.5/4 outperforms existing algorithms in causal benchmarks, while ChatGPT performs poorly. LLMs are used to simulate human behavior, analyze behavioral characteristics, and simulate social relationships. LLMs are limited in their
Large language models (LLMs) have been used in various research areas, including planning in simulated worlds and modeling human behavior in social sciences and psychology. LLMs have shown potential in replicating human judgments and behaviors, although larger models tend to perform
Das et al. (2022) developed Qameleon, a multilingual question-answering model trained with only 5 examples. Ahia et al. (2023) studied tokenization costs in commercial language models. Ainslie et
This document contains a list of references to various papers and studies on large language models, including topics such as collaborative inference, fine-tuning, parameter efficiency, memorization, adversarial alignment, and more.
This excerpt includes a list of references to various papers and articles related to large language models.
Transformer-XL, a language model beyond fixed-length context, is discussed along with other relevant models and approaches in the field of computational linguistics.
This document includes various research papers and preprints related to large language models, covering topics such as structured information extraction, analysis of model performance compared to humans, limitations of transformers, reducing hallucination in dialogue systems, mathematical frameworks for transformer circuits, self-sup
Excerpted from the document are various references to papers and preprints related to large language models. These references cover topics such as automated formalization of theorem statements, sparse training with mixture-of-experts, social reasoning in language models, reducing harms through
Efficient evolution of human antibodies from general protein language models. Artificial muses: Generative AI chatbots with human-level creativity. Red teaming with coevolution. A theory of emergent in-context learning as implicit structure induction. Classifier-free diffusion
Large language models have the potential for self-improvement and can be used for creative writing, AI safety, and few-shot learning. They can also be trained as zero-shot planners, for protein structure prediction, and to evaluate and induce personality. Efficient
This excerpt contains a list of references to various papers and articles related to large language models.
GeDi: Generative discriminator guided sequence generation; Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense; Subword regularization improves neural network translation models; Sentencepiece: A simple and language independent subword tokenizer; GitHub
The summary includes a list of various research papers and articles related to large language models and their applications in different fields. These include topics such as context length, few-shot learning, privacy attacks, attention mechanisms, code generation, social biases, evaluation of language
This document contains a collection of research papers on various challenges and applications of large language models. The papers cover topics such as designing effective instruction tuning, measuring the effects of training data, overcoming prompt order sensitivity, maximizing communication efficiency, analyzing leakage of personally identifiable
This summary includes a selection of research papers and projects related to large language models, covering topics such as healthcare, screenplay writing, model editing, text detection, cross-task generalization, genomic sequence modeling, code synthesis, and image generation. It also mentions
The challenges and applications of large language models are discussed in various papers and technical reports. These include issues related to the speed of chat GPT-4, asynchronous pipelines for processing large corpora, the use of fairseq toolkit for sequence modeling, and
This excerpt includes various references to papers, blog posts, and conference proceedings related to large language models, transformer frameworks, scaling language models, and training techniques. It also mentions specific topics such as legal information extraction, arithmetic and symbolic induction, sentiment analysis,
In a document discussing challenges and applications of large language models, various sources and studies are referenced. Topics include model training, non-deterministic inference, automated evaluation methods, object hallucination in image captioning, the false consensus effect, risk psychology,
This text excerpt contains a list of references and citations from various sources related to large language models.
Several sources and papers related to large language models and their applications are referenced in the text excerpt. These sources cover topics such as quantifying the capabilities of language models, grammatical error correction, knowledge-enhanced pre-training, legal aspects of language models,
Several studies and papers on large language models for various applications have been referenced, including models for science, email understanding, dialog applications, data-to-text generation, authorship attribution, math word problem solving, robotics, biomedical text, protein generalization, scene
The text excerpt consists of a list of references to various papers and articles related to large language models and their applications.
This excerpt contains a list of references to various articles and papers related to large language models.
This text excerpt includes citations for various research papers related to large language models and their applications.
This document provides a list of references to various papers and articles related to large language models. It includes studies on model hallucinations, optimization, alignment, benchmarking, prompting, vulnerability, parallelism, fine-tuning, and other topics.