arxiv_id stringlengths 10 12 | abstract stringlengths 459 2.2k |
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2408.16673 | Large language models rely on Supervised Fine-Tuning (SFT) to specialize in downstream tasks. Cross Entropy (CE) loss is the de facto choice in SFT, but it often leads to overfitting and limited output diversity due to its aggressive updates to the data distribution. This paper aim to address these issues by introduci... |
2408.16293 | Language models have demonstrated remarkable performance in solving reasoning tasks; however, even the strongest models still occasionally make reasoning mistakes. Recently, there has been active research aimed at improving reasoning accuracy, particularly by using pretrained language models to "self-correct" their mi... |
2312.07104 | Large language models (LLMs) are increasingly used for complex tasks requiring multiple chained generation calls, advanced prompting techniques, control flow, and interaction with external environments. However, efficient systems for programming and executing these applications are lacking.
To bridge this gap, we intr... |
2408.13359 | Finding the optimal learning rate for language model pretraining is a challenging task. This is not only because there is a complicated correlation between learning rate, batch size, number of training tokens, model size, and other hyperparameters but also because it is prohibitively expensive to perform a hyperparame... |
2408.12857 | Recently, a wide range of memory-efficient LLM training algorithms have gained substantial popularity. These methods leverage the low-rank structure of gradients to project optimizer states into a subspace using projection matrix found by singular value decomposition (SVD). However, convergence of these algorithms is ... |
2405.12981 | Key-value (KV) caching plays an essential role in accelerating decoding for transformer-based autoregressive large language models (LLMs) . However, the amount of memory required to store the KV cache can become prohibitive at long sequence lengths and large batch sizes. Since the invention of the transformer, two of ... |
2105.11921 | Professional summaries are written with document-level information, such as the theme of the document, in mind. This is in contrast with most seq2seq decoders which simultaneously learn to focus on salient content, while deciding what to generate, at each decoding step. With the motivation to narrow this gap, we introd... |
2407.01082 | Large Language Models (LLMs) generate longform text by successively sampling the next token based on the probability distribution of the token vocabulary at each decoding step. Current popular truncation sampling methods such as top-sampling, also known as nucleus sampling, often struggle to balance coherence and crea... |
2406.01436 | Knowledge editing is a rising technique for efficiently updating factual knowledge in Large Language Models (LLMs) with minimal alteration of parameters. However, recent studies have identified concerning side effects, such as knowledge distortion and the deterioration of general abilities, that have emerged after edi... |
2401.17585 | Current approaches of knowledge editing struggle to effectively propagate updates to interconnected facts.
In this work, we delve into the barriers that hinder the appropriate propagation of updated knowledge within these models for accurate reasoning.
To support our analysis, we introduce a novel reasoning-based bench... |
2402.11078 | Fine-tuning is dismissed as not effective for model editing
due to its poor performance compared to more specialized methods.
However, fine-tuning is simple, agnostic to the architectural details of the model being edited,
and able to leverage ongoing advances in standard training methods (e.g., PEFT) ,
making it an ap... |
1704.04368 | Neural sequence-to-sequence models have provided a viable new approach
forabstractivetext summarization (meaning they are not restricted to simply selecting and rearranging passages from the original text) .
However, these models have two shortcomings: they are liable to reproduce factual details inaccurately, and they... |
2407.18940 | Literature search questions, such as “where can I find research on the evaluation of consistency in generated summaries?” pose significant challenges for modern search engines and retrieval systems. These questions often require a deep understanding of research concepts and the ability to reason over entire articles. I... |
2403.03853 | As Large Language Models (LLMs) continue to advance in performance, their size has escalated significantly, with current LLMs containing billions or even trillions of parameters. However, in this study, we discovered that many layers of LLMs exhibit high similarity, and some layers play a negligible role in network fu... |
2406.16797 | Existing methods for adapting large language models (LLMs) to new tasks are not suited to multi-task adaptation because they modify all the model weights–causing destructive interference between tasks. The resulting effects, such as catastrophic forgetting of earlier tasks, make it challenging to obtain good performan... |
2407.21417 | Modern language models (LMs) need to follow human instructions while being faithful; yet, they often fail to achieve both.
Here, we provide concrete evidence of a trade-off between instruction following (i.e., follow open-ended instructions) and faithfulness (i.e., ground responses in given context) when training LM... |
2408.08274 | The Mixture of Experts (MoE) framework has become a popular architecture for large language models due to its superior performance over dense models. However, training MoEs from scratch in a large-scale regime is prohibitively expensive. Existing methods mitigate this by pre-training multiple dense expert models inde... |
2408.07852 | While many capabilities of *language models* (LMs) improve with increased training budget, the influence of scale on hallucinations is not yet fully understood. Hallucinations come in many forms, and there is no universally accepted definition. We thus focus on studying only those hallucinations where a correct answer... |
2202.05262v5 | We analyze the storage and recall of factual associations in autoregressive transformer language models, finding evidence that these associations correspond to localized, directly-editable computations. We first develop a causal intervention for identifying neuronactivationsthat are decisive in a model’s factual predic... |
2307.12950v3 | We propose Reinforcement Learning from Contrast Distillation (RLCD) , a method for aligning language models to follow natural language principles without using human feedback.
RLCD trains a preference model using simulated preference pairs that contain both a high-quality and low-quality example, generated using contra... |
2402.17834v1 | We introduce StableLM 2 1.6B, the first in a new generation of our language model series.
In this technical report, we present in detail the data and training procedure leading to the base and instruction-tuned versions of StableLM 2 1.6B.
The weights for both models are available via Hugging Face for anyone to downloa... |
2307.07924v5 | Software engineering is a domain characterized by intricate decision-making processes, often relying on nuanced intuition and consultation. Recent advancements in deep learning have started to revolutionize software engineering practices through elaborate designs implemented at various stages of software development. I... |
2408.07089 | Recent advancements in Chain-of-Thoughts (CoT) and Program-ofThoughts (PoT) methods have greatly enhanced language models' mathematical reasoning capabilities, facilitating their integration into instruction tuning datasets with LLMs. However, existing methods for large-scale dataset creation require substantial seed ... |
2404.08819v2 | State-space models (SSMs) have emerged as a potential alternative architecture for building large language models (LLMs) compared to the previously ubiquitous transformer architecture. One theoretical weakness of transformers is that they cannot express certain kinds of sequential computation and state tracking, whic... |
2404.18424v2 | The current use of large language models (LLMs) for zero-shot document ranking follows one of two ways: 1) prompt-based re-ranking methods, which require no further training but are feasible for only re-ranking a handful of candidate documents due to the associated computational costs; and 2) unsupervised contrastiv... |
2404.19553v1 | We extend the context length of Llama-3-8B-Instruct from 8K to 80K via QLoRA fine-tuning111The model is noted as Llama-3-8B-Instruct-80K-QLoRA given its max context length during fine-tuning. However, users could apply the model for even longer contexts via extrapolation.. The entire training cycle is super efficient, ... |
2402.04553v1 | We present a novel approach to accelerate stochastic gradient descent (SGD) by utilizing curvature information obtained from Hessian-vector products or finite differences of parameters and gradients, similar to the BFGS algorithm. Our approach involves two preconditioners: a matrix-free preconditioner and a low-rank a... |
2405.07987 | We argue that representations in AI models, particularly deep networks, are converging. First, we survey many examples of convergence in the literature: over time and across multiple domains, the ways by which different neural networks represent data are becoming more aligned.
Next, we demonstrate convergence across da... |
2408.03943 | What do we want from machine intelligence? We envision machines that are not just tools for thought, but *partners* in thought: reasonable, insightful, knowledgeable, reliable, and trustworthy systems that think *with* us. Current artificial intelligence (AI) systems satisfy some of these criteria, some of the time. I... |
2406.15334 | The recent success of interleaved Large Multimodal Models (LMMs) in few-shot learning suggests that in-context learning (ICL) with many examples can be promising for learning new tasks. However, thismany-shotmultimodal ICL setting has one crucial problem: it is fundamentally limited by the model’s context length set ... |
2402.11782 | Retrieval-augmented language models are being increasingly tasked with subjective, contentious, and conflicting queries such as “is aspartame linked to cancer”.
To resolve these ambiguous queries, one must search through a large range of websites and considerwhich, if any, of this evidence do I find convincing?In this ... |
2402.14207 | We study how to apply large language models to write grounded and organized long-form articles from scratch, with comparable breadth and depth to Wikipedia pages. This underexplored problem poses new challenges at thepre-writingstage, including how to research the topic and prepare an outline prior to writing.
We propo... |
2408.04682 | Recent large language models (LLMs) advancements sparked a growing research interest in tool assisted LLMs solving real-world challenges, which calls for comprehensive evaluation of tool-use capabilities. While previous works focused on either evaluating over stateless web services (RESTful API), based on a single tur... |
2105.09938 | While programming is one of the most broadly applicable skills in modern society, it is unclear how well state-of-the-art machine learning models can write code. Despite its importance, there has been surprisingly little work on evaluating code generation, and it can be difficult to assess code generation performance i... |
2310.17813 | The push to train ever larger neural networks has motivated the study of initialization and training at large network width.
A key challenge is to scale training so that a network’s internal representations evolve nontrivially at all widths, a process known asfeature learning.
Here, we show that feature learning is ach... |
2408.04614 | We propose a new method, instruction backand-forth translation, to construct high-quality synthetic data grounded in world knowledge for aligning large language models (LLMs). Given documents from a web corpus, we generate and curate synthetic instructions using the backtranslation approach proposed by Li et al. (20... |
2407.07726v1 | PaliGemma is an open Vision-Language Model (VLM) that is based on the SigLIP-So400m vision encoder and the Gemma-2B language model.
It is trained to be a versatile and broadly knowledgeable base model that is effective to transfer. It achieves strong performance on a wide variety of open-world tasks.
We evaluate PaliG... |
2407.21118 | KV-Cache compression methods generally sample a KV-Cache of effectual tokens or quantize it into lower bits.
However, these methods cannot exploit the redundancy of the hidden dimension of KV tensors.
This paper investigates a unique hidden dimension approach calledPalu, a novel KV-Cache compression framework that util... |
2406.04604 | When using language models (LMs) to solve complex problems, humans might struggle to understand the LM-generated solutions and repair the flawed ones.
To assist humans in repairing them, we propose to automatically decompose complex solutions into multiple simpler pieces that correspond to specific subtasks. We introd... |
2405.16528 | Training of large neural networks requires significant computational resources. Despite advances using low-rank adapters and quantization, pretraining of models such as LLMs on consumer hardware has not been possible without model sharding, offloading during training, or per-layer gradient updates. To address these lim... |
2408.02442 | Structured generation, the process of producing content in standardized formats like JSON and XML, is widely utilized in real-world applications to extract key output information from large language models (LLMs). This study investigates whether such constraints on generation space impact LLMs' abilities, including re... |
2407.09941 | A wide array of sequence models are built on a framework modeled after Transformers, comprising alternating sequence mixer and channel mixer layers. This paper studies a unifying *matrix mixer* view of sequence mixers that can be conceptualized as a linear map on the input sequence. This framework encompasses a broad ... |
2004.11714 | Text generation is ubiquitous in many NLP tasks, from summarization, to dialogue and machine translation.
The dominant parametric approach is based on locally normalized models which predict one word at a time. While these
work remarkably well, they are plagued by exposure bias due to the greedy nature of the generatio... |
2407.21075 | We present foundation language models developed to power Apple Intelligence features, including a3 billion parameter model designed to run efficiently on devices and a large server-based language model designed for Private Cloud Compute.
These models are designed to perform a wide range of tasks efficiently, accurately... |
2408.02666 | Model-based evaluation is at the heart of successful model development - as a reward model for training, and as a replacement for human evaluation. To train such evaluators, the standard approach is to collect a large amount of human preference judgments over model responses, which is costly and the data becomes stale... |
2407.20224 | Knowledge editing techniques have been increasingly adopted to efficiently correct the false or outdated knowledge in Large Language Models (LLMs) , due to the high cost of retraining from scratch. Meanwhile, one critical but under-explored question is:can knowledge editing be used to inject harm into LLMs?In this pape... |
2407.00121 | Large language models (LLMs) have recently shown tremendous promise in serving as the backbone to agentic systems, as demonstrated by their performance in multi-faceted, challenging benchmarks like SWE-Bench and Agent-Bench.
However, to realize the true potential of LLMs as autonomous agents, they must learn to identi... |
2407.20243 | Embeddings from Large Language Models (LLMs) have emerged as critical components in various applications, particularly for information retrieval.
While high-dimensional embeddings generally demonstrate superior performance as they contain more salient information, their practical application is frequently hindered by ... |
2310.10505 | Alignment is crucial for training large language models. The predominant strategy is Reinforcement Learning from Human Feedback (RLHF) , with Proximal Policy Optimization (PPO) as the de-facto algorithm. Yet, PPO is known to struggle with computational inefficiency, a challenge that this paper aims to address. We iden... |
2406.11939v1 | The rapid evolution of language models has necessitated the development of more challenging benchmarks. Current static benchmarks often struggle to consistently distinguish between the capabilities of different models and fail to align with real-world user preferences. On the other hand, live crowd-sourced platforms li... |
2407.21787 | Scaling the amount of compute used to train language models has dramatically improved their capabilities. However, when it comes to inference, we often limit the amount of compute to only one attempt per problem. Here, we explore inference compute as another axis for scaling by increasing the number of generated sample... |
2407.21770 | We introduce MoMa, a novel modality-aware mixture-of-experts (MoE) architecture designed for pre-training mixed-modal, early-fusion language models. MoMa processes images and text in arbitrary sequences by dividing expert modules into modality-specific groups. These groups exclusively process designated tokens while e... |
2010.13369 | Recently, Transformer-based language models have demonstrated remarkable performance across many NLP domains.
However, the unsupervised pre-training step of these models suffers from unbearable overall computational expenses.
Current methods for accelerating the pre-training either rely on massive parallelism with adva... |
2304.07193 | The recent breakthroughs in natural language processing for model pretraining on large quantities of data have opened the way for similar foundation models in computer vision.
These models could greatly simplify the use of images in any system by producing general-purpose visual features, i.e., features that work acros... |
2407.21018 | Large Language Models (LLMs) have revolutionized the field of natural language processing, achieving unprecedented performance across a variety of applications by leveraging increased model sizes and sequence lengths.
However, the associated rise in computational and memory costs poses significant challenges, particul... |
2310.11451 | Large Language Models (LLMs) inherently encode a wealth of knowledge within their parameters through pre-training on extensive corpora. While prior research has delved into operations on these parameters to manipulate the underlying implicit knowledge—encompassing detection, editing, and merging—there remains an ambig... |
2402.12219 | The quality of finetuning data is crucial for aligning large language models (LLMs) with human values.
Current methods to improve data quality are either labor-intensive or prone to factual errors caused by LLM hallucinations.
This paper explores elevating the quality of existing instruction data to better align with ... |
2207.02598 | Machine learning (ML) models are typically optimized for their accuracy on a given dataset.
However, this predictive criterion rarely captures all desirable properties of a model, in particular how well it matches a domain expert’sunderstandingof a task.
Underspecificationrefers to the existence of multiple models tha... |
2011.03395 | ML models often exhibit unexpectedly poor behavior when they are deployed in real-world domains. We identify underspecification as a key reason for these failures. An ML pipeline is underspecified when it can return many predictors with equivalently strong held-out performance in the training domain. Underspecification... |
2405.14838 | When leveraging language models for reasoning tasks, generating explicit chain-of-thought (CoT) steps often proves essential for achieving high accuracy in final outputs. In this paper, we investigate if models can be taught to internalize these CoT steps. To this end, we propose a simple yet effective method for inte... |
1901.09335 | Large-batch SGD is important for scaling training of deep neural networks.
However, without fine-tuning hyperparameter schedules, the generalization of the model may be hampered.
We propose to use batch augmentation: replicating instances of samples within the same batch with different data augmentations. Batch augment... |
1902.05509 | MultiGrain is a network architecture producing compact vector representations that are suited both for image classification and particular object retrieval.
It builds on a standard classification trunk.
The top of the network produces an embedding containing coarse and fine-grained information, so that images can be re... |
2406.15972 | Continual learning aims to allow models to learn new tasks without forgetting what has been learned before. This work introduces Elastic Variational Continual Learning with Weight Consolidation (EVCL) , a novel hybrid model that integrates the variational posterior approximation mechanism of Variational Continual Learn... |
2403.14606 | Artificial intelligence has recently experienced remarkable advances, fueled by
large models, vast datasets, accelerated hardware, and, last but not least,
the transformative power of differentiable programming. This new programming
paradigm enables end-to-end differentiation of complex computer programs
(including tho... |
2206.07137 | Training on web-scale data can take months. But most computation and time is wasted onredundantand noisy points that are already learnt or not learnable. To accelerate training, we introduceReducible Holdout Loss Selection(RHO-LOSS) , a simple but principled technique which selects approximately those points for traini... |
2404.01413 | The proliferation of generative models, combined with pretraining on web-scale data, raises a timely question: what happens when these models are trained on their own generated outputs? Recent investigations into model-data feedback loops proposed that such loops would lead to a phenomenon termedmodel collapse, under w... |
2407.17465 | The Maximal Update Parametrization (µP) aims to make the optimal hyperparameters (HPs) of a model independent of its size, allowing them to be swept using a cheap proxy model rather than the full-size target model.
We present a new scheme, u-µP, which improves upon µP by combining it with Unit Scaling, a method for d... |
2202.12837 | Large language models (LMs) are able to in-context learn—perform a new task via inference alone by conditioning on a few input-label pairs (demonstrations) and making predictions for new inputs.
However, there has been little understanding ofhowthe model learns andwhichaspects of the demonstrations contribute to end ... |
2402.18819 | In-context learning (ICL) exhibits dual operating modes:task learning,i.e. acquiring a new skill from in-context samples, andtask retrieval,i.e., locating and activating a relevant pretrained skill.
Recent theoretical work investigates various mathematical models to analyze ICL, but existing models explain only one op... |
2407.14414 | Language models can be used to solve long-horizon planning problems in two distinct modes. In afast‘System-’ mode, models directly generate plans without any explicit search or backtracking, and in aslow‘System-’ mode, they plan step-by-step by explicitly searching over possible actions. While System-planning is typica... |
2406.11794 | We introduce DataComp for Language Models (DCLM) , a testbed for controlled dataset experiments with the goal of improving language models.
As part ofDCLM, we provide a standardized corpus of 240T tokens extracted from Common Crawl, effective pretraining recipes based on the OpenLM framework, and a broad suite of 53 do... |
2403.13787 | Reward models (RMs) are at the crux of successful RLHF to align pretrained models to human preferences, yet there has been relatively little study that focuses on evaluation of those reward models.
Evaluating reward models presents an opportunity to understand the opaque technologies used for alignment of language mod... |
1911.00172v2 | We introduceNN-LMs, which extend a pre-trained neural language model (LM) by linearly interpolating it with a-nearest neighbors (NN) model.
The nearest neighbors are computed according to distance in the pre-trained LM embedding space, and can be drawn from any text collection, including the original LM training data... |
2407.10969 | We introduce,Q-Sparse, a simple yet effective approach to training sparsely-activated large language models (LLMs) .Q-Sparseenablesfull sparsity of activationsin LLMs which can bring significant efficiency gains in inference. This is achieved by applying top-sparsification to the activations and the straight-through-es... |
2407.12077 | We introduce GoldFinch, a hybrid Linear Attention/Transformer sequence model that uses a new technique to efficiently generate a highly compressed and reusable KV-Cache in linear time and space with respect to sequence length. GoldFinch stacks our new GOLD transformer on top of an enhanced version of the Finch (RWKV-6)... |
2407.12267 | We present a new approach for generating 3D house wireframes with semantic enrichment using an autoregressive model. Unlike conventional generative models that independently process vertices, edges, and faces, our approach employs a unified wire-based representation for improved coherence in learning 3D wireframe struc... |
2406.19223 | Tokenizers are crucial for encoding information in Large Language Models, but their development has recently stagnated, and they contain inherent weaknesses. Major limitations include computational overhead, ineffective vocabulary use, and unnecessarily large embedding and head layers. Additionally, their performance i... |
2402.03496 | Adaptive gradient optimizers like Adam(W) are the default training algorithms for many deep learning architectures, such as transformers.
Their diagonal preconditioner is based on the gradient outer product which is incorporated into the parameter update via a square root.
While these methods are often motivated as ap... |
2407.01449 | Documents are visually rich structures that convey information through text, as well as tables, figures, page layouts, or fonts. While modern document retrieval systems exhibit strong performance on query-to-text matching, they struggle to exploit visual cues efficiently, hindering their performance on practical docume... |
2407.11966 | Good weight initialization serves as an effective measure to reduce the training cost of a deep neural network (DNN) model. The choice of how to initialize parameters is challenging and may require manual tuning, which can be time-consuming and prone to human error. To overcome such limitations, this work takes a nove... |
2401.16380 | Large language models are trained on massive scrapes of the web, which are often unstructured, noisy, and poorly phrased.
Current scaling laws show that learning from such data requires an abundance of both compute and data, which grows with the size of the model being trained.
This is infeasible both because of the la... |
2406.18518 | The advancement of function-calling agent models requires diverse, reliable, and high-quality datasets. This paper presents APIGen, an automated data generation pipeline designed to synthesize verifiable high-quality datasets for function-calling applications. We leverage APIGen and collect 3,673 executable APIs acros... |
2406.00770 | Fine-tuning large pre-trained language models with Evol-Instruct has achieved encouraging results across a wide range of tasks. However, designing effective evolving methods for instruction evolution requires substantial human expertise. This paper proposesAuto Evol-Instruct, an end-to-end framework that evolves instru... |
2407.09450 | Large language models (LLMs) have shown remarkable capabilities, but still struggle with processing extensive contexts, limiting their ability to maintain coherence and accuracy over long sequences. In contrast, the human brain excels at organising and retrieving episodic experiences across vast temporal scales, spann... |
2407.07565 | In this paper we consider contamination by code generation test sets, in particular in their use in modern large language models.
We discuss three possible sources of such contamination and show findings supporting each of them: (i) direct data leakage, (ii) indirect data leakage through the use of synthetic data and... |
2406.15877 | Automated software engineering has been greatly empowered by the recent advances in Large Language Models (LLMs) for programming. While current benchmarks have shown that LLMs can perform various software engineering tasks like human developers, the majority of their evaluations are limited to short and self-contained... |
2407.09025 | Spreadsheets are characterized by their extensive two-dimensional grids, flexible layouts, and varied formatting options, which pose significant challenges for large language models (LLMs) . In response, we introduceSpreadsheetLLM, pioneering an efficient encoding method designed to unleash and optimize LLMs’ powerful ... |
2406.06608 | Generative Artificial Intelligence (GenAI) systems are being increasingly deployed across all parts of industry and research settings. Developers and end users interact with these systems through the use of prompting or prompt engineering. While prompting is a widespread and highly researched concept, there exists con... |
2012.15723 | The recent GPT-3 modelachieves remarkable few-shot performance solely by leveraging a natural-language prompt and a few task demonstrations as input context.
Inspired by their findings, we study few-shot learning in a more practical scenario, where we use smaller language models for which fine-tuning is computationally... |
2310.16164v1 | Large Language Models (LLMs) are being increasingly employed in data science for tasks like data preprocessing and analytics. However, data scientists encounter substantial obstacles when conversing with LLM-powered chatbots and acting on their suggestions and answers. We conducted a mixed-methods study, including con... |
2310.10134 | Language agents have shown some ability to interact with an external environment, e.g., a virtual
world such as ScienceWorld, to perform complex tasks, e.g., growing a plant, without the startup
costs of reinforcement learning. However, despite their zero-shot capabilities, these agents
to date do not continually impro... |
2407.05872 | Robust and effective scaling of models from small to large width typically requires the precise adjustment of many algorithmic and architectural details, such as parameterization and optimizer choices. In this work, we propose a new perspective on parameterization by investigating a key assumption in prior work about ... |
2405.14366 | A critical approach for efficiently deploying computationally demanding large language models (LLMs) is Key-Value (KV) caching. The KV cache stores key-value states of previously generated tokens, significantly reducing the need for repetitive computations and thereby lowering latency in autoregressive generation.
Ho... |
2403.10616 | Progress in machine learning (ML) has been fueled by scaling neural network models. This scaling has been enabled by ever more heroic feats of engineering, necessary for accommodating ML approaches that require high bandwidth communication between devices working in parallel.
In this work, we propose a co-designed mod... |
2406.05184v2 | Generative text-to-image models enable us to synthesize unlimited amounts of images in a controllable manner, spurring many recent efforts to train vision models with synthetic data.
However, every synthetic image ultimately originates from the upstream data used to train the generator. What additional value does the i... |
2306.05426 | In many domains, autoregressive models can attain high likelihood on the task of predicting the next observation.
However, this maximum-likelihood (MLE) objective does not necessarily match a downstream use-case of autoregressively generating high-quality sequences.
The MLE objective weights sequences proportionally t... |
2403.09347 | Effective attention modules have played a crucial role in the success of Transformer-based large language models
(LLMs) , but the quadratic time and memory complexities of these attention modules also pose a challenge when processing long sequences.
One potential solution for the long sequence problem is to utilize dis... |
2309.05516 | Large Language Models (LLMs) have proven their exceptional capabilities in performing language-related tasks. However, their deployment poses significant challenges due to their considerable memory and storage requirements. In response to this issue, weight-only quantization, particularly 3 and 4-bit weight-only quant... |
2401.10491 | While training large language models (LLMs) from scratch can generate models with distinct functionalities and strengths, it comes at significant costs and may result in redundant capabilities. Alternatively, a cost-effective and compelling approach is to merge existing pre-trained LLMs into a more potent model. Howev... |
2305.14325 | Large language models (LLMs) have demonstrated remarkable capabilities in language generation, understanding, and few-shot learning in recent years. An extensive body of work has explored how their performance may be further improved through the tools of prompting, ranging from verification, self-consistency, or inter... |
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