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检索条件"任意字段=Conference on empirical methods in natural language processing"

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Abstraction-of-Thought Makes language Models Better Reasoners

Abstraction-of-Thought Makes Language Models Better Reasoner...

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2024 conference on empirical methods in natural language processing, EMNLP 2024

作者： Hong, Ruixin Zhang, Hongming Pan, Xiaoman Yu, Dong Zhang, Changshui China China Department of Automation Tsinghua University Beijing China Tencent AI Lab Seattle United States

ISBN: (纸本)9798891761681

Abstract reasoning, the ability to reason from the abstract essence of a problem, serves as a key to generalization in human reasoning. However, eliciting language models to perform reasoning with abstraction remains unexplored. This paper seeks to bridge this gap by introducing a novel structured reasoning format called Abstraction-of-Thought (AoT). The uniqueness of AoT lies in its explicit requirement for varying levels of abstraction within the reasoning process. This approach could elicit language models to first contemplate on the abstract level before incorporating concrete details, which is overlooked by the prevailing step-by-step Chain-of-Thought (CoT) method. To align models with the AoT format, we present AOT COLLECTION, a generic finetuning dataset consisting of 348k high-quality samples with AoT reasoning processes, collected via an automated and scalable pipeline. We finetune a wide range of language models with AOT COLLECTION and conduct extensive evaluations on 23 unseen tasks from the challenging benchmark Big-Bench Hard. Experimental results indicate that models aligned to AoT reasoning format substantially outperform those aligned to CoT in many reasoning tasks. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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GRASS: Compute Efficient Low-Memory LLM Training with Structured Sparse Gradients

GRASS: Compute Efficient Low-Memory LLM Training with Struct...

引用

2024 conference on empirical methods in natural language processing, EMNLP 2024

作者： Muhamed, Aashiq Li, Oscar Woodruff, David Diab, Mona Smith, Virginia Language Technologies Institute Carnegie Mellon University United States Machine Learning Department Carnegie Mellon University United States Department of Computer Science Carnegie Mellon University United States

ISBN: (纸本)9798891761643

Large language model (LLM) training and finetuning are often bottlenecked by limited GPU memory. While existing projection-based optimization methods address this by projecting gradients into a lower-dimensional subspace to reduce optimizer state memory, they typically rely on dense projection matrices, which can introduce computational and memory overheads. In this work, we propose GRASS (GRAdient Stuctured Sparsification), a novel approach that leverages sparse projections to transform gradients into structured sparse updates. This design not only significantly reduces memory usage for optimizer states but also minimizes gradient memory footprint, computation, and communication costs, leading to substantial throughput improvements. Extensive experiments on pretraining and finetuning tasks demonstrate that GRASS achieves competitive performance to full-rank training and existing projection-based methods. Notably, GRASS enables half-precision pretraining of a 13B parameter LLaMA model on a single 40GB A100 GPU-a feat infeasible for previous methods-and yields up to a 2× throughput improvement on an 8-GPU system. © 2024 Association for Computational Linguistics.

关键词： Graphics processing unit

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Self-Training for Sample-Efficient Active Learning for Text Classification with Pre-Trained language Models

Self-Training for Sample-Efficient Active Learning for Text ...

引用

2024 conference on empirical methods in natural language processing, EMNLP 2024

作者： Schröder, Christopher Heyer, Gerhard Leipzig Dresden Germany TUD Dresden University of Technology Germany Leipzig University Germany

ISBN: (纸本)9798891761643

Active learning is an iterative labeling process that is used to obtain a small labeled subset, despite the absence of labeled data, thereby enabling to train a model for supervised tasks such as text classification. While active learning has made considerable progress in recent years due to improvements provided by pretrained language models, there is untapped potential in the often neglected unlabeled portion of the data, although it is available in considerably larger quantities than the usually small set of labeled data. In this work, we investigate how self-training, a semi-supervised approach that uses a model to obtain pseudo-labels for unlabeled data, can be used to improve the efficiency of active learning for text classification. Building on a comprehensive reproduction of four previous self-training approaches, some of which are evaluated for the first time in the context of active learning or natural language processing, we introduce HAST, a new and effective self-training strategy, which is evaluated on four text classification benchmarks. Our results show that it outperforms the reproduced self-training approaches and reaches classification results comparable to previous experiments for three out of four datasets, using as little as 25% of the data. The code is publicly available at https://***/chschroeder/self-training-for-sample-efficient-active-learning. © 2024 Association for Computational Linguistics.

关键词： Self-supervised learning

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Structure Guided Prompt: Instructing Large language Model in Multi-Step Reasoning by Exploring Graph Structure of the Text

Structure Guided Prompt: Instructing Large Language Model in...

引用

2024 conference on empirical methods in natural language processing, EMNLP 2024

作者： Cheng, Kewei Ahmed, Nesreen K. Willke, Theodore L. Sun, Yizhou Amazon United States Cisco Outshift Hungary Intel Labs United States UCLA United States

ISBN: (纸本)9798891761643

Although Large language Models (LLMs) excel at addressing straightforward reasoning tasks, they frequently struggle with difficulties when confronted by more complex multi-step reasoning due to a range of factors. Firstly, natural language often encompasses complex relationships among entities, making it challenging to maintain a clear reasoning chain over longer spans. Secondly, the abundance of linguistic diversity means that the same entities and relationships can be expressed using different terminologies and structures, complicating the task of identifying and establishing connections between multiple pieces of information. Graphs provide an effective solution to represent data rich in relational information and capture long-term dependencies among entities. To harness the potential of graphs, our paper introduces Structure Guided Prompt, an innovative three-stage task-agnostic prompting framework designed to improve the multi-step reasoning capabilities of LLMs in a zero-shot setting. This framework explicitly converts unstructured text into a graph via LLMs and instructs them to navigate this graph using task-specific strategies to formulate responses. By effectively organizing information and guiding navigation, it enables LLMs to provide more accurate and context-aware responses. Our experiments show that this framework significantly enhances the reasoning capabilities of LLMs, enabling them to excel in a broader spectrum of natural language scenarios. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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LONGEMBED: Extending Embedding Models for Long Context Retrieval

LONGEMBED: Extending Embedding Models for Long Context Retri...

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2024 conference on empirical methods in natural language processing, EMNLP 2024

作者： Zhu, Dawei Wang, Liang Yang, Nan Song, Yifan Wu, Wenhao Wei, Furu Li, Sujian School of Computer Science Peking University China National Key Laboratory for Multimedia Information Processing Peking University China Jiangsu Collaborative Innovation Center for Language Ability Jiangsu Normal University China Microsoft Corporation United States

ISBN: (纸本)9798891761643

Embedding models play a pivotal role in modern NLP applications such as document retrieval. However, existing embedding models are limited to encoding short documents of typically 512 tokens, restrained from application scenarios requiring long inputs. This paper explores context window extension of existing embedding models, pushing their input length to a maximum of 32,768. We begin by evaluating the performance of existing embedding models using our newly constructed LONGEMBED benchmark, which includes two synthetic and four real-world tasks, featuring documents of varying lengths and dispersed target information. The benchmarking results highlight huge opportunities for enhancement in current models. Via comprehensive experiments, we demonstrate that training-free context window extension strategies can effectively increase the input length of these models by several folds. Moreover, comparison of models using Absolute Position Encoding (APE) and Rotary Position Encoding (RoPE) reveals the superiority of RoPE-based embedding models in context window extension, offering empirical guidance for future models. Our benchmark, code and trained models will be released to advance the research in long context embedding models. © 2024 Association for Computational Linguistics.

关键词： Encoding (symbols)

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FreeAL: Towards Human-Free Active Learning in the Era of Large language Models

FreeAL: Towards Human-Free Active Learning in the Era of Lar...

引用

conference on empirical methods in natural language processing (EMNLP)

作者： Xiao, Ruixuan Dong, Yiwen Zhao, Junbo Wu, Runze Lin, Minmin Chen, Gang Wang, Haobo Zhejiang Univ Hangzhou Peoples R China NetEase Fuxi AI Lab Hangzhou Peoples R China

ISBN: (纸本)9798891760608

Collecting high-quality labeled data for model training is notoriously time-consuming and labor-intensive for various NLP tasks. While copious solutions, such as active learning for small language models (SLMs) and prevalent in-context learning in the era of large language models (LLMs), have been proposed and alleviate the labeling burden to some extent, their performances are still subject to human intervention. It is still underexplored how to reduce the annotation cost in the LLMs era. To bridge this, we revolutionize traditional active learning and propose an innovative collaborative learning framework FreeAL to interactively distill and filter the task-specific knowledge from LLMs. During collaborative training, an LLM serves as an active annotator inculcating its coarse-grained knowledge, while a downstream SLM is incurred as a student to filter out high-quality in-context samples to feedback LLM for the subsequent label refinery. Extensive experiments on eight benchmark datasets demonstrate that FreeAL largely enhances the zero-shot performances for both SLM and LLM without any human supervision. The code is available at https://***/Justherozen/FreeAL.

关键词： Zero-shot learning

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FOLIO: natural language Reasoning with First-Order Logic

FOLIO: Natural Language Reasoning with First-Order Logic

引用

2024 conference on empirical methods in natural language processing, EMNLP 2024

作者： Han, Simeng Schoelkopf, Hailey Zhao, Yilun Qi, Zhenting Riddell, Martin Zhou, Wenfei Coady, James Peng, David Qiao, Yujie Benson, Luke Sun, Lucy Wardle-Solano, Alex Szabo, Hannah Zubova, Ekaterina Burtell, Matthew Fan, Jonathan Liu, Yixin Wong, Brian Sailor, Malcolm Ni, Ansong Nan, Linyong Kasai, Jungo Yu, Tao Zhang, Rui Fabbri, Alexander R. Kryscinski, Wojciech Yavuz, Semih Liu, Ye Lin, Xi Victoria Joty, Shafiq Zhou, Yingbo Xiong, Caiming Ying, Rex Cohan, Arman Radev, Dragomir Yale University United States Harvard University United States NVIDIA United States Iowa City West High School United States University of Washington United States University of Hong Kong Hong Kong Penn State University United States Meta AI United States Salesforce Research United States

ISBN: (纸本)9798891761643

Large language models (LLMs) have achieved remarkable performance on a variety of natural language understanding tasks. However, existing benchmarks are inadequate in measuring the complex logical reasoning capabilities of a model. We present FOLIO, a human-annotated, logically complex and diverse dataset for reasoning in natural language (NL), equipped with first-order logic (FOL) annotations. FOLIO consists of 1,430 examples (unique conclusions), each paired with one of 487 sets of premises used to deductively reason for the validity of each conclusion. The logical correctness of the premises and conclusions is ensured by their FOL annotations, which are automatically verified by an FOL inference engine. In addition to the main NL reasoning task, NL-FOL pairs in FOLIO constitute a new NL-FOL translation dataset. Our experiments on FOLIO systematically evaluate the FOL reasoning ability of supervised fine-tuning on medium-sized language models. For both NL reasoning and NL-FOL translation, we benchmark multiple state-of-the-art language models. Our results show that a subset of FOLIO presents a challenge for one of the most capable Large language Model (LLM) publicly available, GPT-4. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Distractor Generation in Multiple-Choice Tasks: A Survey of methods, Datasets, and Evaluation

Distractor Generation in Multiple-Choice Tasks: A Survey of ...

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2024 conference on empirical methods in natural language processing, EMNLP 2024

作者： Alhazmi, Elaf Sheng, Quan Z. Zhang, Wei Emma Zaib, Munazza Alhazmi, Ahoud School of Computing Macquarie University Australia School of Computer and Mathematical Sciences The University of Adelaide Australia College of Engineering and Computing in Al-Lith Umm Al-Qura University Saudi Arabia

ISBN: (纸本)9798891761643

The distractor generation task focuses on generating incorrect but plausible options for objective questions such as fill-in-the-blank and multiple-choice questions. This task is widely utilized in educational settings across various domains and subjects. The effectiveness of these questions in assessments relies on the quality of the distractors, as they challenge examinees to select the correct answer from a set of misleading options. The evolution of artificial intelligence (AI) has transitioned the task from traditional methods to the use of neural networks and pre-trained language models. This shift has established new benchmarks and expanded the use of advanced deep learning methods in generating distractors. This survey explores distractor generation tasks, datasets, methods, and current evaluation metrics for English objective questions, covering both text-based and multi-modal domains. It also evaluates existing AI models and benchmarks and discusses potential future research directions. © 2024 Association for Computational Linguistics.

关键词： Contrastive Learning

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SEEKR: Selective Attention-Guided Knowledge Retention for Continual Learning of Large language Models

SEEKR: Selective Attention-Guided Knowledge Retention for Co...

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2024 conference on empirical methods in natural language processing, EMNLP 2024

作者： He, Jinghan Guo, Haiyun Zhu, Kuan Zhao, Zihan Tang, Ming Wang, Jinqiao Foundation Model Research Center Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China Peng Cheng Laboratory China Wuhan AI Research China Chongqing University China

ISBN: (纸本)9798891761643

Continual learning (CL) is crucial for language models to dynamically adapt to the evolving real-world demands. To mitigate the catastrophic forgetting problem in CL, data replay has been proven a simple and effective strategy, and the subsequent data-replay-based distillation can further enhance the performance. However, existing methods fail to fully exploit the knowledge embedded in models from previous tasks, resulting in the need for a relatively large number of replay samples to achieve good results. In this work, we first explore and emphasize the importance of attention weights in knowledge retention, and then propose a SElective attEntion-guided Knowledge Retention method (SEEKR) for data-efficient replay-based continual learning of large language models (LLMs). Specifically, SEEKR performs attention distillation on the selected attention heads for finer-grained knowledge retention, where the proposed forgettability-based and task-sensitivity-based measures are used to identify the most valuable attention heads. Experimental results on two continual learning benchmarks for LLMs demonstrate the superiority of SEEKR over the existing methods on both performance and efficiency. Explicitly, SEEKR achieves comparable or even better performance with only 1/10 of the replayed data used by other methods, and reduces the proportion of replayed data to 1%. The code is available at https://***/jinghan1he/SEEKR. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Improving Referring Ability for Biomedical language Models

Improving Referring Ability for Biomedical Language Models

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2024 conference on empirical methods in natural language processing, EMNLP 2024

作者： Jiang, Junfeng Fei, Cheng Aizawa, Akiko The University of Tokyo Japan Kyoto University Japan National Institute of Informatics Japan

ISBN: (纸本)9798891761681

Existing auto-regressive large language models (LLMs) are primarily trained using documents from general domains. In the biomedical domain, continual pre-training is a prevalent method for domain adaptation to inject professional knowledge into powerful LLMs that have been pre-trained in general domains. Previous studies typically conduct standard pretraining by randomly packing multiple documents into a long pre-training sequence. Recently, some existing works suggest that enhancing the relatedness of documents within the same pre-training sequence may be advantageous. However, these studies primarily focus on general domains, which cannot be readily applied in the biomedical domain where the distinction of fine-grained topics is harder. Is it possible to further improve the pre-training for biomedical language models (LMs) using exactly the same corpus? In this paper, we explore an improved approach to continual pretraining, which is a prevalent method for domain adaptation, by utilizing information from the citation network in this challenging scenario. empirical studies demonstrate that our proposed LinkLM data improves both the intra-sample and inter-sample referring abilities of auto-regressive LMs in the biomedical domain, encouraging more profound consideration of task-specific pre-training sequence design for continual pre-training. © 2024 Association for Computational Linguistics.

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