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

共 15353 条记录，以下是1211-1220 订阅

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Guided Knowledge Generation with language Models for Commonsense Reasoning

Guided Knowledge Generation with Language Models for Commons...

引用

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

作者： Wei, Xiao Chen, Haoran Yu, Hang Fei, Hao Liu, Qian Shanghai University China National University of Singapore Singapore University of Auckland New Zealand

ISBN: (纸本)9798891761681

Large language Models (LLMs) have achieved notable success in commonsense reasoning tasks, benefiting from their rich world knowledge acquired through extensive pretraining. While approaches like Chain-of-Thought (CoT) have shown promise in enhancing LLMs' reasoning capabilities, mitigating the influence of inaccurate commonsense knowledge remains a challenge, particularly for small-scale LLMs (e.g., those with less than 10B parameters). In this work, we propose a novel method named Guided Knowledge Generation (GuideKG) to address these issues. It presents three advantages: (i) Employing LLMs to generate knowledge explanations and to automatically assign labels based on the probability of correct answers eliminates the need for costly manual annotation in subsequent training. (ii) Training a new module called the Know-Filter, which evaluates knowledge, with the introduction of a novel loss function to enhance its performance. (iii) Evaluating the effectiveness of knowledge fragments at the sentence level and fusing them allows for precise control over the generation process of LLMs. We evaluate our GuideKG on small-scale LLMs and show that it outperforms all baselines on four widely-used commonsense reasoning benchmarks. Moreover, our experiments reveal that, with proper guidance, small-scale LLMs can exhibit exceptional performance in commonsense reasoning. The code is publicly available. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Unlocking the Future: Exploring Look-Ahead Planning Mechanistic Interpretability in Large language Models

Unlocking the Future: Exploring Look-Ahead Planning Mechanis...

引用

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

作者： Men, Tianyi Cao, Pengfei Jin, Zhuoran Chen, Yubo Liu, Kang Zhao, Jun The Key Laboratory of Cognition and Decision Intelligence for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Shanghai Artificial Intelligence Laboratory China

ISBN: (纸本)9798891761643

Planning, as the core module of agents, is crucial in various fields such as embodied agents, web navigation, and tool using. With the development of large language models (LLMs), some researchers treat large language models as intelligent agents to stimulate and evaluate their planning capabilities. However, the planning mechanism is still unclear. In this work, we focus on exploring the look-ahead planning mechanism in large language models from the perspectives of information flow and internal representations. First, we study how planning is done internally by analyzing the multi-layer perception (MLP) and multi-head self-attention (MHSA) components at the last token. We find that the output of MHSA in the middle layers at the last token can directly decode the decision to some extent. Based on this discovery, we further trace the source of MHSA by information flow, and we reveal that MHSA mainly extracts information from spans of the goal states and recent steps. According to information flow, we continue to study what information is encoded within it. Specifically, we explore whether future decisions have been encoded in advance in the representation of flow. We demonstrate that the middle and upper layers encode a few short-term future decisions to some extent when planning is successful. Overall, our research analyzes the look-ahead planning mechanisms of LLMs, facilitating future research on LLMs performing planning tasks. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

来源：评论

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INCOMPLETE OBSERVATIONS BIAS SUPPRESSION FOR ABDUCTIVE natural language INFERENCE 49

INCOMPLETE OBSERVATIONS BIAS SUPPRESSION FOR ABDUCTIVE NATUR...

引用

49th IEEE International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Gu, Yu Luo, Xianlong Yang, Meng Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Abductive natural language commonsense reasoning is a task aiming at inferring the most plausible explanation in narrative text for observed events. Previous works mostly concentrate on utilizing powerful pre-trained language models and making better use of excess training data to learn abundant event commonsense knowledge. However, the utilization of causal effect is hidden in the language reasoning process and the explicit constraint of the causal effect between events has not been explored, resulting in biased inference. The model may focus on one observed event and make the wrong prediction while ignoring the other helpful events. To reveal the problem we modify the original task by appending unrelated text to the context which won't change the causal relation. And typical methods get worse in the new task as they are not good at utilizing the complementary between the two observations. Motivated by eliminating the shortcut from incomplete observation and utilizing the complementarity of the two observations, we propose an incomplete observation bias suppression method to guide the training process. Results show our approach can ease the problem revealed in the new task. Based on the proposed method and the new task, our method also get competitive result on the original task.

关键词： natural language Inference Bias Suppression Context splicing Pre-trained methods

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Gradient-based Gradual Pruning for language-Specific Multilingual Neural Machine Translation

Gradient-based Gradual Pruning for Language-Specific Multili...

引用

2023 conference on empirical methods in natural language processing, EMNLP 2023

作者： He, Dan Pham, Minh-Quang Ha, Thanh-Le Turchi, Marco Zoom Video Communications United States

ISBN: (纸本)9798891760608

Multilingual neural machine translation (MNMT) offers the convenience of translating between multiple languages with a single model. However, MNMT often suffers from performance degradation in high-resource languages compared to bilingual counterparts. This degradation is commonly attributed to parameter interference, which occurs when parameters are fully shared across all language pairs. In this work, to tackle this issue we propose a gradient-based gradual pruning technique for MNMT. Our approach aims to identify an optimal sub-network for each language pair within the multilingual model by leveraging gradient-based information as pruning criterion and gradually increasing the pruning ratio as schedule. Our approach allows for partial parameter sharing across language pairs to alleviate interference, and each pair preserves its unique parameters to capture language-specific information. Comprehensive experiments on IWSLT and WMT datasets show that our approach yields a notable performance gain on both datasets. ©2023 Association for Computational Linguistics.

关键词： Neural machine translation

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TPTU-v2: Boosting Task Planning and Tool Usage of Large language Model-based Agents in Real-world Industry Systems

TPTU-v2: Boosting Task Planning and Tool Usage of Large Lang...

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

作者： Kong, Yilun Ruan, Jingqing Chen, Yihong Zhang, Bin Bao, Tianpeng Shi, Shiwei Du, Guoqing Hu, Xiaoru Mao, Hangyu Li, Ziyue Zeng, Xingyu Zhao, Rui Wang, Xueqian Shenzhen International Graduate School Tsinghua University China SenseTime Research China Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China University of Cologne Germany Qingyuan Research Institute Shanghai Jiaotong University China

ISBN: (纸本)9798891761667

Large language Models (LLMs) have demonstrated proficiency in addressing tasks that necessitate a combination of task planning and the usage of external tools, such as weather and calculator APIs. However, real-world industrial systems present prevalent challenges in task planning and tool usage: numerous APIs in the real system make it intricate to invoke the appropriate one, while the inherent limitations of LLMs pose challenges in orchestrating an accurate sub-task sequence and API-calling order. This paper introduces a comprehensive framework aimed at enhancing the Task Planning and Tool Usage (TPTU) abilities of LLM-based agents in industry. Our framework comprises three key components designed to address these challenges: (1) the API Retriever selects the most pertinent APIs among the extensive API set;(2) the Demo Selector retrieves task-level demonstrations, which is further used for in-context learning to aid LLMs in accurately decomposing subtasks and effectively invoking hard-to-distinguish APIs;(3) LLM Finetuner tunes a base LLM to enhance its capability for task planning and API calling . We validate our methods using a real-world industry system and an open-sourced academic dataset, demonstrating the efficacy of each individual component as well as the integrated framework. The code is available at here. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Password Classification Using Machine Learning and natural language processing Techniques: methods and Evaluations 8

Password Classification Using Machine Learning and Natural L...

引用

8th Cyber Security in Networking conference

作者： Binh Le Thanh Thai Takii, Tsubasa Tanaka, Hidema Natl Def Acad Dept Comp Sci Yokosuka Kanagawa Japan

ISBN: (纸本)9798331534110;9798331534103

Passwords are the most widely used authentication method and play a crucial role in the field of information security. In this study, we explore the effectiveness of applying machine learning (ML) and natural language processing (NLP) techniques to password classification. We compare the performance of classifiers by using eight ML techniques and four NLP techniques to classify user-created passwords. The experimental results show that the classifier using a combination of Bag-of-Words and Logistic Regression outperforms other classifiers, achieving an accuracy of 98.53% and a recall for weak passwords of 99.68%.

关键词： Password machine learning natural language processing security privacy

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In2Core: Leveraging Influence Functions for Coreset Selection in Instruction Finetuning of Large language Models

In2Core: Leveraging Influence Functions for Coreset Selectio...

引用

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

作者： Joaquin, Ayrton San Wang, Bin Liu, Zhengyuan Asher, Nicholas Lim, Brian Muller, Philippe Chen, Nancy F. CNRS@CREATE Singapore A*STAR Singapore CNRS IRIT France National University of Singapore Singapore A*STAR Singapore

ISBN: (纸本)9798891761681

Despite advancements, fine-tuning Large language Models (LLMs) remains costly due to the extensive parameter count and substantial data requirements for model generalization. Accessibility to computing resources remains a barrier for the open-source community. To address this challenge, we propose the In2Core algorithm, which selects a coreset by analyzing the correlation between training and evaluation samples with a trained model. Notably, we assess the model's internal gradients to estimate this relationship, aiming to rank the contribution of each training point. To enhance efficiency, we propose an optimization to compute influence functions with a reduced number of layers while achieving similar accuracy. By applying our algorithm to instruction fine-tuning data of LLMs, we can achieve similar performance with just 50% of the training data. Meantime, using influence functions to analyze model coverage to certain testing samples could provide a reliable and interpretable signal on the training set's coverage of those test points. © 2024 Association for Computational Linguistics.

关键词： Open systems

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TADI: Topic-aware Attention and Powerful Dual-encoder Interaction for Recall in News Recommendation

TADI: Topic-aware Attention and Powerful Dual-encoder Intera...

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conference on empirical methods in natural language processing (EMNLP)

作者： Jiang, Junxiang

ISBN: (纸本)9798891760615

News recommendation is one of the widest commercialization in natural language processing research area, which aims to recommend news according to user interests. News recall plays an important role in news recommendation. It is to recall candidates from a very large news database. Recent researches of news recall mostly adopt dual-encoder architecture as it provides a much faster recall scheme, and they encode each word equally. However, these works remain two challenges: irrelevant word distraction and weak dual-encoder interaction. Therefore, we propose a model Topic-aware Attention and powerful Dual-encoder Interaction for recall in news recommendation (TADI). To avoid irrelevant word distraction, TADI designs a Topic-aware Attention (TA) which weights words according to news topics. To enhance dual-encoder interaction, TADI provides a cheap yet powerful interaction module, namely Dual-encoder Interaction (DI). DI helps dual encoders interact powerfully based on two auxiliary targets. After performance comparisons between TADI and state-of-the-arts in a series of experiments, we verify the effectiveness of TADI.

关键词： natural language processing systems

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Multilingual Synopses of Movie Narratives: A Dataset for Vision-language Story Understanding

Multilingual Synopses of Movie Narratives: A Dataset for Vis...

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

作者： Sun, Yidan Yu, Jianfei Li, Boyang College of Computing and Data Science Nanyang Technological University Singapore School of Computer Science and Engineering Nanjing University of Science and Technology China

ISBN: (纸本)9798891761681

Story video-text alignment, a core task in computational story understanding, aims to align video clips with corresponding sentences in their descriptions. However, progress on the task has been held back by the scarcity of manually annotated video-text correspondence and the heavy concentration on English narrations of Hollywood movies. To address these issues, in this paper, we construct a large-scale multilingual video story dataset named Multilingual Synopses of Movie Narratives (M-SYMON), containing 13,166 movie summary videos from 7 languages, as well as manual annotation of fine-grained video-text correspondences for 101.5 hours of video. Training on the human annotated data from M-SYMON outperforms the SOTA methods by 15.7 and 16.2 percentage points on Clip Accuracy and Sentence IoU scores, respectively, demonstrating the effectiveness of the annotations. As benchmarks for future research, we create 6 baseline approaches with different multilingual training strategies, compare their performance in both intra-lingual and cross-lingual setups, exemplifying the challenges of multilingual video-text alignment. The dataset is released at: https://***/insundaycathy/M-SyMoN. © 2024 Association for Computational Linguistics.

关键词： Visual languages

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Skills-in-Context: Unlocking Compositionality in Large language Models

Skills-in-Context: Unlocking Compositionality in Large Langu...

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

作者： Chen, Jiaao Pan, Xiaoman Yu, Dian Song, Kaiqiang Wang, Xiaoyang Yu, Dong Chen, Jianshu Tencent AI Lab BellevueWA98004 United States Georgia Institute of Technology United States

ISBN: (纸本)9798891761681

We investigate how to elicit compositional generalization capabilities in large language models (LLMs). Compositional generalization empowers LLMs to solve complex problems by combining foundational skills, a critical reasoning ability akin to human intelligence. However, even the most advanced LLMs currently struggle with this form of reasoning. We examine this problem within the framework of in-context learning and find that demonstrating both foundational skills and compositional examples grounded in these skills within the same prompt context is crucial. We refer to this prompt structure as skills-in-context (SKiC). With as few as two exemplars, this in-context learning structure enables LLMs to tackle more challenging problems requiring innovative skill combinations, achieving near-perfect systematic generalization across a broad range of tasks. Intriguingly, SKiC also unlocks the latent potential of LLMs, allowing them to more actively utilize pre-existing internal skills acquired during earlier pretraining stages to solve complex reasoning problems. The SKiC structure is robust across different skill constructions and exemplar choices and demonstrates strong transferability to new tasks. Finally, inspired by our in-context learning study, we show that fine-tuning LLMs with SKiC-style data can elicit zero-shot weak-to-strong generalization, enabling the models to solve much harder problems directly with standard prompting. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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