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

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Toward Improving Robustness of Coreference Resolution for Thai language 6

Toward Improving Robustness of Coreference Resolution for Th...

引用

6th International conference on natural language processing (ICNLP)

作者： Suwannapichat, Poomphob Tarnpradab, Sansiri Prom-on, Santitham King Mongkuts Univ Technol Thonburi Dept Comp Engn Bangkok Thailand

ISBN: (纸本)9798350349122;9798350349115

Coreference resolution aims to identify expressions in a text that refer to the same entity and establish connections between them. This paper presents an improved method for Thai coreference resolution, extending the F-coref architecture with two key enhancements. First, to handle the absence of explicit word boundaries in Thai, a pre-tokenization step is implemented before applying the model tokenizer. This ensures accurate alignment between gold coreference labels and resulting tokens. Second, an improved loss function is proposed to overcome a challenge encountered by F-coref during training. This modification prevents the model from solely optimizing coreference to null spans, ensuring a more balanced training trajectory. empirical evaluations demonstrate the effectiveness of these modifications in boosting the robustness of Thai coreference resolution.

关键词： Coreference resolution Anaphora resolution Pronoun resolution

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SciER: An Entity and Relation Extraction Dataset for Datasets, methods, and Tasks in Scientific Documents

SciER: An Entity and Relation Extraction Dataset for Dataset...

引用

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

作者： Zhang, Qi Chen, Zhijia Pan, Huitong Caragea, Cornelia Latecki, Longin Jan Dragut, Eduard Temple University United States University of Illinois Chicago United States

ISBN: (纸本)9798891761643

Scientific information extraction (SciIE) is critical for converting unstructured knowledge from scholarly articles into structured data (entities and relations). Several datasets have been proposed for training and validating SciIE models. However, due to the high complexity and cost of annotating scientific texts, those datasets restrict their annotations to specific parts of paper, such as abstracts, resulting in the loss of diverse entity mentions and relations in context. In this paper, we release a new entity and relation extraction dataset for entities related to datasets, methods, and tasks in scientific articles. Our dataset contains 106 manually annotated full-text scientific publications with over 24k entities and 12k relations. To capture the intricate use and interactions among entities in full texts, our dataset contains a fine-grained tag set for relations. Additionally, we provide an out-of-distribution test set to offer a more realistic evaluation. We conduct comprehensive experiments, including state-of-the-art supervised models and our proposed LLM baselines, and highlight the challenges presented by our dataset, encouraging the development of innovative models to further the field of SciIE. © 2024 Association for Computational Linguistics.

关键词： Data assimilation

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Control Large language Models via Divide and Conquer

Control Large Language Models via Divide and Conquer

引用

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

作者： Li, Bingxuan Wang, Yiwei Meng, Tao Chang, Kai-Wei Peng, Nanyun University of California Los Angeles United States University of California Merced United States

ISBN: (纸本)9798891761643

This paper investigates controllable generation for large language models (LLMs) with prompt-based control, focusing on Lexically Constrained Generation (LCG). We systematically evaluate the performance of LLMs on satisfying lexical constraints with prompt-based control, as well as their efficacy in downstream applications. We conclude that LLMs face significant challenges in consistently satisfying lexical constraints with prompt-based control. We identified three key limitations of LLMs for LCG, including (1) position bias, where LLMs tend to satisfy constraints that appear in specific positions within the input;(2) low responsiveness to decoding parameters, which render minimal impact on control of LLMs;and (3) struggle with handling the inherent complexity of certain constraints (e.g., compound words). To address these issues, we introduce a Divide and Conquer Generation strategy, effective for both white-box and black-box LLMs, to enhance LLMs performance in LCG tasks, which demonstrates over 90% improvement on success rate in the most challenging LCG task. Our analysis provides valuable insights into the performance of LLMs in LCG with prompt-based control, and our proposed strategy offers a pathway to more sophisticated and customized text generation applications. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Don't Just Say "I don't know"! Self-aligning Large language Models for Responding to Unknown Questions with Explanations

Don't Just Say "I don't know"! Self-aligning Large Language ...

引用

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

作者： Deng, Yang Zhao, Yong Li, Moxin Ng, See-Kiong Chua, Tat-Seng Singapore Management University Singapore National University of Singapore Singapore

ISBN: (纸本)9798891761643

Despite the remarkable abilities of Large language Models (LLMs) to answer questions, they often display a considerable level of overconfidence even when the question does not have a definitive answer. To avoid providing hallucinated answers to these unknown questions, existing studies typically investigate approaches to refusing to answer these questions. In this work, we propose a novel and scalable self-alignment method to utilize the LLM itself to enhance its response-ability to different types of unknown questions, being capable of not just refusing to answer but further proactively providing explanations to the unanswerability of unknown questions. Specifically, the Self-Align method first employ a two-stage class-aware self-augmentation approach to generate a large amount of unknown question-response data. Then we conduct disparity-driven self-curation to select qualified data for fine-tuning the LLM itself for aligning the responses to unknown questions as desired. Experimental results on two datasets across four types of unknown questions validate the superiority of the Self-Aligned method over existing baselines in terms of three types of task formulation. © 2024 Association for Computational Linguistics.

关键词： Question answering

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MuMath-Code: Combining Tool-Use Large language Models with Multi-perspective Data Augmentation for Mathematical Reasoning

MuMath-Code: Combining Tool-Use Large Language Models with M...

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

作者： Yin, Shuo You, Weihao Ji, Zhilong Zhong, Guoqiang Bai, Jinfeng Tomorrow Advancing Life China College of Computer Science and Technology Ocean University of China China

ISBN: (纸本)9798891761643

The tool-use Large language Models (LLMs) that integrate with external Python interpreters have significantly enhanced mathematical reasoning capabilities for open-source LLMs, while tool-free methods chose another track: augmenting math reasoning data. However, a great method to integrate the above two research paths and combine their advantages remains to be explored. In this work, we firstly include new math questions via multi-perspective data augmenting methods and then synthesize code-nested solutions to them. The open LLMs (e.g., Llama-2) are finetuned on the augmented dataset to get the resulting models, MuMath-Code (µ-Math-Code). During the inference phase, our MuMath-Code generates code and interacts with the external python interpreter to get the execution results. Therefore, MuMath-Code leverages the advantages of both the external tool and data augmentation. To fully leverage the advantages of our augmented data, we propose a two-stage training strategy: In Stage-1, we finetune Llama-2 on pure CoT data to get an intermediate model, which then is trained on the code-nested data in Stage-2 to get the resulting MuMath-Code. Our MuMath-Code-7B achieves 83.8% on GSM8K and 52.4% on MATH, while MuMath-Code-70B model achieves new state-of-the-art performance among open methods-achieving 90.7% on GSM8K and 55.1% on MATH. Extensive experiments validate the combination of tool use and data augmentation, as well as our two-stage training strategy. We release the proposed dataset along with the associated code for public use: https://***/youweihao-tal/MuMath-Code. © 2024 Association for Computational Linguistics.

关键词： Python

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Repairs in a Block World: A New Benchmark for Handling User Corrections with Multi-Modal language Models

Repairs in a Block World: A New Benchmark for Handling User ...

引用

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

作者： Chiyah-Garcia, Javier Suglia, Alessandro Eshghi, Arash Heriot-Watt University Edinburgh United Kingdom

ISBN: (纸本)9798891761643

In dialogue, the addressee may initially misunderstand the speaker and respond erroneously, often prompting the speaker to correct the misunderstanding in the next turn with a Third Position Repair (TPR). The ability to process and respond appropriately to such repair sequences is thus crucial in conversational AI systems. In this paper, we first collect, analyse, and publicly release BLOCKWORLD-REPAIRS: a dataset of multi-modal TPR sequences in an instruction-following manipulation task that is, by design, rife with referential ambiguity. We employ this dataset to evaluate several state-ofthe-art Vision and language Models (VLM) across multiple settings, focusing on their capability to process and accurately respond to TPRs and thus recover from miscommunication. We find that, compared to humans, all models significantly underperform in this task. We then show that VLMs can benefit from specialised losses targeting relevant tokens during fine-tuning, achieving better performance and generalising better to new scenarios. Our results suggest that these models are not yet ready to be deployed in multi-modal collaborative settings where repairs are common, and highlight the need to design training regimes and objectives that facilitate learning from interaction. Our code and data are available at ***/JChiyah/blockworld-repairs. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Show and Guide: Instructional-Plan Grounded Vision and language Model

Show and Guide: Instructional-Plan Grounded Vision and Langu...

引用

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

作者： Glória-Silva, Diogo Semedo, David Magalhães, João NOVA LINCS NOVA School of Science and Technology Portugal

ISBN: (纸本)9798891761643

Guiding users through complex procedural plans is an inherently multimodal task in which having visually illustrated plan steps is crucial to deliver an effective plan guidance. However, existing works on plan-following language models (LMs) often are not capable of multimodal input and output. In this work, we present MM-PlanLLM, the first multimodal LLM designed to assist users in executing instructional tasks by leveraging both textual plans and visual information. Specifically, we bring cross-modality through two key tasks: Conversational Video Moment Retrieval, where the model retrieves relevant step-video segments based on user queries, and Visually-Informed Step Generation, where the model generates the next step in a plan, conditioned on an image of the user's current progress. MM-PlanLLM is trained using a novel multitask-multistage approach, designed to gradually expose the model to multimodal instructional-plans semantic layers, achieving strong performance on both multimodal and textual dialogue in a plan-grounded setting. Furthermore, we show that the model delivers cross-modal temporal and plan-structure representations aligned between textual plan steps and instructional video moments. © 2024 Association for Computational Linguistics.

关键词： Semantics

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CELLO : Causal Evaluation of Large Vision-language Models

CELLO : Causal Evaluation of Large Vision-Language Models

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

作者： Chen, Meiqi Peng, Bo Zhang, Yan Lu, Chaochao State Key Laboratory of General Artificial Intelligence Peking University Beijing China School of Intelligence Science and Technology Peking University China Shanghai Jiao Tong University China Shanghai Artificial Intelligence Laboratory China

ISBN: (纸本)9798891761643

Causal reasoning is fundamental to human intelligence and crucial for effective decision-making in real-world environments. Despite recent advancements in large vision-language models (LVLMs), their ability to comprehend causality remains unclear. Previous work typically focuses on commonsense causality between events and/or actions, which is insufficient for applications like embodied agents and lacks the explicitly defined causal graphs required for formal causal reasoning. To overcome these limitations, we introduce a fine-grained and unified definition of causality involving interactions between humans and/or objects. Building on the definition, we construct a novel dataset, CELLO, consisting of 14,094 causal questions across all four levels of causality: discovery, association, intervention, and counterfactual. This dataset surpasses traditional commonsense causality by including explicit causal graphs that detail the interactions between humans and objects. Extensive experiments on CELLO reveal that current LVLMs still struggle with causal reasoning tasks, but they can benefit significantly from our proposed CELLO-CoT, a causally inspired chain-of-thought prompting strategy. Both quantitative and qualitative analyses from this study provide valuable insights for future research. Our project page is at https://***/OpenCausaLab/CELLO. © 2024 Association for Computational Linguistics.

关键词： Visual languages

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TransferCVLM: Transferring Cross-Modal Knowledge for Vision-language Modeling

TransferCVLM: Transferring Cross-Modal Knowledge for Vision-...

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

作者： Choi, Dongha Kim, Jung-Jae Lee, Hyunju GIST Artificial Intelligence Graduate School Gwangju Korea Republic of A*STAR Singapore

ISBN: (纸本)9798891761681

Recent large vision-language multimodal models pre-trained with huge amount of image-text pairs show remarkable performances in downstream tasks. However, the multimodal pre-training has limitations in terms of resources and training time when it comes to obtaining new models that surpass existing models. To overcome these issues, we propose TransferCVLM, a method of efficient knowledge transfer that integrates pre-trained uni-modal models (and cross-modal fusion-encoder) into a combined vision-language model (CVLM), without pre-training the CVLM with large amount of multimodal data, and then for each task application, fine-tunes the CVLM and transfers the multimodal knowledge of a teacher vision-language model to the CVLM by using knowledge distillation techniques. We demonstrate that 1) the fine-tuned CVLM performs comparable to other vision-language models of similar size, that 2) the multimodal knowledge transfer consistently enhances the CVLM, and the knowledge-transferred CVLM composed of large-size unimodal models outperforms the teacher multimodal model in most of downstream tasks, and that 3) TransferCVLM can also be used for model compression when using small-size unimodal models. We estimate that the training of TransferCVLM takes only 6% of pretraining of other vision-language models. Our code is available at https://***/DMCBGIST/TransferCVLM. © 2024 Association for Computational Linguistics.

关键词： Visual languages

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MIXTURE-OF-SKILLS: Learning to Optimize Data Usage for Fine-Tuning Large language Models

MIXTURE-OF-SKILLS: Learning to Optimize Data Usage for Fine-...

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

作者： Wu, Minghao Vu, Thuy-Trang Qu, Lizhen Haffari, Gholamreza Monash University Australia

ISBN: (纸本)9798891761643

Large language models (LLMs) are typically fine-tuned on diverse and extensive datasets sourced from various origins to develop a comprehensive range of skills, such as writing, reasoning, chatting, coding, and more. Each skill has unique characteristics, and these datasets are often heterogeneous and imbalanced, making the fine-tuning process highly challenging. Balancing the development of each skill while ensuring the model maintains its overall performance requires sophisticated techniques and careful dataset curation. In this work, we propose a general, model-agnostic, reinforcement learning framework, MIXTURE-OF-SKILLS (MOS), that learns to optimize data usage automatically during the fine-tuning process. This framework ensures the optimal comprehensive skill development of LLMs by dynamically adjusting the focus on different datasets based on their current learning state. To validate the effectiveness of MOS, we conduct extensive experiments using three diverse LLM backbones on two widely used benchmarks and demonstrate that MOS substantially enhances model performance. Building on the success of MOS, we propose MOSPEC, an adaptation for task-specific fine-tuning, which harnesses the utilities of various datasets for a specific purpose. Our work underlines the significance of dataset rebalancing and present MOS as a powerful, general solution for optimizing data usage in the fine-tuning of LLMs for various purposes. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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