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

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ConvKGYarn: Spinning Configurable and Scalable Conversational Knowledge Graph QA Datasets with Large language Models

ConvKGYarn: Spinning Configurable and Scalable Conversationa...

引用

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

作者： Pradeep, Ronak Lee, Daniel Mousavi, Ali Pound, Jeff Sang, Yisi Lin, Jimmy Ilyas, Ihab Potdar, Saloni Arefiyan, Mostafa Li, Yunyao Apple United States University of Waterloo Canada Adobe United States

ISBN: (纸本)9798891761667

The rapid evolution of Large language Models (LLMs) and conversational assistants necessitates dynamic, scalable, and configurable conversational datasets for training and evaluation. These datasets must accommodate diverse user interaction modes, including text and voice, each presenting unique modeling challenges. Knowledge Graphs (KGs), with their structured and evolving nature, offer an ideal foundation for current and precise knowledge. Although human-curated KG-based conversational datasets exist, they struggle to keep pace with the rapidly changing user information needs. We present ConvKGYarn, a scalable method for generating up-to-date and configurable conversational KGQA datasets. Qualitative psychometric analyses demonstrate ConvKGYarn’s effectiveness in producing high-quality data comparable to popular conversational KGQA datasets across various metrics. ConvKGYarn excels in adhering to human interaction configurations and operating at a significantly larger scale. We showcase ConvKGYarn’s utility by testing LLMs on diverse conversations — exploring model behavior on conversational KGQA sets with different configurations grounded in the same KG fact set. Our results highlight the ability of ConvKGYarn to improve KGQA foundations and evaluate parametric knowledge of LLMs, thus offering a robust solution to the constantly evolving landscape of conversational assistants. © 2024 Association for Computational Linguistics.

关键词： Knowledge graph

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Unveiling Narrative Reasoning Limits of Large language Models with Trope in Movie Synopses

Unveiling Narrative Reasoning Limits of Large Language Model...

引用

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

作者： Su, Hung-Ting Hsu, Ya-Ching Lin, Xudong Shi, Xiang-Qian Niu, Yulei Hsu, Han-Yuan Lee, Hung-Yi Hsu, Winston H. National Taiwan University Taiwan Columbia University United States Mobile Drive Technology

ISBN: (纸本)9798891761681

Large language models (LLMs) equipped with chain-of-thoughts (CoT) prompting have shown significant multi-step reasoning capabilities in factual content like mathematics, commonsense, and logic. However, their performance in narrative reasoning, which demands greater abstraction capabilities, remains unexplored. This study utilizes tropes in movie synopses to assess the narrative reasoning abilities of state-of-the-art LLMs and uncovers their low performance. We introduce a trope-wise querying approach to address these challenges and boost the F1 score by 11.8 points. Moreover, while prior studies suggest that CoT enhances multi-step reasoning, this study shows CoT can cause hallucinations in narrative content, reducing GPT-4's performance. We also introduce an Adversarial Injection method to embed trope-related text tokens into movie synopses without explicit tropes, revealing CoT's heightened sensitivity to such injections. Our comprehensive analysis provides insights for future research directions. Code available: https://***/Shelley1214/Trope. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

来源：评论

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natural language processing (NLP) Techniques: Usability in Human-Computer Interactions 6

Natural Language Processing (NLP) Techniques: Usability in H...

引用

6th International conference on natural language processing (ICNLP)

作者： Das, Satyesh Das, Divyesh Rajiv Gandhi Inst Petr Technol Comp Sci & Engn Amethi Uttar Pradesh India Thapar Inst Engn & Technol Elect & Comp Engn Patiala Punjab India

ISBN: (纸本)9798350349122;9798350349115

The study sheds light on the concept of natural language processing (NLP) which enables Human-Computer Interaction (HCI). Recent advances in Artificial Intelligence (AI) and brain architecture have led to significant advancements in NLP. These upgrades have made it possible to use more recent dialect models and computations. Machines are now capable of understanding and producing human language with astounding accuracy and unmatched attentiveness due to the development of deep learning and the availability of large datasets. Evaluating the role of NLP in improving human-computer interaction is the main purpose of this paper. In regard to this, secondary methods have been used for data collection as well as thematic analysis process has been adopted for interpreting data. As results, it has been identified that NLP techniques enable computerised opinion research, supporting organisations in making information-driven decisions and boosting customer loyalty.

关键词： human-computer interaction NLP machine translation language complexity

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Training-free Deep Concept Injection Enables language Models for Video Question Answering

Training-free Deep Concept Injection Enables Language Models...

引用

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

作者： Lin, Xudong Li, Manling Zemel, Richard Ji, Heng Chang, Shih-Fu Columbia University United States University of Illinois Urbana-Champaign United States

ISBN: (纸本)9798891761643

Recently, enabling pretrained language models (PLMs) to perform zero-shot crossmodal tasks such as video question answering has been extensively studied. A popular approach is to learn a projection network that projects visual features into the input text embedding space of a PLM, as well as feed-forward adaptation layers, with the weights of the PLM frozen. However, is it really necessary to learn such additional layers? In this paper, we make the first attempt to demonstrate that the PLM is able to perform zero-shot crossmodal tasks without any crossmodal pretraining, when the observed visual concepts are injected as both additional input text tokens and augmentation in the intermediate features within each feed-forward network for the PLM. Specifically, inputting observed visual concepts as text tokens helps to inject them through the self-attention layers in the PLM;to augment the intermediate features in a way that is compatible with the PLM, we propose to construct adaptation layers based on the intermediate representation of concepts (obtained by solely inputting them to the PLM). These two complementary injection mechanisms form the proposed Deep Concept Injection, which comprehensively enables the PLM to perceive instantly without crossmodal pretraining. Extensive empirical analysis on zero-shot video question answering, as well as visual question answering, shows Deep Concept Injection achieves competitive or even better results in both zero-shot and fine-tuning settings, compared to state-of-the-art methods that require crossmodal pretraining. © 2024 Association for Computational Linguistics.

关键词： Question answering

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Monotonic Paraphrasing Improves Generalization of language Model Prompting

Monotonic Paraphrasing Improves Generalization of Language M...

引用

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

作者： Liu, Qin Wang, Fei Xu, Nan Yan, Tianyi Meng, Tao Chen, Muhao UC Davis United States USC United States UCLA United States

ISBN: (纸本)9798891761681

Performance of large language models (LLMs) may vary with different prompts or instructions for even the same *** commonly recognized factor for this phenomenon is the model's familiarity with the given prompt or instruction, which is typically estimated by its ***, finding the prompt with the lowest perplexity is challenging, given the enormous space of possible prompting *** this paper, we propose monotonic paraphrasing (MONOPARA), an end-to-end decoding strategy that paraphrases given prompts or instructions into their lower perplexity counterparts based on an ensemble of a paraphrase LM for prompt (or instruction) rewriting, and a target LM (*** prompt or instruction executor) that constrains the generation for lower *** ensemble decoding process can efficiently paraphrase the original prompt without altering its semantic meaning, while monotonically decreasing the perplexity of each generation as calculated by the target *** explore in detail both greedy and search-based decoding as two alternative decoding schemes of ***, MONOPARA does not require any training and can monotonically lower the perplexity of the paraphrased prompt or instruction, leading to improved performance of zero-shot LM prompting as evaluated on a wide selection of *** addition, MONOPARA is also shown to effectively improve LMs' generalization on perturbed and unseen task instructions. © 2024 Association for Computational Linguistics.

关键词： Semantics

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The State of the Art of Large language Models on Chartered Financial Analyst Exams

The State of the Art of Large Language Models on Chartered F...

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

作者： Mahfouz, Mahmoud Callanan, Ethan Sibue, Mathieu Papadimitriou, Antony Ma, Zhiqiang Liu, Xiaomo Zhu, Xiaodan J.P. Morgan AI Research Queen’s University Canada

ISBN: (纸本)9798891761667

The Chartered Financial Analyst (CFA) program is one of the most widely recognized financial certifications globally. In this work, we test a variety of state-of-the-art large language models (LLMs) on mock CFA exams to provide an overview of their financial analysis capabilities using the same evaluation standards applied for human professionals. We benchmark five leading proprietary models and nine open-source models on all three levels of the CFA through challenging multiple-choice and essay questions. We find that flagship proprietary models perform relatively well and can solidly pass levels I and II exams, but fail at level III due to essay questions. Open-source models generally fall short of estimated passing scores, but still show strong performance considering their size, cost, and availability advantages. We also find that using textbook data helps bridge the gap between open-source and proprietary models to a certain extent, despite reduced gains in CFA levels II and III. By understanding the current financial analysis abilities of LLMs, we aim to guide practitioners on which models are best suited for enhancing automation in the financial industry. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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How Reliable Are Automatic Evaluation methods for Instruction-Tuned LLMs?

How Reliable Are Automatic Evaluation Methods for Instructio...

引用

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

作者： Doostmohammadi, Ehsan Holmström, Oskar Kuhlmann, Marco Linköping University Sweden

ISBN: (纸本)9798891761681

Work on instruction-tuned Large language Models (LLMs) has used automatic methods based on text overlap and LLM judgments as cost-effective alternatives to human evaluation. In this paper, we perform a meta-evaluation of such methods and assess their reliability across a broad range of tasks. In evaluating how well automatic methods align with human evaluations, correlation metrics are the most commonly employed method despite their inherent limitations when dealing with ties and different scales. To address these shortcomings, we use Pairwise Accuracy as an alternative to standard correlation measures. We observe that while automatic evaluation methods can approximate human ratings under specific conditions, their validity is highly context-dependent. Specifically, the simple ROUGE-L metric correlates very well with human ratings for short-answer English tasks but is unreliable in free-form generation tasks and cross-lingual scenarios. The effectiveness of the more advanced method of using GPT-4 as a judge diminishes significantly if reference answers are not included in the prompt, which is the scenario where this method has the potential to provide the most value compared to other metrics. Our findings enhance the understanding of how automatic methods should be applied and interpreted when developing and evaluating instruction-tuned LLMs. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Advancing Adversarial Suffix Transfer Learning on Aligned Large language Models

Advancing Adversarial Suffix Transfer Learning on Aligned La...

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

作者： Liu, Hongfu Xie, Yuxi Wang, Ye Shieh, Michael National University of Singapore Singapore

ISBN: (纸本)9798891761643

language language Models (LLMs) face safety concerns due to potential misuse by malicious users. Recent red-teaming efforts have identified adversarial suffixes capable of jail-breaking LLMs using the gradient-based search algorithm Greedy Coordinate Gradient (GCG). However, GCG struggles with computational inefficiency, limiting further investigations regarding suffix transferability and scalability across models and data. In this work, we bridge the connection between search efficiency and suffix transferability. We propose a two-stage transfer learning framework, DeGCG, which decouples the search process into behavior-agnostic pre-searching and behavior-relevant post-searching. Specifically, we employ direct first target token optimization in pre-searching to facilitate the search process. We apply our approach to cross-model, cross-data, and self-transfer scenarios. Furthermore, we introduce an interleaved variant of our approach, i-DeGCG, which iteratively leverages self-transferability to accelerate the search process. Experiments on HarmBench demonstrate the efficiency of our approach across various models and domains. Notably, our i-DeGCG outperforms the baseline on Llama2-chat-7b with ASRs of 43.9 (+22.2) and 39.0 (+19.5) on valid and test sets, respectively. Further analysis on cross-model transfer indicates the pivotal role of first target token optimization in leveraging suffix transferability for efficient searching. © 2024 Association for Computational Linguistics.

关键词： Adversarial machine learning

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Mentor-KD: Making Small language Models Better Multi-step Reasoners

Mentor-KD: Making Small Language Models Better Multi-step Re...

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

作者： Lee, Hojae Kim, Junho Lee, SangKeun Department of Computer Science and Engineering Korea Republic of Department of Artificial Intelligence Korea University Seoul Korea Republic of

ISBN: (纸本)9798891761643

Large language Models (LLMs) have displayed remarkable performances across various complex tasks by leveraging Chain-of-Thought (CoT) prompting. Recently, studies have proposed a Knowledge Distillation (KD) approach, reasoning distillation, which transfers such reasoning ability of LLMs through fine-tuning language models of multi-step rationales generated by LLM teachers. However, they have inadequately considered two challenges regarding insufficient distillation sets from the LLM teacher model, in terms of 1) data quality and 2) soft label provision. In this paper, we propose Mentor-KD, which effectively distills the multi-step reasoning capability of LLMs to smaller LMs while addressing the aforementioned challenges. Specifically, we exploit a mentor, intermediate-sized task-specific fine-tuned model, to augment additional CoT annotations and provide soft labels for the student model during reasoning distillation. We conduct extensive experiments and confirm Mentor-KD's effectiveness across various models and complex reasoning tasks. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Optimized Speculative Sampling for GPU Hardware Accelerators

Optimized Speculative Sampling for GPU Hardware Accelerators

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

作者： Wagner, Dominik Lee, Seanie Baumann, Ilja Seeberger, Philipp Riedhammer, Korbinian Bocklet, Tobias Technische Hochschule Nürnberg Georg Simon Ohm Germany KAIST Korea Republic of

ISBN: (纸本)9798891761643

In this work, we optimize speculative sampling for parallel hardware accelerators to improve sampling speed. We notice that substantial portions of the intermediate matrices necessary for speculative sampling can be computed concurrently. This allows us to distribute the workload across multiple GPU threads, enabling simultaneous operations on matrix segments within thread blocks. This results in profiling time improvements ranging from 6% to 13% relative to the baseline implementation, without compromising accuracy. To further accelerate speculative sampling, probability distributions parameterized by softmax are approximated by sigmoid. This approximation approach results in significantly greater relative improvements in profiling time, ranging from 37% to 94%, with a minor decline in accuracy. We conduct extensive experiments on both automatic speech recognition and summarization tasks to validate the effectiveness of our optimization methods. © 2024 Association for Computational Linguistics.

关键词： Graphics processing unit

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