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

作者： Zheng, JiaYing Zhang, HaiNan Wang, LingXiang Qiu, WangJie Zheng, HongWei Zheng, ZhiMing Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing Institute of Artificial Intelligence Beihang University China Beijing Academy of Blockchain and Edge Computing China

ISBN: (纸本)9798891761643

Private data, being larger and quality-higher than public data, can greatly improve large language models (LLM). However, due to privacy concerns, this data is often dispersed in multiple silos, making its secure utilization for LLM training a challenge. Federated learning (FL) is an ideal solution for training models with distributed private data, but traditional frameworks like FedAvg are unsuitable for LLM due to their high computational demands on clients. An alternative, split learning, offloads most training parameters to the server while training embedding and output layers locally, making it more suitable for LLM. Nonetheless, it faces significant challenges in security and efficiency. Firstly, the gradients of embeddings are prone to attacks, leading to potential reverse engineering of private data. Furthermore, the server's limitation of handle only one client's training request at a time hinders parallel training, severely impacting training efficiency. In this paper, we propose a Federated Learning framework for LLM, named FL-GLM, which prevents data leakage caused by both server-side and peer-client attacks while improving training efficiency. Specifically, we first place the input block and output block on local client to prevent embedding gradient attacks from server. Secondly, we employ key-encryption during client-server communication to prevent reverse engineering attacks from peer-clients. Lastly, we employ optimization methods like client-batching or server-hierarchical, adopting different acceleration methods based on the actual computational capabilities of the server. Experimental results on NLU and generation tasks demonstrate that FL-GLM achieves comparable metrics to centralized chatGLM model, validating the effectiveness of our federated learning framework. © 2024 Association for Computational Linguistics.

关键词： Federated learning

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The SIFo Benchmark: Investigating the Sequential Instruction Following Ability of Large language Models

The SIFo Benchmark: Investigating the Sequential Instruction...

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

作者： Chen, Xinyi Liao, Baohao Qi, Jirui Eustratiadis, Panagiotis Monz, Christof Bisazza, Arianna de Rijke, Maarten University of Amsterdam Netherlands University of Groningen Netherlands

ISBN: (纸本)9798891761681

Following multiple instructions is a crucial ability for large language models (LLMs). Evaluating this ability comes with significant challenges: (i) limited coherence between multiple instructions, (ii) positional bias where the order of instructions affects model performance, and (iii) a lack of objectively verifiable tasks. To address these issues, we introduce a benchmark designed to evaluate models' abilities to follow multiple instructions through sequential instruction following (SIFo) tasks. In SIFo, the successful completion of multiple instructions is verifiable by examining only the final instruction. Our benchmark evaluates instruction following using four tasks (text modification, question answering, mathematics, and security rules), each assessing different aspects of sequential instruction following. Our evaluation of popular LLMs, both closed-source and open-source, shows that more recent and larger models significantly outperform their older and smaller counterparts on the SIFo tasks, validating the benchmark's effectiveness. All models struggle with following sequences of instructions, hinting at an important lack of robustness of today's language models. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Evaluating Subjective Cognitive Appraisals of Emotions from Large language Models

Evaluating Subjective Cognitive Appraisals of Emotions from ...

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conference on empirical methods in natural language processing (EMNLP)

作者： Zhan, Hongli Ong, Desmond C. Li, Junyi Jessy Univ Texas Austin Dept Linguist Austin TX 78712 USA Univ Texas Austin Dept Psychol Austin TX USA

ISBN: (纸本)9798891760615

The emotions we experience involve complex processes;besides physiological aspects, research in psychology has studied cognitive appraisals where people assess their situations subjectively, according to their own values (Scherer, 2005). Thus, the same situation can often result in different emotional experiences. While the detection of emotion is a well-established task, there is very limited work so far on the automatic prediction of cognitive appraisals. This work fills the gap by presenting COVIDET- APPRAISALS, the most comprehensive dataset to-date that assesses 24 appraisal dimensions, each with a natural language rationale, across 241 Reddit posts. COVIDET- APPRAISALS presents an ideal testbed to evaluate the ability of large language models- excelling at a wide range of NLP tasks - to automatically assess and explain cognitive appraisals. We found that while the best models are performant, open-sourced LLMs fall short at this task, presenting a new challenge in the future development of emotionally intelligent models. We release our dataset at https://***/ honglizhan/CovidET-Appraisals-Public.

关键词： natural language processing systems

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A novel idea generation tool using a structured conversational AI (CAI) system

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AI EDAM-ARTIFICIAL INTELLIGENCE FOR ENGINEERING DESIGN ANALYSIS AND MANUFACTURING 2025年 39卷 e11-e11页

作者： Sankar, B. Sen, Dibakar Indian Inst Sci IISc Dept Mech Engn Bangalore India Indian Inst Sci IISc Dept Design & Mfg Bangalore India

This article presents a novel conversational artificial intelligence (CAI)-enabled active ideation system as a creative idea generation tool to assist novice product designers in mitigating the initial latency and ideation bottlenecks that are commonly observed. It is a dynamic, interactive, and contextually responsive approach, actively involving a large language model (LLM) from the domain of natural language processing (NLP) in artificial intelligence (AI) to produce multiple statements of potential ideas for different design problems. Integrating such AI models with ideation creates what we refer to as an active ideation scenario, which helps foster continuous dialog-based interaction, context-sensitive conversation, and prolific idea generation. An empirical study was conducted with 30 novice product designers to generate multiple ideas for given problems using traditional methods and the new CAI-based interface. The ideas generated by both methods were qualitatively evaluated by a panel of experts. The findings demonstrated the relative superiority of the proposed tool for generating prolific, meaningful, novel, and diverse ideas. The interface was enhanced by incorporating a prompt-engineered structured dialog style for each ideation stage to make it uniform and more convenient for the product designers. A pilot study was conducted and the resulting responses of such a structured CAI interface were found to be more succinct and aligned toward the subsequent design stage. The article thus established the rich potential of using generative AI (Gen-AI) for the early ill-structured phase of the creative product design process.

关键词： artificial intelligence generative pretrained transformer ideation large language model product design conversational AI

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Visual Storytelling with Question-Answer Plans

Visual Storytelling with Question-Answer Plans

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conference on empirical methods in natural language processing (EMNLP)

作者： Liu, Danyang Lapata, Mirella Keller, Frank Univ Edinburgh Sch Informat Inst Language Cognit & Computat 10 Crichton St Edinburgh EH8 9AB Midlothian Scotland

ISBN: (纸本)9798891760615

Visual storytelling aims to generate compelling narratives from image sequences. Existing models often focus on enhancing the representation of the image sequence, e.g., with external knowledge sources or advanced graph structures. Despite recent progress, the stories are often repetitive, illogical, and lacking in detail. To mitigate these issues, we present a novel framework which integrates visual representations with pretrained language models and planning. Our model translates the image sequence into a visual prefix, a sequence of continuous embeddings which language models can interpret. It also leverages a sequence of question-answer pairs as a blueprint plan for selecting salient visual concepts and determining how they should be assembled into a narrative. Automatic and human evaluation on the VIST benchmark (Huang et al., 2016) demonstrates that blueprint-based models generate stories that are more coherent, interesting, and natural compared to competitive baselines and state-of-the-art systems.

关键词： Blueprints

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Is It Good Data for Multilingual Instruction Tuning or Just Bad Multilingual Evaluation for Large language Models?

Is It Good Data for Multilingual Instruction Tuning or Just ...

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

作者： Chen, Pinzhen Yu, Simon Guo, Zhicheng Haddow, Barry University of Edinburgh United Kingdom Northeastern University United States Tsinghua University China

ISBN: (纸本)9798891761643

Multilingual large language models are designed, claimed, and expected to cater to speakers of varied languages. We hypothesise that the current practices of fine-tuning and evaluating these models may not perfectly align with this objective owing to a heavy reliance on translation, which cannot cover language-specific knowledge but can introduce translation defects. It remains unknown whether the nature of the instruction data has an impact on the model output;conversely, it is questionable whether translated test sets can capture such nuances. Due to the often coupled practices of using translated data in both stages, such imperfections could have been overlooked. This work investigates these issues using controlled native or translated data during the instruction tuning and evaluation stages. We show that native or generation benchmarks reveal a notable difference between native and translated instruction data especially when model performance is high, whereas other types of test sets cannot. The comparison between round-trip and single-pass translations reflects the importance of knowledge from language-native resources. Finally, we demonstrate that regularization is beneficial to bridging this gap on structured but not generative tasks. © 2024 Association for Computational Linguistics.

关键词： Contrastive Learning

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Quantifying and Mitigating Unimodal Biases in Multimodal Large language Models: A Causal Perspective

Quantifying and Mitigating Unimodal Biases in Multimodal Lar...

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

作者： Chen, Meiqi Cao, Yixin Zhang, Yan Lu, Chaochao State Key Laboratory of General Artificial Intelligence Peking University Beijing China School of Intelligence Science and Technology Peking University China School of Computer Science Fudan University China Shanghai Artificial Intelligence Laboratory China

ISBN: (纸本)9798891761681

Recent advancements in Large language Models (LLMs) have facilitated the development of Multimodal LLMs (MLLMs). Despite their impressive capabilities, MLLMs often suffer from over-reliance on unimodal biases (e.g., language bias and vision bias), leading to incorrect answers in complex multimodal tasks. To investigate this issue, we propose a causal framework to interpret the biases in Visual Question Answering (VQA) problems. Within this framework, we conduct an in-depth causal analysis to assess the causal effect of these biases on MLLM predictions. Based on the analysis, we introduce 1) a novel MORE dataset with 12,000 challenging VQA instances requiring multi-hop reasoning and overcoming unimodal biases. 2) a causality-enhanced agent framework CAVE that guides models to comprehensively integrate information from different modalities and mitigate biases. Our experiments show that MLLMs perform poorly on MORE, indicating strong unimodal biases and limited semantic understanding. However, when integrated with our CAVE, promising improvements in reasoning and bias mitigation can be seen. These findings provide important insights for the development of more robust MLLMs and contribute to the broader goal of advancing multimodal AI systems capable of deeper understanding and reasoning. Our project page is at https://***/OpenCausaLab/MORE. © 2024 Association for Computational Linguistics.

关键词： Semantics

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Multilingual k-Nearest-Neighbor Machine Translation

Multilingual k-Nearest-Neighbor Machine Translation

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

作者： Stap, David Monz, Christof Language Technology Lab University of Amsterdam Netherlands

ISBN: (纸本)9798891760608

k-nearest-neighbor machine translation has demonstrated remarkable improvements in machine translation quality by creating a datastore of cached examples. However, these improvements have been limited to high-resource language pairs, with large datastores, and remain a challenge for low-resource languages. In this paper, we address this issue by combining representations from multiple languages into a single datastore. Our results consistently demonstrate substantial improvements not only in low-resource translation quality (up to +3.6 BLEU), but also for high-resource translation quality (up to +0.5 BLEU). Our experiments show that it is possible to create multilingual datastores that are a quarter of the size, achieving a 5.3x speed improvement, by using linguistic similarities for datastore creation. © 2023 Association for Computational Linguistics.

关键词： Machine translation

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natural Disaster Tweets Classification Using Multimodal Data

Natural Disaster Tweets Classification Using Multimodal Data

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conference on empirical methods in natural language processing (EMNLP)

作者： Basit, Mohammad Abdul Shaikh, Salman Ghufran Alam, Bashir Fatima, Zubaida Jamia Millia Islamia Dept Comp Engn New Delhi India King Abdullah Univ Sci & Technol Riyadh Saudi Arabia IIIT Delhi Dept Elect & Commun Engn New Delhi India

ISBN: (纸本)9798891760608

Social media platforms are extensively used for expressing opinions or conveying information. The information available on such platforms can be used for various humanitarian and disaster-related tasks as distributing messages in different formats through social media is quick and easy. Often this useful information during disaster events goes to waste as efficient systems don't exist which can turn these unstructured data into meaningful format which can ultimately assist aid agencies. In disaster identification and assessment, information available is naturally multimodal, however, most existing work has been solely focused on single modalities e.g. images or texts separately. When information from different modalities are integrated, it produces significantly better results. In this paper, we have explored different models which can lead to the development of a system that deals with multimodal datasets and can perform sequential hierarchical classification. Specifically, we aim to find the damage and its severity along with classifying the data into humanitarian categories. The different stages in the hierarchical classification have had their respective models selected by researching with many different modality specific models and approaches of multimodal classification including multi task learning. The hierarchical model can give results at different abstraction levels according to the use cases. Through extensive quantitative and qualitative analysis, we show how our system is effective in classifying the multimodal tweets along with an excellent computational efficiency and assessment performance. With the help of our approach, we aim to support disaster management through identification of situations involving humanitarian tragedies and aid in assessing the severity and type of damage.

关键词： Classification (of information)

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MiniChain: A Small Library for Coding with Large language Models

MiniChain: A Small Library for Coding with Large Language Mo...

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2023 conference on empirical methods in natural language processing: System Demonstrations, EMNLP 2023

作者： Rush, Alexander M. Hugging Face Cornell Tech United States

Programming augmented by large language models (LLMs) opens up many new application areas, but also requires care. LLMs are accurate enough, on average, to replace core functionality, yet make basic mistakes that demonstrate a lack of robustness. An ecosystem of prompting tools, from intelligent agents to new programming languages, has emerged with different solutions for patching LLMs with other tools. In this work, we introduce MiniChain, an opinionated tool for LLM augmented programming, with the design goals of ease-of-use of prototyping, transparency through automatic visualization, and a minimalistic approach to advanced features. The MiniChain library provides core primitives for coding LLM calls, separating out prompt templates, and capturing program structure. The library includes demo implementations of the main applications papers in the area, including chat-bots, code generation, retrieval-based question answering, and complex information extraction. The library is open-source and available at https://***/srush/MiniChain, with code demos available at https://***/, and video demo at https://***/watch?v=VszZ1VnO7sk. © 2023 Association for Computational Linguistics.

关键词： Computational linguistics

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