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

共 15363 条记录，以下是1281-1290 订阅

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FastMem: Fast Memorization of Prompt Improves Context Awareness of Large language Models

FastMem: Fast Memorization of Prompt Improves Context Awaren...

引用

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

作者： Zhu, Junyi Liu, Shuochen Yu, Yu Tang, Bo Yan, Yibo Li, Zhiyu Xiong, Feiyu Xu, Tong Blaschko, Matthew B. ESAT-PSI KU Leuven Belgium University of Science and Technology of China China Institute for Advanced Algorithms Research Shanghai China National University of Singapore Singapore

ISBN: (纸本)9798891761681

Large language models (LLMs) excel in generating coherent text, but they often struggle with context awareness, leading to inaccuracies in tasks requiring faithful adherence to provided information. We introduce FastMem, a novel method designed to enhance instruction fine-tuned LLMs' context awareness through fast memorization of the prompt. FastMem maximizes the likelihood of the prompt before inference by updating only the last Feed-Forward Network (FFN) module. This targeted approach ensures efficient optimization without overfitting, significantly improving the model's ability to comprehend and accurately follow the context. Our experiments demonstrate substantial gains in reading comprehension, text summarization and adherence to output structures. For instance, FastMem improves the accuracy of Llama 3-8B-Inst on the NQ-SWAP dataset from 59.1% to 71.6%, and reduces the output structure failure rate of Qwen 1.5-4B-Chat from 34.9% to 25.5%. Extensive experimental results highlight FastMem's potential to offer a robust solution to enhance the reliability and accuracy of LLMs in various applications. Our code is available at: https://***/IAAR-Shanghai/FastMem. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

来源：评论

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Developing a Pragmatic Benchmark for Assessing Korean Legal language Understanding in Large language Models

Developing a Pragmatic Benchmark for Assessing Korean Legal ...

引用

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

作者： Kim, Yeeun Choi, Jinhwan Choi, Young Rok Park, Hai Jin Choi, Eunkyung Hwang, Wonseok University of Seoul Korea Republic of LBox Korea Republic of Hanyang University Korea Republic of

ISBN: (纸本)9798891761681

Large language models (LLMs) have demonstrated remarkable performance in the legal domain, with GPT-4 even passing the Uniform Bar Exam in the U.S. However their efficacy remains limited for non-standardized tasks and tasks in languages other than English. This underscores the need for careful evaluation of LLMs within each legal system before application. Here, we introduce KBL, a benchmark for assessing the Korean legal language understanding of LLMs, consisting of (1) 7 legal knowledge tasks (510 examples), (2) 4 legal reasoning tasks (288 examples), and (3) the Korean bar exam (4 domains, 53 tasks, 2,510 examples). First two datasets were developed in close collaboration with lawyers to evaluate LLMs in practical scenarios in a certified manner. Furthermore, considering legal practitioners' frequent use of extensive legal documents for research, we assess LLMs in both a closed book setting, where they rely solely on internal knowledge, and a retrieval-augmented generation (RAG) setting, using a corpus of Korean statutes and precedents. The results indicate substantial room and opportunities for improvement. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

来源：评论

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Ask-before-Plan: Proactive language Agents for Real-World Planning

Ask-before-Plan: Proactive Language Agents for Real-World Pl...

引用

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

作者： Zhang, Xuan Deng, Yang Ren, Zifeng Ng, See-Kiong Chua, Tat-Seng National University of Singapore Singapore Singapore Management University Singapore

ISBN: (纸本)9798891761681

The evolution of large language models (LLMs) has enhanced the planning capabilities of language agents in diverse real-world scenarios. Despite these advancements, the potential of LLM-powered agents to comprehend ambiguous user instructions for reasoning and decision-making is still under exploration. In this work, we introduce a new task, Proactive Agent Planning, which requires language agents to predict clarification needs based on user-agent conversation and agent-environment interaction, invoke external tools to collect valid information, and generate a plan to fulfill the user's demands. To study this practical problem, we establish a new benchmark dataset, Ask-before-Plan. To tackle the deficiency of LLMs in proactive planning, we propose a novel multi-agent framework, Clarification-Execution-Planning (CEP), which consists of three agents specialized in clarification, execution, and planning. We introduce the trajectory tuning scheme for the clarification agent and static execution agent, as well as the memory recollection mechanism for the dynamic execution agent. Extensive evaluations and comprehensive analyses conducted on the Ask-before-Plan dataset validate the effectiveness of our proposed framework. © 2024 Association for Computational Linguistics.

关键词： Clarifiers

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LAMBDA: Large language Model-Based Data Augmentation for Multi-Modal Machine Translation

LAMBDA: Large Language Model-Based Data Augmentation for Mul...

引用

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

作者： Wang, Yusong Li, Dongyuan Shen, Jialun Xu, Yicheng Xu, Mingkun Funakoshi, Kotaro Okumura, Manabu Tokyo Institute of Technology Tokyo Japan Guangdong Institute of Intelligence Science and Technology Hengqin Guangdong Zhuhai519031 China

ISBN: (纸本)9798891761681

Multi-modal machine translation (MMT) can reduce ambiguity and semantic distortion compared with traditional machine translation (MT) by utilizing auxiliary information such as images. However, current MMT methods face two primary challenges. The first is their underperformance compared to MT methods based on pre-trained models. The second is the inadequate exploitation and integration of the image modality within the model, primarily due to a lack of triplet training data. A mainstream approach is to introduce large amounts of parallel and monolingual data to train the text model and the visual model separately. However, incorporating extensive external data can result in data imbalance, which may introduce biases during training. Additionally, the collection and cleaning of such large datasets is labor-intensive. To overcome these challenges, we introduce a novel, low-cost, large language model-based data augmentation method called LAMBDA, which can enrich the original samples and expand the dataset without requiring external images and text. We propose a fine-grained image captioning module with a noise filter to hierarchically and accurately extract unexploited information from images. Additionally, we design two specific prompts to guide the GPT-3.5 model in generating enriched texts and the corresponding translations. The enriched samples contain diverse text and strong connections between text and images, leading to significant improvements for MMT baselines, with the highest being an increase of up to 3.83 BLEU score and 3.61 METEOR score. © 2024 Association for Computational Linguistics.

关键词： Semantics

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Transformer Working Memory Enables Regular language Reasoning And natural language Length Extrapolation

Transformer Working Memory Enables Regular Language Reasonin...

引用

conference on empirical methods in natural language processing (EMNLP)

作者： Chi, Ta-Chung Fan, Ting-Han Rudnicky, Alexander I. Ramadge, Peter J. Carnegie Mellon Univ Pittsburgh PA 15213 USA Princeton Univ Princeton NJ 08544 USA

ISBN: (纸本)9798891760615

Unlike recurrent models, conventional wisdom has it that Transformers cannot perfectly model regular languages. Inspired by the notion of working memory, we propose a new Transformer variant named RegularGPT. With its novel combination of Weight-Sharing, Adaptive-Depth, and Sliding-Dilated-Attention, RegularGPT constructs working memory along the depth dimension, thereby enabling efficient and successful modeling of regular languages such as PARITY. We further test RegularGPT on the task of natural language length extrapolation and surprisingly find that it rediscovers the local windowed attention effect deemed necessary in prior work for length extrapolation.

关键词： Extrapolation

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GAMA: A Large Audio-language Model with Advanced Audio Understanding and Complex Reasoning Abilities

GAMA: A Large Audio-Language Model with Advanced Audio Under...

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

作者： Ghosh, Sreyan Kumar, Sonal Seth, Ashish Evuru, Chandra Kiran Reddy Tyagi, Utkarsh Sakshi, S. Nieto, Oriol Duraiswami, Ramani Manocha, Dinesh University of Maryland College Park United States Adobe United States

ISBN: (纸本)9798891761643

Perceiving and understanding non-speech sounds and non-verbal speech is essential to making decisions that help us interact with our surroundings. In this paper, we propose GAMA, a novel General-purpose Large Audiolanguage Model (LALM) with Advanced Audio Understanding and Complex Reasoning Abilities. We build GAMA by integrating an LLM with multiple types of audio representations, including features from a custom Audio Q-Former, a multi-layer aggregator that aggregates features from multiple layers of an audio encoder. We fine-tune GAMA on a large-scale audio-language dataset, which augments it with audio understanding capabilities. Next, we propose CompA-R (Instruction-Tuning for Complex Audio Reasoning), a synthetically generated instruction-tuning (IT) dataset with instructions that require the model to perform complex reasoning on the input audio. We instruction-tune GAMA with CompA-R to endow it with complex reasoning abilities, where we further add a soft prompt as input with high-level semantic evidence by leveraging event tags of the input audio. Finally, we also propose CompA-R-test, a human-labeled evaluation dataset for evaluating the capabilities of LALMs on open-ended audio question-answering that requires complex reasoning. Through automated and expert human evaluations, we show that GAMA outperforms all other LALMs in literature on diverse audio understanding tasks by margins of 1%-84% and demonstrates state-of-the-art performance on deductive reasoning and hallucination evaluation benchmarks. Further, GAMA IT-ed on CompA-R proves to be superior in its complex reasoning capabilities. © 2024 Association for Computational Linguistics.

关键词： Semantics

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Large language Model as an Assignment Evaluator: Insights, Feedback, and Challenges in a 1000+ Student Course

Large Language Model as an Assignment Evaluator: Insights, F...

引用

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

作者： Chiang, Cheng-Han Chen, Wei-Chih Kuan, Chun-Yi Yang, Chienchou Lee, Hung-Yi National Taiwan University Taiwan Mediatek Inc. Taiwan

ISBN: (纸本)9798891761643

Using large language models (LLMs) for automatic evaluation has become an important evaluation method in NLP research. However, it is unclear whether these LLM-based evaluators can be applied in real-world classrooms to assess student assignments. This empirical report shares how we use GPT-4 as an automatic assignment evaluator in a university course with 1,028 students. Based on student responses, we find that LLM-based assignment evaluators are generally acceptable to students when students have free access to these LLM-based evaluators. However, students also noted that the LLM sometimes fails to adhere to the evaluation instructions. Additionally, we observe that students can easily manipulate the LLM-based evaluator to output specific strings, allowing them to achieve high scores without meeting the assignment rubric. Based on student feedback and our experience, we provide several recommendations for integrating LLM-based evaluators into future classrooms. Our observation also highlights potential directions for improving LLM-based evaluators, including their instruction-following ability and vulnerability to prompt hacking. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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ERVQA: A Dataset to Benchmark the Readiness of Large Vision language Models in Hospital Environments

ERVQA: A Dataset to Benchmark the Readiness of Large Vision ...

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

作者： Ray, Sourjyadip Gupta, Kushal Kundu, Soumi Kasat, Payal Arvind Aditya, Somak Goyal, Pawan Department of CSE Indian Institute of Technology Kharagpur India All India Institute of Medical Sciences Kalyani India All India Institute of Medical Sciences Deoghar India Dr. B.C. Roy Multi-Speciality Medical Research Centre IIT Kharagpur India

ISBN: (纸本)9798891761643

The global shortage of healthcare workers has demanded the development of smart healthcare assistants, which can help monitor and alert healthcare workers when necessary. We examine the healthcare knowledge of existing Large Vision language Models (LVLMs) via the Visual Question Answering (VQA) task in hospital settings through expert annotated open-ended questions. We introduce the Emergency Room Visual Question Answering (ERVQA) dataset, consisting of triplets covering diverse emergency room scenarios, a seminal benchmark for LVLMs. By developing a detailed error taxonomy and analyzing answer trends, we reveal the nuanced nature of the task. We benchmark state-of-the-art open-source and closed LVLMs using traditional and adapted VQA metrics: Entailment Score and CLIPScore Confidence. Analyzing errors across models, we infer trends based on properties like decoder type, model size, and in-context examples. Our findings suggest the ERVQA dataset presents a highly complex task, highlighting the need for specialized, domain-specific solutions. © 2024 Association for Computational Linguistics.

关键词： Visual languages

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H2O Open Ecosystem for State-of-the-art Large language Models

H2O Open Ecosystem for State-of-the-art Large Language Model...

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

作者： Candel, Arno McKinney, Jon Singer, Philipp Pfeiffer, Pascal Jeblick, Maximilian Lee, Chun Ming Conde, Marcos V. H2O.ai Inc. Mountain ViewCA United States

Large language Models (LLMs) represent a revolution in AI. However, they also pose many significant risks, such as the presence of biased, private, copyrighted or harmful text. For this reason we need open, transparent and safe solutions. We introduce a complete open-source ecosystem for developing and testing LLMs. The goal of this project is to boost open alternatives to closed-source approaches. We release h2oGPT, a family of fine-tuned LLMs from 7 to 70 Billion parameters. We also introduce H2O LLM Studio, a framework and no-code GUI designed for efficient fine-tuning, evaluation, and deployment of LLMs using the most recent state-of-the-art techniques. Our code and models are licensed under fully permissive Apache 2.0 licenses. We believe open-source language models help to boost AI development and make it more accessible and trustworthy. © 2023 Association for Computational Linguistics.

关键词： Ecosystems

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language and Mental Health: Measures of Emotion Dynamics from Text as Linguistic Biosocial Markers

Language and Mental Health: Measures of Emotion Dynamics fro...

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conference on empirical methods in natural language processing (EMNLP)

作者： Teodorescu, Daniela Cheng, Tiffany Fyshe, Alona Mohammad, Saif M. Univ Alberta Alberta Machine Intelligence Inst Amii Dept Comp Sci Edmonton AB Canada Ludwig Maximilians Univ Munchen Ctr Informat & Language Proc MaiNLP Munich Germany Carleton Univ Ottawa ON Canada Univ Alberta Dept Psychol Edmonton AB Canada Natl Res Council Canada Ottawa ON Canada Univ Alberta Edmonton AB Canada

ISBN: (纸本)9798891760608

Research in psychopathology has shown that, at an aggregate level, the patterns of emotional change over time-emotion dynamics-are indicators of one's mental health. One's patterns of emotion change have traditionally been determined through self-reports of emotions;however, there are known issues with accuracy, bias, and ease of data collection. Recent approaches to determining emotion dynamics from one's everyday utterances addresses many of these concerns, but it is not yet known whether these measures of utterance emotion dynamics (UED) correlate with mental health diagnoses. Here, for the first time, we study the relationship between tweet emotion dynamics and mental health disorders. We find that each of the UED metrics studied varied by the user's self-disclosed diagnosis. For example: average valence was significantly higher (i.e., more positive text) in the control group compared to users with ADHD, MDD, and PTSD. Valence variability was significantly lower in the control group compared to ADHD, depression, bipolar disorder, MDD, PTSD, and OCD but not PPD. Rise and recovery rates of valence also exhibited significant differences from the control. This work provides important early evidence for how linguistic cues pertaining to emotion dynamics can play a crucial role as biosocial markers for mental illnesses and aid in the understanding, diagnosis, and management of mental health disorders.

关键词： Dynamics

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