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

共 15258 条记录，以下是581-590 订阅

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Tending Towards Stability: Convergence Challenges in Small language Models

Tending Towards Stability: Convergence Challenges in Small L...

引用

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

作者： Martinez, Richard Diehl Lesci, Pietro Buttery, Paula University of Cambridge United Kingdom

ISBN: (纸本)9798891761681

Increasing the number of parameters in language models is a common strategy to enhance their performance. However, smaller language models remain valuable due to their lower operational costs. Despite their advantages, smaller models frequently underperform compared to their larger counterparts, even when provided with equivalent data and computational resources. Specifically, their performance tends to degrade in the late pretraining phase. This is anecdotally attributed to their reduced representational capacity. Yet, the exact causes of this performance degradation remain unclear. We use the Pythia model suite to analyse the training dynamics that underlie this phenomenon. Across different model sizes, we investigate the convergence of the Attention and MLP activations to their final state and examine how the effective rank of their parameters influences this process. We find that nearly all layers in larger models stabilise early in training-within the first 20%-whereas layers in smaller models exhibit slower and less stable convergence, especially when their parameters have lower effective rank. By linking the convergence of layers' activations to their parameters' effective rank, our analyses can guide future work to address inefficiencies in the learning dynamics of small models. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Learning the Visualness of Text Using Large Vision-language Models

Learning the Visualness of Text Using Large Vision-Language ...

引用

conference on empirical methods in natural language processing (EMNLP)

作者： Verma, Gaurav Rossi, Ryan A. Tensmeyer, Christopher Gu, Jiuxiang Nenkova, Ani Georgia Inst Technol Atlanta GA 30332 USA Adobe Res San Jose CA 95110 USA

ISBN: (纸本)9798891760608

Visual text evokes an image in a person's mind, while non-visual text fails to do so. A method to automatically detect visualness in text will enable text-to-image retrieval and generation models to augment text with relevant images. This is particularly challenging with long-form text as text-to-image generation and retrieval models are often triggered for text that is designed to be explicitly visual in nature, whereas long-form text could contain many non-visual sentences. To this end, we curate a dataset of 3,620 English sentences and their visualness scores provided by multiple human annotators. We also propose a fine-tuning strategy that adapts large vision-language models like CLIP by modifying the model's contrastive learning objective to map text identified as non-visual to a common NULL image while matching visual text to their corresponding images in the document. We evaluate the proposed approach on its ability to (i) classify visual and non-visual text accurately, and (ii) attend over words that are identified as visual in psycholinguistic studies. empirical evaluation indicates that our approach performs better than several heuristics and baseline models for the proposed task. Furthermore, to highlight the importance of modeling the visualness of text, we conduct qualitative analyses of text-to-image generation systems like DALL-E.

关键词： Visual languages

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Do LLMs Overcome Shortcut Learning? An Evaluation of Shortcut Challenges in Large language Models

Do LLMs Overcome Shortcut Learning? An Evaluation of Shortcu...

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

作者： Yuan, Yu Zhao, Lili Zhang, Kai Zheng, Guangting Liu, Qi State Key Lab of Cognitive Intelligence University of Science and Technology of China China School of Computer Science and Technology University of Science and Technology of China China

ISBN: (纸本)9798891761643

Large language Models (LLMs) have shown remarkable capabilities in various natural language processing tasks. However, LLMs may rely on dataset biases as shortcuts for prediction, which can significantly impair their robustness and generalization capabilities. This paper presents Shortcut Suite, a comprehensive test suite designed to evaluate the impact of shortcuts on LLMs' performance, incorporating six shortcut types, five evaluation metrics, and four prompting strategies. Our extensive experiments yield several key findings: 1) LLMs demonstrate varying reliance on shortcuts for downstream tasks, significantly impairing their performance. 2) Larger LLMs are more likely to utilize shortcuts under zero-shot and few-shot in-context learning prompts. 3) Chain-of-thought prompting notably reduces shortcut reliance and outperforms other prompting strategies, while few-shot prompts generally underperform compared to zero-shot prompts. 4) LLMs often exhibit overconfidence in their predictions, especially when dealing with datasets that contain shortcuts. 5) LLMs generally have a lower explanation quality in shortcut-laden datasets, with errors falling into three types: distraction, disguised comprehension, and logical fallacy. Our findings offer new insights for evaluating robustness and generalization in LLMs and suggest potential directions for mitigating the reliance on shortcuts. The code is available at https://***/yyhappier/***. © 2024 Association for Computational Linguistics.

关键词： Zero-shot learning

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Consistent Bidirectional language Modelling: Expressive Power and Representational Conciseness

Consistent Bidirectional Language Modelling: Expressive Powe...

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

作者： Shopov, Georgi Gerdjikov, Stefan IICT Bulgarian Academy of Sciences Bulgaria FMI Sofia University Bulgaria

ISBN: (纸本)9798891761643

The inability to utilise future contexts and the pre-determined left-to-right generation order are major limitations of unidirectional language models. Bidirectionality has been introduced to address those deficiencies. However, a crucial shortcoming of bidirectional language models is the potential inconsistency of their conditional distributions. This fundamental flaw greatly diminishes their applicability and hinders their capability of tractable sampling and likelihood computation. In this work, we introduce a class of bidirectional language models, called latent language models, that are consistent by definition and can be efficiently used both for generation and scoring of sequences. We define latent language models based on the well-understood formalism of bisequential decompositions from automata theory. This formal correspondence allows us to precisely charaterise the abilities and limitations of a subclass of latent language models, called rational language models. As a result, we obtain that latent language models are exponentially more concise and significantly more expressive than unidirectional language models. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Enhancing Tool Retrieval with Iterative Feedback from Large language Models

Enhancing Tool Retrieval with Iterative Feedback from Large ...

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

作者： Xu, Qiancheng Li, Yongqi Xia, Heming Li, Wenjie Department of Computing The Hong Kong Polytechnic University China

ISBN: (纸本)9798891761681

Tool learning aims to enhance and expand large language models' (LLMs) capabilities with external tools, which has gained significant attention *** methods have shown that LLMs can effectively handle a certain amount of tools through in-context learning or ***, in real-world scenarios, the number of tools is typically extensive and irregularly updated, emphasizing the necessity for a dedicated tool retrieval *** retrieval is nontrivial due to the following challenges: 1) complex user instructions and tool descriptions;2) misalignment between tool retrieval and tool usage *** address the above issues, we propose to enhance tool retrieval with iterative feedback from the large language ***, we prompt the tool usage model, i.e., the LLM, to provide feedback for the tool retriever model in multiround, which could progressively improve the tool retriever's understanding of instructions and tools and reduce the gap between the two standalone *** build a unified and comprehensive benchmark to evaluate tool retrieval *** extensive experiments indicate that our proposed approach achieves advanced performance in both in-domain evaluation and out-of-domain evaluation. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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METAREFLECTION: Learning Instructions for language Agents using Past Reflections

METAREFLECTION: Learning Instructions for Language Agents us...

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

作者： Gupta, Priyanshu Kirtania, Shashank Singha, Ananya Gulwani, Sumit Radhakrishna, Arjun Shi, Sherry Soares, Gustavo Microsoft United States

ISBN: (纸本)9798891761643

The popularity of Large language Models (LLMs) have unleashed a new age of language Agents for solving a diverse range of tasks. While contemporary frontier LLMs are capable enough to power reasonably good language agents, the closed-API model makes it hard to improve in cases they perform sub-optimally. To address this, recent works have explored ways to improve their performance using techniques like self-reflection and prompt optimization. Unfortunately, techniques like self-reflection can be used only in an online setup, while contemporary prompt optimization techniques are designed and tested to work on simple tasks. To this end, we introduce METAREFLECTION, a novel offline reinforcement learning technique that enhances the performance of language Agents by augmenting a semantic memory based on experiential learnings from past trials. We demonstrate the efficacy of METAREFLECTION by evaluating across multiple domains, including complex logical reasoning, biomedical semantic similarity, open world question answering, and vulnerability threat detection, in Infrastructure-as-Code, spanning different agent designs. METAREFLECTION boosts language agents' performance by 4 % to 16.82 % over the raw GPT-4 baseline and performs on par with existing state-of-the-art prompt optimization techniques while requiring fewer LLM calls. We release our experimental code at: ***/metareflection-code. © 2024 Association for Computational Linguistics.

关键词： Semantics

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Solving for X and Beyond: Can Large language Models Solve Complex Math Problems with More-Than-Two Unknowns?

Solving for X and Beyond: Can Large Language Models Solve Co...

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

作者： Kao, Kuei-Chun Wang, Ruochen Hsieh, Cho-Jui Department of Computer Science University of California Los Angeles United States

ISBN: (纸本)9798891761681

Large language Models (LLMs) have demonstrated remarkable performance in solving math problems, a hallmark of human intelligence. Despite high success rates on current benchmarks;however, these often feature simple problems with only one or two unknowns, which do not sufficiently challenge their reasoning capacities. This paper introduces a novel benchmark, BeyondX, designed to address these limitations by incorporating problems with multiple unknowns. Recognizing the challenges in proposing multi-unknown problems from scratch, we developed BeyondX using an innovative automated pipeline that progressively increases complexity by expanding the number of unknowns in simpler problems. empirical study on BeyondX reveals that the performance of existing LLMs, even those fine-tuned specifically on math tasks, significantly decreases as the number of unknowns increases - with a performance drop of up to 70% observed in GPT-4. To tackle these challenges, we propose the Formulate-and-Solve strategy, a generalized prompting approach that effectively handles problems with an arbitrary number of unknowns. Our findings reveal that this strategy not only enhances LLM performance on the BeyondX benchmark but also provides deeper insights into the computational limits of LLMs when faced with more complex mathematical challenges. © 2024 Association for Computational Linguistics.

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Dynamic Rewarding with Prompt Optimization Enables Tuning-free Self-Alignment of language Models

Dynamic Rewarding with Prompt Optimization Enables Tuning-fr...

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

作者： Singla, Somanshu Wang, Zhen Liu, Tianyang Ashfaq, Abdullah Hu, Zhiting Xing, Eric P. UC San Diego United States MBZUAI United Arab Emirates CMU United States

ISBN: (纸本)9798891761643

Aligning Large language Models (LLMs) traditionally relies on costly training and human preference annotations. Self-alignment aims to reduce these expenses by aligning models by themselves. To further minimize the cost and enable LLM alignment without any expensive tuning and annotations, we introduce a new tuning-free approach for self-alignment, called Dynamic Rewarding with Prompt Optimization (DRPO). Our approach leverages a search-based optimization framework that allows LLMs to iteratively self-improve and design the best alignment instructions without the need for additional training or human intervention. The core of DRPO is a dynamic rewarding mechanism, which identifies and rectifies model-specific alignment weaknesses, allowing LLMs to adapt efficiently to diverse alignment challenges. empirical evaluations on eight recent LLMs, both open- and closed-source, reveal that DRPO significantly enhances alignment performance, with base models outperforming their SFT/RLHF-tuned counterparts. Moreover, DRPO's automatically optimized prompts surpass those curated by human experts, further validating the effectiveness of our approach. Our findings highlight the great potential of current LLMs to be adaptively self-aligned through inference-time optimization, complementing existing tuning-based alignment research. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Improving Zero-shot LLM Re-Ranker with Risk Minimization

Improving Zero-shot LLM Re-Ranker with Risk Minimization

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

作者： Yuan, Xiaowei Yang, Zhao Wang, Yequan Zhao, Jun Liu, Kang The Key Laboratory of Cognition and Decision Intelligence for Complex Systems Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China Beijing Academy of Artificial Intelligence Beijing China

ISBN: (纸本)9798891761643

In the Retrieval-Augmented Generation (RAG) system, advanced Large language Models (LLMs) have emerged as effective Query Likelihood Models (QLMs) in an unsupervised way, which re-rank documents based on the probability of generating the query given the content of a document. However, directly prompting LLMs to approximate QLMs inherently is biased, where the estimated distribution might diverge from the actual document-specific distribution. In this study, we introduce a novel framework, UR3, which leverages Bayesian decision theory to both quantify and mitigate this estimation bias. Specifically, UR3 reformulates the problem as maximizing the probability of document generation, thereby harmonizing the optimization of query and document generation probabilities under a unified risk minimization objective. Our empirical results indicate that UR3 significantly enhances re-ranking, particularly in improving the Top-1 accuracy. It benefits the QA tasks by achieving higher accuracy with fewer input documents. © 2024 Association for Computational Linguistics.

关键词： Structured Query language

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From Local Concepts to Universals: Evaluating the Multicultural Understanding of Vision-language Models

From Local Concepts to Universals: Evaluating the Multicultu...

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

作者： Bhatia, Mehar Ravi, Sahithya Chinchure, Aditya Hwang, Eunjeong Shwartz, Vered University of British Columbia Vector Institute for AI Canada

ISBN: (纸本)9798891761643

Despite recent advancements in vision-language models, their performance remains suboptimal on images from non-western cultures, due to underrepresentation in training datasets. Various benchmarks have been proposed to test models' cultural inclusivity, but they have limited coverage of cultures and do not adequately assess cultural diversity across universal as well as culture-specific local concepts. To address these limitations, we introduce the GLOBALRG benchmark, comprising two challenging tasks: retrieval across universals and cultural visual grounding. The former task entails retrieving culturally-diverse images for universal concepts from 50 countries, while the latter aims at grounding culture-specific concepts within images from 15 countries. Our evaluation across a wide range of models reveals that the performance varies significantly across cultures - underscoring the necessity for enhancing multicultural understanding in vision-language models. Our data and code can be found at https://***/. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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