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

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Making Large language Models Better Reasoners with Orchestrated Streaming Experiences

Making Large Language Models Better Reasoners with Orchestra...

引用

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

作者： Liu, Xiangyang He, Junliang Qiu, Xipeng School of Computer Science Fudan University Shanghai Collaborative Innovation Center of Intelligent Visual Computing China

ISBN: (纸本)9798891761643

Large language models (LLMs) can perform complex reasoning by generating intermediate thoughts under zero-shot or few-shot settings. However, zero-shot prompting always encounters low performance, and the superior performance of few-shot prompting hinges on the manual-crafted demonstrations. In this paper, we present RoSE (Reasoning with Orchestrated Streaming Experiences), a general framework for solving reasoning tasks that can self-improve without complex external efforts. To enable RoSE, we describe an architecture that extends an LLM to store all answered questions and their thoughts in a streaming experience pool then orchestrates helpful questions from the pool to assist in answering new questions. To set up a question-aware orchestration mechanism, RoSE first calculates the similarity of each question in the pool with a new test question. Since the solution to each answered question is not always correct, RoSE will sort the questions according to their similarity with the new question, and then uniformly divide them into multiple buckets. It finally extracts one question from each bucket to make these extracted questions more diverse. To make these extracted questions help RoSE answer new questions as much as possible, we introduce two other attributes of uncertainty and complexity for each question. RoSE will preferentially select the questions with low uncertainty and high complexity from each bucket. We evaluate the versatility of RoSE in various reasoning tasks, LLMs, and CoT methods. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

来源：评论

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Exploring Chain of Thought Style Prompting for Text-to-SQL

Exploring Chain of Thought Style Prompting for Text-to-SQL

引用

conference on empirical methods in natural language processing (EMNLP)

作者： Tai, Chang-You Chen, Ziru Zhang, Tianshu Deng, Xiang Sun, Huan Ohio State Univ Columbus OH 43210 USA

ISBN: (纸本)9798891760608

In-context learning with large language models (LLMs) has recently caught increasing attention due to its superior few-shot performance on various tasks. However, its performance on text-to-SQL parsing still has much room for improvement. In this paper, we hypothesize that a crucial aspect of LLMs to improve for text-to-SQL parsing is their multi-step reasoning ability. Thus, we systematically study how to enhance LLMs' reasoning ability through chain of thought (CoT) style prompting, including the original chain-of-thought prompting (Wei et al., 2022b) and least-to-most prompting (Zhou et al., 2023). Our experiments demonstrate that iterative prompting as in Zhou et al. (2023) may be unnecessary for text-to-SQL parsing, and using detailed reasoning steps tends to have more error propagation issues. Based on these findings, we propose a new CoT-style prompting method for text-to-SQL parsing. It brings 5.2 and 6.5 point absolute gains on the Spider development set and the Spider Realistic set, respectively, compared to the standard prompting method without reasoning steps;2.4 and 1.5 point absolute gains, compared to the least-to-most prompting method(1).

关键词： Iterative methods

来源：评论

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From Complex to Simple: Enhancing Multi-Constraint Complex Instruction Following Ability of Large language Models

From Complex to Simple: Enhancing Multi-Constraint Complex I...

引用

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

作者： He, Qianyu Zeng, Jie He, Qianxi Liang, Jiaqing Xiao, Yanghua Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China School of Data Science Fudan University China

ISBN: (纸本)9798891761681

It is imperative for Large language models (LLMs) to follow instructions with elaborate requirements (i.e. Complex Instructions Following). Yet, it remains under-explored how to enhance the ability of LLMs to follow complex instructions with multiple constraints. To bridge the gap, we initially study what training data is effective in enhancing complex constraints following abilities. We found that training LLMs with instructions containing multiple constraints enhances their understanding of complex instructions, especially those with lower complexity levels. Additionally, we further propose methods addressing how to obtain and utilize the effective training data. Finally, we conduct extensive experiments to prove the effectiveness of our methods in terms of overall performance and training efficiency. We also demonstrate that our methods improve models' ability to follow instructions generally and generalize effectively across out-of-domain, in-domain, and adversarial settings, while maintaining general capabilities. The datasets and code are publicly available at https://***/meowpass/FollowComplexInstruction. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Unveiling Factual Recall Behaviors of Large language Models through Knowledge Neurons

Unveiling Factual Recall Behaviors of Large Language Models ...

引用

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

作者： Wang, Yifei Chen, Yuheng Wen, Wanting Sheng, Yu Li, Linjing Zeng, Daniel State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Beijing Wenge Technology Co. Ltd. Beijing China

ISBN: (纸本)9798891761643

In this paper, we investigate whether Large language Models (LLMs) actively recall or retrieve their internal repositories of factual knowledge when faced with reasoning tasks. Through an analysis of LLMs' internal factual recall at each reasoning step via Knowledge Neurons, we reveal that LLMs fail to harness the critical factual associations under certain circumstances. Instead, they tend to opt for alternative, shortcut-like pathways to answer reasoning questions. By manually manipulating the recall process of parametric knowledge in LLMs, we demonstrate that enhancing this recall process directly improves reasoning performance whereas suppressing it leads to notable degradation. Furthermore, we assess the effect of Chain-of-Thought (CoT) prompting, a powerful technique for addressing complex reasoning tasks. Our findings indicate that CoT can intensify the recall of factual knowledge by encouraging LLMs to engage in orderly and reliable reasoning. Furthermore, we explored how contextual conflicts affect the retrieval of facts during the reasoning process to gain a comprehensive understanding of the factual recall behaviors of LLMs. © 2024 Association for Computational Linguistics.

关键词： Neurons

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CONTESTS: a Framework for Consistency Testing of Span Probabilities in language Models

CONTESTS: a Framework for Consistency Testing of Span Probab...

引用

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

作者： Wagner, Eitan Slavutsky, Yuli Abend, Omri School of Computer Science and Engineering Department of Statistics and Data Science Hebrew University of Jerusalem Israel

ISBN: (纸本)9798891761643

Although language model scores are often treated as probabilities, their reliability as probability estimators has mainly been studied through calibration, overlooking other aspects. In particular, it is unclear whether language models produce the same value for different ways of assigning joint probabilities to word spans. Our work introduces a novel framework, ConTestS (Consistency Testing over Spans), involving statistical tests to assess score consistency across interchangeable completion and conditioning orders. We conduct experiments on post-release real and synthetic data to eliminate training effects. Our findings reveal that both Masked language Models (MLMs) and autoregressive models exhibit inconsistent predictions, with autoregressive models showing larger discrepancies. Larger MLMs tend to produce more consistent predictions, while autoregressive models show the opposite trend. Moreover, for both model types, prediction entropies offer insights into the true word span likelihood and therefore can aid in selecting optimal decoding strategies. The inconsistencies revealed by our analysis, as well their connection to prediction entropies and differences between model types, can serve as useful guides for future research on addressing these limitations. © 2024 Association for Computational Linguistics.

关键词： Prediction models

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The empirical Variability of Narrative Perceptions of Social Media Texts

The Empirical Variability of Narrative Perceptions of Social...

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

作者： Mire, Joel Antoniak, Maria Ash, Elliott Piper, Andrew Sap, Maarten Carnegie Mellon University United States University of Copenhagen Denmark ETH Zürich Switzerland McGill University Canada Allen Institute for AI United States

ISBN: (纸本)9798891761643

Most NLP work on narrative detection has focused on prescriptive definitions of stories crafted by researchers, leaving open the questions: how do crowd workers perceive texts to be a story, and why? We investigate this by building STORYPERCEPTIONS, a dataset of 2,496 perceptions of storytelling in 502 social media texts from 255 crowd workers, including categorical labels along with free-text storytelling rationales, authorial intent, and *** construct a fine-grained bottom-up taxonomy of crowd workers' varied and nuanced perceptions of storytelling by open-coding their free-text *** comparative analyses at the label and code level, we illuminate patterns of disagreement among crowd workers and across other annotation contexts, including prescriptive labeling from researchers and LLM-based ***, plot complexity, references to generalized or abstract actions, and holistic aesthetic judgments (such as a sense of cohesion) are especially important in *** empirical findings broaden understanding of the types, relative importance, and contentiousness of features relevant to narrative detection, highlighting opportunities for future work on reader-contextualized models of narrative reception. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Hateful Word in Context Classification

Hateful Word in Context Classification

引用

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

作者： Hoeken, Sanne Zarrieß, Sina Alaçam, Özge Department of Linguistics Bielefeld University Germany Center for Information and Language Processing LMU Munich Germany

ISBN: (纸本)9798891761643

Hate speech detection is a prevalent research field, yet it remains underexplored at the level of word meaning. This is significant, as terms used to convey hate often involve non-standard or novel usages which might be overlooked by commonly leveraged LMs trained on general language use. In this paper, we introduce the Hateful Word in Context Classification (HateWiC) task and present a dataset of ∼4000 WiC-instances, each labeled by three annotators. Our analyses and computational exploration focus on the interplay between the subjective nature (context-dependent connotations) and the descriptive nature (as described in dictionary definitions) of hateful word senses. HateWiC annotations confirm that hatefulness of a word in context does not always derive from the sense definition alone. We explore the prediction of both majority and individual annotator labels, and we experiment with modeling context- and sense-based inputs. Our findings indicate that including definitions proves effective overall, yet not in cases where hateful connotations vary. Conversely, including annotator demographics becomes more important for mitigating performance drop in subjective hate prediction. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Can language Models Induce Grammatical Knowledge from Indirect Evidence?

Can Language Models Induce Grammatical Knowledge from Indire...

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

作者： Oba, Miyu Oseki, Yohei Fukatsu, Akiyo Haga, Akari Ouchi, Hiroki Watanabe, Taro Sugawara, Saku Nara Institute of Science and Technology Japan The University of Tokyo Japan National Institute of Informatics Japan

ISBN: (纸本)9798891761643

What kinds of and how much data is necessary for language models to induce grammatical knowledge to judge sentence acceptability? Recent language models still have much room for improvement in their data efficiency compared to *** paper investigates whether language models efficiently use indirect data (indirect evidence), from which they infer sentence *** contrast, humans use indirect evidence efficiently, which is considered one of the inductive biases contributing to efficient language *** explore this question, we introduce the Wug InDirect Evidence Test (WIDET), a dataset consisting of training instances inserted into the pre-training data and evaluation *** inject synthetic instances with newly coined wug words into pretraining data and explore the model's behavior on evaluation data that assesses grammatical acceptability regarding those *** prepare the injected instances by varying their levels of indirectness and *** experiments surprisingly show that language models do not induce grammatical knowledge even after repeated exposure to instances with the same structure but differing only in lexical items from evaluation instances in certain language *** findings suggest a potential direction for future research: developing models that use latent indirect evidence to induce grammatical knowledge. © 2024 Association for Computational Linguistics.

关键词： Data assimilation

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ToViLaG: Your Visual-language Generative Model is Also An Evildoer

ToViLaG: Your Visual-Language Generative Model is Also An Ev...

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conference on empirical methods in natural language processing (EMNLP)

作者： Wang, Xinpeng Yi, Xiaoyuan Jiang, Han Zhou, Shanlin Wei, Zhihua Xie, Xing Tongji Univ Dept Comp Sci & Technol Shanghai Peoples R China Microsoft Res Asia Redmond WA USA

ISBN: (纸本)9798891760608

Warning: this paper includes model outputs showing offensive content. Recent large-scale Visual-language Generative Models (VLGMs) have achieved unprecedented improvement in multimodal image/text generation. However, these models might also generate toxic content, e.g., offensive text and pornography images, raising significant ethical risks. Despite exhaustive studies on toxic degeneration of language models, this problem remains largely unexplored within the context of visual-language generation. This work delves into the propensity for toxicity generation and susceptibility to toxic data across various VLGMs. For this purpose, we built ToViLaG, a dataset comprising 32K co-toxic/mono-toxic text-image pairs and 1K innocuous but evocative text that tends to stimulate toxicity. Furthermore, we propose WInToRe, a novel toxicity metric tailored to visual-language generation, which theoretically reflects different aspects of toxicity considering both input and output. On such a basis, we benchmarked the toxicity of a diverse spectrum of VLGMs and discovered that some models do more evil than expected while some are more vulnerable to infection, underscoring the necessity of VLGMs detoxification. Therefore, we develop an innovative information bottleneckbased detoxification method. Our method reduces toxicity while maintaining acceptable generation quality, providing a promising initial solution to this line of research.

关键词： Visual languages

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Counting the Bugs in ChatGPT's Wugs: A Multilingual Investigation into the Morphological Capabilities of a Large language Model

Counting the Bugs in ChatGPT's Wugs: A Multilingual Investig...

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conference on empirical methods in natural language processing (EMNLP)

作者： Weissweiler, Leonie Hofmann, Valentin Kantharuban, Anjali Cai, Anna Dutt, Ritam Hengle, Amey Kabra, Anubha Kulkarni, Atharva Vijayakumar, Abhishek Yu, Haofei Schuetze, Hinrich Oflazer, Kemal Mortensen, David R. Carnegie Mellon Univ Pittsburgh PA 15213 USA Ludwig Maximilians Univ Munchen Munich Germany Univ Oxford Oxford England Munich Ctr Machine Learning Munich Germany Allen Inst AI Seattle WA USA IIT Delhi Delhi India

ISBN: (纸本)9798891760608

Large language models (LLMs) have recently reached an impressive level of linguistic capability, prompting comparisons with human language skills. However, there have been relatively few systematic inquiries into the linguistic capabilities of the latest generation of LLMs, and those studies that do exist (i) ignore the remarkable ability of humans to generalize, (ii) focus only on English, and (iii) investigate syntax or semantics and overlook other capabilities that lie at the heart of human language, like morphology. Here, we close these gaps by conducting the first rigorous analysis of the morphological capabilities of ChatGPT in four typologically varied languages (specifically, English, German, Tamil, and Turkish). We apply a version of Berko's (1958) wug test to ChatGPT, using novel, uncontaminated datasets for the four examined languages. We find that ChatGPT massively underperforms purpose-built systems, particularly in English. Overall, our results-through the lens of morphology-cast a new light on the linguistic capabilities of ChatGPT, suggesting that claims of human-like language skills are premature and misleading.

关键词： Morphology

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