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

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Closing the Loop: Learning to Generate Writing Feedback via language Model Simulated Student Revisions

Closing the Loop: Learning to Generate Writing Feedback via ...

引用

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

作者： Nair, Inderjeet Tan, Jiaye Su, Xiaotian Gere, Anne Wang, Xu Wang, Lu University of Michigan United States ETH Zürich Switzerland

ISBN: (纸本)9798891761643

Providing feedback is widely recognized as crucial for refining students' writing skills. Recent advances in language models (LMs) have made it possible to automatically generate feedback that is actionable and well-aligned with human-specified attributes. However, it remains unclear whether the feedback generated by these models is truly effective in enhancing the quality of student revisions. Moreover, prompting LMs with a precise set of instructions to generate feedback is nontrivial due to the lack of consensus regarding the specific attributes that can lead to improved revising performance. To address these challenges, we propose PROF that PROduces Feedback via learning from LM simulated student revisions. PROF aims to iteratively optimize the feedback generator by directly maximizing the effectiveness of students' overall revising performance as simulated by LMs. Focusing on an economic essay assignment, we empirically test the efficacy of PROF and observe that our approach not only surpasses a variety of baseline methods in effectiveness of improving students' writing but also demonstrates enhanced pedagogical values, even though it was not explicitly trained for this aspect. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Multi-expert Prompting Improves Reliability, Safety and Usefulness of Large language Models

Multi-expert Prompting Improves Reliability, Safety and Usef...

引用

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

作者： Long, Do Xuan Yen, Duong Ngoc Tuan, Luu Anh Kawaguchi, Kenji Kan, Min-Yen Chen, Nancy F. National University of Singapore Singapore A*STAR Singapore Nanyang Technological University Singapore

ISBN: (纸本)9798891761643

We present Multi-expert Prompting, a novel enhancement of ExpertPrompting (Xu et al., 2023), designed to improve the large language model (LLM) ***, it guides an LLM to fulfill an input instruction by simulating multiple experts, aggregating their responses, and selecting the best among individual and aggregated *** process is performed in a single chain of thoughts through our seven carefully designed subtasks derived from the Nominal Group Technique (Ven and Delbecq, 1974), a well-established decision-making *** evaluations demonstrate that Multi-expert Prompting significantly outperforms ExpertPrompting and comparable baselines in enhancing the truthfulness, factuality, informativeness, and usefulness of responses while reducing toxicity and *** further achieves state-ofthe-art truthfulness by outperforming the best baseline by 8.69% with ***-expert Prompting is efficient, explainable, and highly adaptable to diverse scenarios, eliminating the need for manual prompt construction. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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MolCA: Molecular Graph-language Modeling with Cross-Modal Projector and Uni-Modal Adapter

MolCA: Molecular Graph-Language Modeling with Cross-Modal Pr...

引用

conference on empirical methods in natural language processing (EMNLP)

作者： Liu, Zhiyuan Li, Sihang Luo, Yanchen Fei, Hao Cao, Yixin Kawaguchi, Kenji Wang, Xiang Chua, Tat-Seng Natl Univ Singapore Singapore Singapore Univ Sci & Technol China Hefei Singapore Singapore Management Univ Singapore Singapore Hefei Comprehens Natl Sci Ctr Inst Dataspace Inst Artificial Intelligence Hefei Singapore

ISBN: (纸本)9798891760608

language Models (LMs) have demonstrated impressive molecule understanding ability on various 1D text-related tasks. However, they inherently lack 2D graph perception- a critical ability of human professionals in comprehending molecules' topological structures. To bridge this gap, we propose MolCA: Molecular Graph-language Modeling with Cross-Modal Projector and Uni-Modal Adapter. MolCA enables an LM (i.e., Galactica) to understand both text- and graph-based molecular contents via the cross-modal projector. Specifically, the cross-modal projector is implemented as a Q-Former to connect a graph encoder's representation space and an LM's text space. Further, MolCA employs a uni-modal adapter (i.e., LoRA) for the LM's efficient adaptation to downstream tasks. Unlike previous studies that couple an LM with a graph encoder via cross-modal contrastive learning, MolCA retains the LM's ability of open-ended text generation and augments it with 2D graph information. To showcase its effectiveness, we extensively benchmark MolCA on tasks of molecule captioning, IUPAC name prediction, and molecule-text retrieval, on which MolCA significantly outperforms the baselines. Our codes and checkpoints can be found at https: //***/acharkq/MolCA.

关键词： Molecules

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Novel Ensemble Sentiment Classification through Speech processing and Stacking Generalization 1

Novel Ensemble Sentiment Classification through Speech Proce...

引用

1st International conference on Electronics, Communication and Signal processing, ICECSP 2024

作者： Chowdhury, Shriya Roy, Soumo John, Mathew Chathurvedi, V Rama Chandra Das, Deepanjali Mohanty, Aparna School of Electronics Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350364590

This paper introduces an innovative method for speech sentiment analysis, by employing a stacking classifier. The proposed system directly transcribes audio and extracts essential features for sentiment analysis. The stacking classifier, which combines multiple classifiers, enhances predictive performance. Intermediate models include K-Nearest Neighbors (k-NN), Random Forest, SVM, Gaussian Naive Bayes, and Logistic Regression. OpenAI Whisper transformer is used for audio-to-text conversion. The methodology encompasses audio data preprocessing, text conversion, natural language processing, and model training. The model is evaluated on accuracy, recall, precision, and F1-score. We examine the effectiveness of various classifiers and feature sets in sentiment analysis of speech data through empirical investigation. The experimental results demonstrate the system's ability to accurately classify sentiment and provide valuable insights into sentiment analysis methods, tailored for spoken language. © 2024 IEEE.

关键词： natural language processing systems

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Information Flow Routes: Automatically Interpreting language Models at Scale

Information Flow Routes: Automatically Interpreting Language...

引用

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

作者： Ferrando, Javier Voita, Elena Universitat Politècnica de Catalunya Spain Meta AI United States

ISBN: (纸本)9798891761643

Information flows by routes inside the network via mechanisms implemented in the model. These routes can be represented as graphs where nodes correspond to token representations and edges to computations. We automatically build these graphs in a top-down manner, for each prediction leaving only the most important nodes and edges. In contrast to the existing workflows relying on activation patching, we do this through attribution: this allows us to efficiently uncover existing circuits with just a single forward pass. Unlike with patching, we do not need a human to carefully design prediction templates, and we can extract information flow routes for any prediction (not just the ones among the allowed templates). As a result, we can analyze model behavior in general, for specific types of predictions, or different domains. We experiment with Llama 2 and show that some attention head roles are overall important, e.g. previous token heads and subword merging heads. Next, we find similarities in Llama 2 behavior when handling tokens of the same part of speech. Finally, we show that some model components can be specialized on domains such as coding or multilingual texts. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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A Systematic Analysis of Large language Models as Soft Reasoners: The Case of Syllogistic Inferences

A Systematic Analysis of Large Language Models as Soft Reaso...

引用

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

作者： Bertolazzi, Leonardo Gatt, Albert Bernardi, Raffaella DISI University of Trento Italy ICS Utrecht University Netherlands CIMeC and DISI University of Trento Italy

ISBN: (纸本)9798891761643

The reasoning abilities of Large language Models (LLMs) are becoming a central focus of study in NLP. In this paper, we consider the case of syllogistic reasoning, an area of deductive reasoning studied extensively in logic and cognitive psychology. Previous research has shown that pre-trained LLMs exhibit reasoning biases, such as content effects, avoid answering that no conclusion follows, display human-like difficulties, and struggle with multi-step reasoning. We contribute to this research line by systematically investigating the effects of chain-of-thought reasoning, in-context learning (ICL), and supervised fine-tuning (SFT) on syllogistic reasoning, considering syllogisms with conclusions that support or violate world knowledge, as well as ones with multiple premises. Crucially, we go beyond the standard focus on accuracy, with an in-depth analysis of the conclusions generated by the models. Our results suggest that the behavior of pre-trained LLMs can be explained by heuristics studied in cognitive science and that both ICL and SFT improve model performance on valid inferences, although only the latter mitigates most reasoning biases without harming model consistency. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Evaluating Psychological Safety of Large language Models

Evaluating Psychological Safety of Large Language Models

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

作者： Li, Xingxuan Li, Yutong Qiu, Lin Joty, Shafiq Bing, Lidong DAMO Academy Alibaba Group Singapore College of Computing and Data Science NTU Singapore School of Social Sciences NTU Singapore Department of Psychology CUHK Hong Kong Salesforce Research Singapore Hupan Lab Hangzhou310023 China

ISBN: (纸本)9798891761643

In this work, we designed unbiased prompts to systematically evaluate the psychological safety of large language models (LLMs). First, we tested five different LLMs by using two personality tests: Short Dark Triad (SD-3) and Big Five Inventory (BFI). All models scored higher than the human average on SD-3, suggesting a relatively darker personality pattern. Despite being instruction fine-tuned with safety metrics to reduce toxicity, InstructGPT, GPT-3.5, and GPT-4 still showed dark personality patterns;these models scored higher than self-supervised GPT-3 on the Machiavellianism and narcissism traits on SD-3. Then, we evaluated the LLMs in the GPT series by using well-being tests to study the impact of fine-tuning with more training data. We observed a continuous increase in the well-being scores of GPT models. Following these observations, we showed that fine-tuning Llama-2-chat-7B with responses from BFI using direct preference optimization could effectively reduce the psychological toxicity of the model. Based on the findings, we recommended the application of systematic and comprehensive psychological metrics to further evaluate and improve the safety of LLMs. Our code is available at https://***/DAMONLP-SG/PsychSafety. © 2024 Association for Computational Linguistics.

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APPLS: Evaluating Evaluation Metrics for Plain language Summarization

APPLS: Evaluating Evaluation Metrics for Plain Language Summ...

引用

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

作者： Guo, Yue August, Tal Leroy, Gondy Cohen, Trevor Wang, Lucy Lu University of Illinois Urbana-Champaign United States University of Arizona United States University of Washington United States Allen Institute for AI United States

ISBN: (纸本)9798891761643

While there has been significant development of models for Plain language Summarization (PLS), evaluation remains a challenge. PLS lacks a dedicated assessment metric, and the suitability of text generation evaluation metrics is unclear due to the unique transformations involved (e.g., adding background explanations, removing jargon). To address these questions, our study introduces a granular meta-evaluation testbed, APPLS, designed to evaluate metrics for PLS. We identify four PLS criteria from previous work-informativeness, simplification, coherence, and faithfulness-and define a set of perturbations corresponding to these criteria that sensitive metrics should be able to detect. We apply these perturbations to the texts of two PLS datasets to create our testbed. Using APPLS, we assess performance of 14 metrics, including automated scores, lexical features, and LLM prompt-based evaluations. Our analysis reveals that while some current metrics show sensitivity to specific criteria, no single method captures all four criteria simultaneously. We therefore recommend a suite of automated metrics be used to capture PLS quality along all relevant criteria. This work contributes the first meta-evaluation testbed for PLS and a comprehensive evaluation of existing metrics. © 2024 Association for Computational Linguistics.

关键词： Testbeds

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Moral Foundations of Large language Models

Moral Foundations of Large Language Models

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

作者： Abdulhai, Marwa Serapio-García, Greg Crepy, Clément Valter, Daria Canny, John Jaques, Natasha Department of Computer Science University of California Berkeley United States Department of Psychology University of Cambridge United Kingdom

ISBN: (纸本)9798891761643

Moral foundations theory (MFT) is a social psychological theory that decomposes human moral reasoning into five factors, including care/harm, liberty/oppression, and sanctity/degradation (Graham et al., 2009). People vary in the weight they place on these dimensions when making moral decisions, in part due to their cultural upbringing and political ideology. As large language models (LLMs) are trained on datasets collected from the internet, they may reflect the biases that are present in such corpora. This paper uses MFT as a lens to analyze whether popular LLMs have acquired a bias towards a particular set of moral values. We analyze known LLMs and find they exhibit particular moral foundations, and show how these relate to human moral foundations and political affiliations. We also measure the consistency of these biases, or whether they vary strongly depending on the context of how the model is prompted. Finally, we show that we can adversarially select prompts that encourage the moral to exhibit a particular set of moral foundations, and that this can affect the model's behavior on downstream tasks. These findings help illustrate the potential risks and unintended consequences of LLMs assuming a particular moral stance. © 2024 Association for Computational Linguistics.

关键词： Social psychology

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Pixology: Probing the Linguistic and Visual Capabilities of Pixel-based language Models

Pixology: Probing the Linguistic and Visual Capabilities of ...

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

作者： Tatariya, Kushal Araujo, Vladimir Bauwens, Thomas de Lhoneux, Miryam Department of Computer Science KU Leuven Belgium Sailplane AI

ISBN: (纸本)9798891761643

Pixel-based language models have emerged as a compelling alternative to subword-based language modelling, particularly because they can represent virtually any script. PIXEL, a canonical example of such a model, is a vision transformer that has been pre-trained on rendered text. While PIXEL has shown promising cross-script transfer abilities and robustness to orthographic perturbations, it falls short of outperforming monolingual subword counterparts like BERT in most other contexts. This discrepancy raises questions about the amount of linguistic knowledge learnt by these models and whether their performance in language tasks stems more from their visual capabilities than their linguistic ones. To explore this, we probe PIXEL using a variety of linguistic and visual tasks to assess its position on the vision-to-language spectrum. Our findings reveal a substantial gap between the model's visual and linguistic understanding. The lower layers of PIXEL predominantly capture superficial visual features, whereas the higher layers gradually learn more syntactic and semantic abstractions. Additionally, we examine variants of PIXEL trained with different text rendering strategies, discovering that introducing certain orthographic constraints at the input level can facilitate earlier learning of surface-level features. With this study, we hope to provide insights that aid the further development of pixel-based language models. © 2024 Association for Computational Linguistics.

关键词： Semantics

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