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

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Correct after Answer: Enhancing Multi-Span Question Answering with Post-processing Method

Correct after Answer: Enhancing Multi-Span Question Answerin...

引用

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

作者： Lin, Jiayi Zhang, Chenyang Tong, Haibo Zhang, Dongyu Hong, Qingqing Hou, Bingxuan Wang, Junli Key Laboratory of Embedded System and Service Computing Tongji University Ministry of Education Shanghai201804 China Collaborative Innovation Center for Financial Network Security Tongji University Shanghai201804 China

ISBN: (纸本)9798891761681

Multi-Span Question Answering (MSQA) requires models to extract one or multiple answer spans from a given context to answer a question. Prior work mainly focuses on designing specific methods or applying heuristic strategies to encourage models to predict more correct predictions. However, these models are trained on gold answers and fail to consider the incorrect predictions. Through a statistical analysis, we observe that models with stronger abilities do not predict less incorrect predictions compared with other models. In this work, we propose Answering-Classifying-Correcting (ACC) framework, which employs a post-processing strategy to handle incorrect predictions. Specifically, the ACC framework first introduces a classifier to classify the predictions into three types and exclude "wrong predictions", then introduces a corrector to modify "partially correct predictions". Experiments on several MSQA datasets show that ACC framework significantly improves the Exact Match (EM) scores, and further analysis demostrates that ACC framework efficiently reduces the number of incorrect predictions, improving the quality of predictions. © 2024 Association for Computational Linguistics.

关键词： Prediction models

来源：评论

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Is Child-Directed Speech Effective Training Data for language Models?

Is Child-Directed Speech Effective Training Data for Languag...

引用

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

作者： Feng, Steven Y. Goodman, Noah D. Frank, Michael C. Stanford University United States

ISBN: (纸本)9798891761643

While high-performing language models are typically trained on hundreds of billions of words, human children become fluent language users with a much smaller amount of data. What are the features of the data they receive, and how do these features support language modeling objectives? To investigate this question, we train GPT-2 and RoBERTa models on 29M words of English child-directed speech and a new matched, synthetic dataset (TinyDialogues), comparing to OpenSubtitles, Wikipedia, and a heterogeneous blend of datasets from the BabyLM challenge. We evaluate the syntactic and semantic knowledge of these models using developmentally-inspired evaluations. Through pretraining experiments, we test whether the global developmental ordering or the local discourse ordering of children's training data supports high performance relative to other datasets. The local properties of the data affect model results, but surprisingly, global properties do not. Further, child language input is not uniquely valuable for training language models. These findings support the hypothesis that, rather than proceeding from better data, the child's learning algorithm is substantially more data-efficient than current language modeling techniques. © 2024 Association for Computational Linguistics.

关键词： Digital elevation model

来源：评论

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An empirical Investigation of Implicit and Explicit Knowledge-Enhanced methods for Ad Hoc Dataset Retrieval

An Empirical Investigation of Implicit and Explicit Knowledg...

引用

conference on empirical methods in natural language processing (EMNLP)

作者： Luo, Weiqing Chen, Qiaosheng Zhang, Zhiyang Huang, Zixian Cheng, Gong Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China

ISBN: (纸本)9798891760615

Ad hoc dataset retrieval has become an important way of finding data on the Web, where the underlying problem is how to measure the relevance of a dataset to a query. State-of-the-art solutions for this task are still lexical methods, which cannot capture semantic similarity. Semantics-aware knowledge-enhanced retrieval methods, which achieved promising results on other tasks, have yet to be systematically studied on this specialized task. To fill the gap, in this paper, we present an empirical investigation of the task where we implement and evaluate, on two test collections, a set of implicit and explicit knowledge-enhancement retrieval methods in various settings. Our results reveal the unique features of the task and suggest an interpolation of different kinds of methods as the current best practice.

关键词： Semantics

来源：评论

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Evaluating Moral Beliefs across LLMs through a Pluralistic Framework

Evaluating Moral Beliefs across LLMs through a Pluralistic F...

引用

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

作者： Liu, Xuelin Zhu, Yanfei Zhu, Shucheng Liu, Pengyuan Liu, Ying Yu, Dong School of Information Science Beijing Language and Culture University Beijing China School of Humanities Tsinghua University Beijing China National Print Media Language Resources Monitoring & Research Center Beijing Language and Culture University Beijing China

ISBN: (纸本)9798891761681

Proper moral beliefs are fundamental for language models, yet assessing these beliefs poses a significant challenge. This study introduces a novel three-module framework to evaluate the moral beliefs of four prominent large language models. Initially, we constructed a dataset containing 472 moral choice scenarios in Chinese, derived from moral words. The decision-making process of the models in these scenarios reveals their moral principle preferences. By ranking these moral choices, we discern the varying moral beliefs held by different language models. Additionally, through moral debates, we investigate the firmness of these models to their moral choices. Our findings indicate that English language models, namely ChatGPT and Gemini, closely mirror moral decisions of the sample of Chinese university students, demonstrating strong adherence to their choices and a preference for individualistic moral beliefs. In contrast, Chinese models such as Ernie and ChatGLM lean towards collectivist moral beliefs, exhibiting ambiguity in their moral choices and debates. This study also uncovers gender bias embedded within the moral beliefs of all examined language models. Our methodology offers an innovative means to assess moral beliefs in both artificial and human intelligence, facilitating a comparison of moral values across different cultures. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Small language Models Fine-tuned to Coordinate Larger language Models improve Complex Reasoning

Small Language Models Fine-tuned to Coordinate Larger Langua...

引用

conference on empirical methods in natural language processing (EMNLP)

作者： Juneja, Gurusha Dutta, Subhabrata Chakrabarti, Soumen Manchhanda, Sunny Chakraborty, Tanmoy IIT Delhi Delhi India Indian Inst Technol Bombay Maharashtra India DYSL AI Bengaluru India

ISBN: (纸本)9798891760608

Large language Models (LLMs) prompted to generate chain-of-thought (CoT) exhibit impressive reasoning capabilities. Recent attempts at prompt decomposition toward solving complex, multi-step reasoning problems depend on the ability of the LLM to simultaneously decompose and solve the problem. A significant disadvantage is that foundational LLMs are typically not available for finetuning, making adaptation computationally prohibitive. We believe (and demonstrate) that problem decomposition and solution generation are distinct capabilites, better addressed in separate modules, than by one monolithic LLM. We introduce DaSLaM, which uses a decomposition generator to decompose complex problems into subproblems that require fewer reasoning steps. These subproblems are answered by a solver. We use a relatively small (13B parameters) LM as the decomposition generator, which we train using policy gradient optimization to interact with a solver LM (regarded as blackbox) and guide it through subproblems, thereby rendering our method solver-agnostic. Evaluation on multiple different reasoning datasets reveal that with our method, a 175 billion parameter LM (text-davinci-003) can produce competitive or even better performance, compared to its orders-of-magnitude larger successor, GPT-4. Additionally, we show that DaSLaM is not limited by the solver's capabilities as a function of scale;e.g., solver LMs with diverse sizes give significant performance improvement with our solver-agnostic decomposition technique. Exhaustive ablation studies evince the superiority of our modular finetuning technique over exorbitantly large decomposer LLMs, based on prompting alone.

关键词： Computational linguistics

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Text encoders bottleneck compositionality in contrastive vision-language models

Text encoders bottleneck compositionality in contrastive vis...

引用

conference on empirical methods in natural language processing (EMNLP)

作者： Kamath, Amita Hessel, Jack Chang, Kai-Wei Univ Calif Los Angeles Los Angeles CA 90024 USA Allen Inst AI Seattle WA USA

ISBN: (纸本)9798891760608

Performant vision-language (VL) models like CLIP represent captions using a single vector. How much information about language is lost in this bottleneck? We first curate CompPrompts, a set of increasingly compositional image captions that VL models should be able to capture (e.g., single object, to object+property, to multiple interacting objects). Then, we train text-only recovery probes that aim to reconstruct captions from single-vector text representations produced by several VL models. This approach does not require images, allowing us to test on a broader range of scenes compared to prior work. We find that: 1) CLIP's text encoder falls short on more compositional inputs, including object relationships, attribute-object association, counting, and negations;2) some text encoders work significantly better than others;and 3) text-only recovery performance predicts multimodal matching performance on ControlledImCaps: a new evaluation benchmark we collect and release consisting of fine-grained compositional images and captions. Specifically, our results suggest textonly recoverability is a necessary (but not sufficient) condition for modeling compositional factors in contrastive VL models. We release our datasets and code.

关键词： Benchmarking

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Query Rewriting for Retrieval-Augmented Large language Models

Query Rewriting for Retrieval-Augmented Large Language Model...

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conference on empirical methods in natural language processing (EMNLP)

作者： Ma, Xinbei Gong, Yeyun He, Pengcheng Zhao, Hai Duan, Nan Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai Peoples R China Shanghai Jiao Tong Univ Key Lab Shanghai Educ Commiss Intelligent Interac Shanghai Peoples R China Microsoft Res Asia Beijing Peoples R China Microsoft Azure AI Redmond WA USA

ISBN: (纸本)9798891760608

Large language Models (LLMs) play powerful, black-box readers in the retrieve-thenread pipeline, making remarkable progress in knowledge-intensive tasks. This work introduces a new framework, Rewrite-Retrieve-Read instead of the previous retrieve-then-read for the retrieval-augmented LLMs from the perspective of the query rewriting. Unlike prior studies focusing on adapting either the retriever or the reader, our approach pays attention to the adaptation of the search query itself, for there is inevitably a gap between the input text and the needed knowledge in retrieval. We first prompt an LLM to generate the query, then use a web search engine to retrieve contexts. Furthermore, to better align the query to the frozen modules, we propose a trainable scheme for our pipeline. A small language model is adopted as a trainable rewriter to cater to the black-box LLM reader. The rewriter is trained using the feedback of the LLM reader by reinforcement learning. Evaluation is conducted on downstream tasks, open-domain QA and multiple-choice QA. Experiments results show consistent performance improvement, indicating that our framework is proven effective and scalable, and brings a new framework for retrieval-augmented LLM (1).

关键词： Pipelines

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Large language Models Can Not Perform Well in Understanding and Manipulating natural language at Both Character and Word Levels?

Large Language Models Can Not Perform Well in Understanding ...

引用

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

作者： Zhang, Yidan He, Zhenan College of Computer Science Sichuan University China

ISBN: (纸本)9798891761681

Despite their promising performance across various tasks, recent studies reveal that Large language models (LLMs) still exhibit significant deficiencies in handling several word-level and character-level tasks, e.g., word unscrambling and sentence editing, indicating urgent needs for substantial improvements in basic language understanding and manipulation. To address these challenges, it is crucial to develop large-scale benchmarks that can comprehensively assess the performance of LLMs in basic language tasks. In this paper, we introduce a bilingual benchmark, CWUM, to investigate the capabilities and limitations of LLMs in understanding and manipulating natural language at both character and word levels. CWUM consists of 15 simple text editing tasks, e.g., letter counting, word reversing, Chinese character inserting, etc. We conduct extensive experiments on eight advanced LLMs, including base models and instruction-tuned (chat) variants. The experimental results highlight significant failures of existing LLMs on CWUM tasks that humans can solve perfectly with 100% accuracy. On English tasks of CWUM, the average accuracy of GPT-4, LLaMA-3-70B, and Qwen-72B is 66.64%, 39.32%, and 33.16%, respectively, which lags far behind human performance. Instruction-tuning the base model does not lead to a distinct performance improvement, as the average accuracy of LLaMA-3-70B-Instruct on English tasks is only 1.44% higher than that of the base LLaMA-3-70B. Ultimately, we show that supervised fine-tuning (SFT) can enhance model performance on CWUM without compromising its ability to generalize across general tasks. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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A Pretrained language Model for Cyber Threat Intelligence

A Pretrained Language Model for Cyber Threat Intelligence

引用

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

作者： Park, Youngja You, Weiqiu IBM T. J. Watson Research Center Yorktown HeightsNY United States University of Pennsylvania PhiladelphiaPA United States

We present a new BERT model for the cybersecurity domain, CTI-BERT, which can improve the accuracy of cyber threat intelligence (CTI) extraction, enabling organizations to better defend against potential cyber threats. We provide detailed information about the domain corpus collection, the training methodology and its effectiveness for a variety of NLP tasks for the cybersecurity domain. The experiments show that CTI-BERT significantly outperforms several general-domain and security-domain models for these cybersecurity applications, indicating that the training data and methodology have a significant impact on the model performance. © 2023 Association for Computational Linguistics.

关键词： Cybersecurity

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Optimizing language Models with Fair and Stable Reward Composition in Reinforcement Learning

Optimizing Language Models with Fair and Stable Reward Compo...

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

作者： Li, Jiahui Zhang, Hanlin Zhang, Fengda Chang, Tai-Wei Kuang, Kun Chen, Long Zhou, Jun Zhejiang University China Ant Group China HKUST Hong Kong

ISBN: (纸本)9798891761643

Reinforcement learning from human feedback (RLHF) and AI-generated feedback (RLAIF) have become prominent techniques that significantly enhance the functionality of pre-trained language models (LMs). These methods harness feedback, sourced either from humans or AI, as direct rewards or to shape reward models that steer LM optimization. Nonetheless, the effective integration of rewards from diverse sources presents a significant challenge due to their disparate characteristics. To address this, recent research has developed algorithms incorporating strategies such as weighting, ranking, and constraining to handle this complexity. Despite these innovations, a bias toward disproportionately high rewards can still skew the reinforcement learning process and negatively impact LM performance. This paper explores a methodology for reward composition that enables simultaneous improvements in LMs across multiple dimensions. Inspired by fairness theory, we introduce a training algorithm that aims to reduce Disparity and enhance Stability among various rewards. Our method treats the aggregate reward as a dynamic weighted sum of individual rewards, with alternating updates to the weights and model parameters. For efficient and straightforward implementation, we employ an estimation technique rooted in the mirror descent method for weight updates, eliminating the need for gradient computations. The empirical results under various types of rewards across a wide range of scenarios demonstrate the effectiveness of our method. © 2024 Association for Computational Linguistics.

关键词： Contrastive Learning

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