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COOL, a Context Outlooker, and Its Application to Question Answering and Other natural language processing Tasks 32

COOL, a Context Outlooker, and Its Application to Question A...

引用

32nd International Joint conference on Artificial Intelligence (IJCAI)

作者： Zhu, Fangyi Ng, See-Kiong Bressan, Stephane Natl Univ Singapore Singapore Singapore

ISBN: (纸本)9781956792034

Vision outlooker improves the performance of vision transformers, which implements a self-attention mechanism by adding an outlook attention, a form of local attention. In natural language processing, as has been the case in computer vision and other domains, transformer-based models constitute the state-of-the-art for most processing tasks. In this domain, too, many authors have argued and demonstrated the importance of local context. We present an outlook attention mechanism, COOL, for natural language processing. COOL, added on top of the self-attention layers of a transformer-based model, encodes local syntactic context considering word proximity and more pair-wise constraints than dynamic convolution used by existing approaches. A comparative empirical performance evaluation of an implementation of COOL with different transformer-based models confirms the opportunity for improvement over a baseline using the original models alone for various natural language processing tasks, including question answering. The proposed approach achieves competitive performance with existing state-of-the-art methods on some tasks.

关键词： natural language processing systems

来源：评论

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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...

引用

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

引用

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

来源：评论

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Fine-Tuning Large language Models for Stock Return Prediction Using Newsflow

Fine-Tuning Large Language Models for Stock Return Predictio...

引用

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

作者： Guo, Tian Hauptmann, Emmanuel Systematic Equities Team RAM Active Investments Geneva Switzerland

ISBN: (纸本)9798891761667

Large language models (LLMs) and their fine-tuning techniques have demonstrated superior performance in various language understanding and generation tasks. This paper explores fine-tuning LLMs for predicting stock returns with financial newsflow. Return prediction is fundamental for quantitative investing tasks like portfolio construction and optimization. We formulate the model to include a text representation and forecasting modules. We propose to compare the encoder-only and decoder-only LLMs, considering they generate text representations in distinct ways. The impact of these different representations on return forecasting remains an open question. Meanwhile, we compare two simple methods of integrating LLMs’ token-level representations into the forecasting module. The experiments on real investment universes reveal that: (1) aggregated representations from LLMs’ token-level embeddings generally produce return predictions that enhance the performance of long-only and long-short portfolios;(2) in the relatively large investment universe, the decoder LLMs-based prediction model leads to stronger portfolios, whereas in the small universes, there are no consistent winners;(3) return predictions derived from LLMs’ text representations are a strong signal for portfolio construction, outperforming conventional sentiment scores. These findings suggest the potential of LLM fine-tuning for enhancing return prediction-based portfolio construction. © 2024 Association for Computational Linguistics.

关键词： Prediction models

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Hierarchical Pretraining on Multimodal Electronic Health Records

Hierarchical Pretraining on Multimodal Electronic Health Rec...

引用

conference on empirical methods in natural language processing (EMNLP)

作者： Wang, Xiaochen Luo, Junyu Wang, Jiaqi Yin, Ziyi Cui, Suhan Zhong, Yuan Wang, Yaqing Ma, Fenglong Penn State Univ University Pk PA 16802 USA Google Res Mountain View CA 94043 USA

ISBN: (纸本)9798891760608

Pretraining has proven to be a powerful technique in natural language processing (NLP), exhibiting remarkable success in various NLP downstream tasks. However, in the medical domain, existing pretrained models on electronic health records (EHR) fail to capture the hierarchical nature of EHR data, limiting their generalization capability across diverse downstream tasks using a single pretrained model. To tackle this challenge, this paper introduces a novel, general, and unified pretraining framework called MEDHMP1, specifically designed for hierarchically multimodal EHR data. The effectiveness of the proposed MEDHMP is demonstrated through experimental results on eight downstream tasks spanning three levels. Comparisons against eighteen baselines further highlight the efficacy of our approach.

关键词： natural language processing systems

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Uncertainty in language Models: Assessment through Rank-Calibration

Uncertainty in Language Models: Assessment through Rank-Cali...

引用

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

作者： Huang, Xinmeng Li, Shuo Yu, Mengxin Sesia, Matteo Hassani, Hamed Lee, Insup Bastani, Osbert Dobriban, Edgar University of Pennsylvania PhiladelphiaPA United States University of Southern California Los AngelesCA United States

ISBN: (纸本)9798891761643

language Models (LMs) have shown promising performance in natural language generation. However, as LMs often generate incorrect or hallucinated responses, it is crucial to correctly quantify their uncertainty in responding to given inputs. In addition to verbalized confidence elicited via prompting, many uncertainty measures (e.g., semantic entropy and affinity-graph-based measures) have been proposed. However, these measures can differ greatly, and it is unclear how to compare them, partly because they take values over different ranges (e.g., [0, ∞) or [0, 1]). In this work, we address this issue by developing a novel and practical framework, termed Rank-Calibration, to assess uncertainty and confidence measures for LMs. Our key tenet is that higher uncertainty (or lower confidence) should imply lower generation quality, on average. Rank-calibration quantifies deviations from this ideal relationship in a principled manner, without requiring ad hoc binary thresholding of the correctness score (e.g., ROUGE or METEOR). The broad applicability and the granular interpretability of our methods are demonstrated empirically. The code to replicate our experiments is here. © 2024 Association for Computational Linguistics.

关键词： Semantics

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TAIL: A Toolkit for Automatic and Realistic Long-Context Large language Model Evaluation

TAIL: A Toolkit for Automatic and Realistic Long-Context Lar...

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2024 conference on empirical methods in natural language processing: System Demonstrations, EMNLP 2024

作者： Gu, Gefei Zhao, Yilun Ning, Ruoxi Zheng, Yanan Cohan, Arman Yale University United States University of Warterloo Canada Allen Institute for AI India

ISBN: (纸本)9798891761674

As long-context large language models (LLMs) gain increasing attention for their ability to handle extensive inputs, the demand for effective evaluation methods has become critical. Existing evaluation methods, however, fall short: needle-in-a-haystack (NIAH) and its variants are overly simplistic, while creating realistic benchmarks is prohibitively expensive due to extensive human annotation requirements. To bridge this gap, we propose TAIL, an automatic toolkit for creating realistic evaluation benchmarks and assessing the performance of long-context LLMs. With TAIL, users can customize the building of a long-context, document-grounded QA benchmark and obtain visualized performance metrics of evaluated models. TAIL has the advantage of requiring minimal human annotation and generating natural questions based on user-provided long-context documents. We apply TAIL to construct a benchmark encompassing multiple expert domains, such as finance, law, patent, and scientific literature. We then evaluate four state-of-the-art long-context LLMs using this benchmark. Results show that all the evaluated LLMs experience varying degrees of performance degradation as context lengths increase. § https://***/yale-nlp/TAIL. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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An Effective Deployment of Diffusion LM for Data Augmentation in Low-Resource Sentiment Classification

An Effective Deployment of Diffusion LM for Data Augmentatio...

引用

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

作者： Chen, Zhuowei Wang, Lianxi Wu, Yuben Liao, Xinfeng Tian, Yujia Zhong, Junyang Guangdong University of Foreign Studies Guangzhou China Guangzhou Key Laboratory of Multilingual Intelligent Processing Guangzhou China

ISBN: (纸本)9798891761643

Sentiment classification (SC) often suffers from low-resource challenges such as domain-specific contexts, imbalanced label distributions, and few-shot scenarios. The potential of the diffusion language model (LM) for textual data augmentation (DA) remains unexplored, moreover, textual DA methods struggle to balance the diversity and consistency of new samples. Most DA methods either perform logical modifications or rephrase less important tokens in the original sequence with the language model. In the context of SC, strong emotional tokens could act critically on the sentiment of the whole sequence. Therefore, contrary to rephrasing less important context, we propose DiffusionCLS to leverage a diffusion LM to capture in-domain knowledge and generate pseudo samples by reconstructing strong label-related tokens. This approach ensures a balance between consistency and diversity, avoiding the introduction of noise and augmenting crucial features of datasets. DiffusionCLS also comprises a Noise-Resistant Training objective to help the model generalize. Experiments demonstrate the effectiveness of our method in various low-resource scenarios including domain-specific and domain-general problems. Ablation studies confirm the effectiveness of our framework's modules, and visualization studies highlight optimal deployment conditions, reinforcing our conclusions. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Themis: A Reference-free NLG Evaluation language Model with Flexibility and Interpretability

Themis: A Reference-free NLG Evaluation Language Model with ...

引用

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

作者： Hu, Xinyu Lin, Li Gao, Mingqi Yin, Xunjian Wan, Xiaojun Wangxuan Institute of Computer Technology Peking University China

ISBN: (纸本)9798891761643

The evaluation of natural language generation (NLG) tasks is a significant and longstanding research area. With the recent emergence of powerful large language models (LLMs), some studies have turned to LLM-based automatic evaluation methods, which demonstrate great potential to become a new evaluation paradigm following traditional string-based and model-based metrics. However, despite the improved performance of existing methods, they still possess some deficiencies, such as dependency on references and limited evaluation flexibility. Therefore, in this paper, we meticulously construct a large-scale NLG evaluation corpus NLG-Eval with annotations from both human and GPT-4 to alleviate the lack of relevant data in this field. Furthermore, we propose Themis, an LLM dedicated to NLG evaluation, which has been trained with our designed multi-perspective consistency verification and rating-oriented preference alignment methods. Themis can conduct flexible and interpretable evaluations without references, and it exhibits superior evaluation performance on various NLG tasks, simultaneously generalizing well to unseen tasks and surpassing other evaluation models, including GPT-4. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Discovering Biases in Information Retrieval Models Using Relevance Thesaurus as Global Explanation

Discovering Biases in Information Retrieval Models Using Rel...

引用

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

作者： Kim, Youngwoo Rahimi, Razieh Allan, James University of Massachusetts Amherst United States

ISBN: (纸本)9798891761643

Most efforts in interpreting neural relevance models have focused on local explanations, which explain the relevance of a document to a query but are not useful in predicting the model's behavior on unseen query-document *** propose a novel method to globally explain neural relevance models by constructing a "relevance thesaurus" containing semantically relevant query and document term *** thesaurus is used to augment lexical matching models such as BM25 to approximate the neural model's *** method involves training a neural relevance model to score the relevance of partial query and document segments, which is then used to identify relevant terms across the vocabulary *** evaluate the obtained thesaurus explanation based on ranking effectiveness and fidelity to the target neural ranking ***, our thesaurus reveals the existence of brand name bias in ranking models, demonstrating one advantage of our explanation method. © 2024 Association for Computational Linguistics.

关键词： Structured Query language

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