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

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LongAlign: A Recipe for Long Context Alignment of Large language Models

LongAlign: A Recipe for Long Context Alignment of Large Lang...

引用

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

作者： Bai, Yushi Lv, Xin Zhang, Jiajie He, Yuze Qi, Ji Hou, Lei Tang, Jie Dong, Yuxiao Li, Juanzi Tsinghua University China Zhipu.AI China

ISBN: (纸本)9798891761681

Extending large language models to effectively handle long contexts requires instruction fine-tuning on input sequences of similar length. To address this, we present LongAlign-a recipe of the instruction data, training, and evaluation for long context alignment. First, we construct a long instruction-following dataset using Self-Instruct. To ensure the data diversity, it covers a broad range of tasks from various long context sources. Second, we investigate different strategies to speed up supervised fine-tuning on datasets with uneven length distribution, namely packing and sorted batching. Additionally, we develop a loss weighting method to balance the contribution to the loss across different sequences during packing training. Third, we introduce the LongBench-Chat benchmark for evaluating instruction-following capabilities on queries of 10k-100k in length. Experiments show that LongAlign outperforms existing recipes for LLMs in long context tasks by up to 30%, while also maintaining their proficiency in handling short, generic tasks. The code, data, and long-aligned models are open-sourced at https://***/THUDM/LongAlign. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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PALS: Personalized Active Learning for Subjective Tasks in NLP

PALS: Personalized Active Learning for Subjective Tasks in N...

引用

conference on empirical methods in natural language processing (EMNLP)

作者： Kanclerz, Kamil Karanowski, Konrad Bielaniewicz, Julita Gruza, Marcin Milkowski, Piotr Kocon, Jan Kazienko, Przemyslaw Wroclaw Univ Sci & Technol Wroclaw Poland

ISBN: (纸本)9798891760608

For subjective NLP problems, such as classification of hate speech, aggression, or emotions, personalized solutions can be exploited. Then, the learned models infer about the perception of the content independently for each reader. To acquire training data, texts are commonly randomly assigned to users for annotation, which is expensive and highly inefficient. Therefore, for the first time, we suggest applying an active learning paradigm in a personalized context to better learn individual preferences. It aims to alleviate the labeling effort by selecting more relevant training samples. In this paper, we present novel Personalized Active Learning techniques for Subjective NLP tasks (PALS) to either reduce the cost of the annotation process or to boost the learning effect. Our five new measures allow us to determine the relevance of a text in the context of learning users' personal preferences. We validated them on three datasets: Wiki discussion texts individually labeled with aggression and toxicity, and on the Unhealthy Conversations dataset. Our PALS techniques outperform random selection even by more than 30%. They can also be used to reduce the number of necessary annotations while maintaining a given quality level. Personalized annotation assignments based on our controversy measure decrease the amount of data needed to just 25%-40% of the initial size.

关键词： natural language processing systems

来源：评论

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EHRAgent: Code Empowers Large language Models for Few-shot Complex Tabular Reasoning on Electronic Health Records

EHRAgent: Code Empowers Large Language Models for Few-shot C...

引用

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

作者： Shi, Wenqi Xu, Ran Zhuang, Yuchen Yu, Yue Zhang, Jieyu Wu, Hang Zhu, Yuanda Ho, Joyce Yang, Carl Wang, May D. Georgia Institute of Technology United States Emory University United States University of Washington United States

ISBN: (纸本)9798891761643

Clinicians often rely on data engineers to retrieve complex patient information from electronic health record (EHR) systems, a process that is both inefficient and time-consuming. We propose EHRAgent, a large language model (LLM) agent empowered with accumulative domain knowledge and robust coding capability. EHRAgent enables autonomous code generation and execution to facilitate clinicians in directly interacting with EHRs using natural language. Specifically, we formulate a multi-tabular reasoning task based on EHRs as a tool-use planning process, efficiently decomposing a complex task into a sequence of manageable actions with external toolsets. We first inject relevant medical information to enable EHRAgent to effectively reason about the given query, identifying and extracting the required records from the appropriate tables. By integrating interactive coding and execution feedback, EHRAgent then effectively learns from error messages and iteratively improves its originally generated code. Experiments on three real-world EHR datasets show that EHRAgent outperforms the strongest baseline by up to 29.6% in success rate, verifying its strong capacity to tackle complex clinical tasks with minimal demonstrations. © 2024 Association for Computational Linguistics.

关键词： Electronic health record

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By My Eyes: Grounding Multimodal Large language Models with Sensor Data via Visual Prompting

By My Eyes: Grounding Multimodal Large Language Models with ...

引用

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

作者： Yoon, Hyungjun Tolera, Biniyam Aschalew Gong, Taesik Lee, Kimin Lee, Sung-Ju KAIST Korea Republic of UNIST Korea Republic of

ISBN: (纸本)9798891761643

Large language models (LLMs) have demonstrated exceptional abilities across various domains. However, utilizing LLMs for ubiquitous sensing applications remains challenging as existing text-prompt methods show significant performance degradation when handling long sensor data sequences. We propose a visual prompting approach for sensor data using multimodal LLMs (MLLMs). We design a visual prompt that directs MLLMs to utilize visualized sensor data alongside the target sensory task descriptions. Additionally, we introduce a visualization generator that automates the creation of optimal visualizations tailored to a given sensory task, eliminating the need for prior task-specific knowledge. We evaluated our approach on nine sensory tasks involving four sensing modalities, achieving an average of 10% higher accuracy than text-based prompts and reducing token costs by 15.8×. Our findings highlight the effectiveness and cost-efficiency of visual prompts with MLLMs for various sensory tasks. The source code is available at https://***/diamond264/ByMyEyes. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Evolutionary Contrastive Distillation for language Model Alignment

Evolutionary Contrastive Distillation for Language Model Ali...

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

作者： Katz-Samuels, Julian Li, Zheng Yun, Hyokun Nigam, Priyanka Xu, Yi Petricek, Vaclav Yin, Bing Chilimbi, Trishul Amazon United States

ISBN: (纸本)9798891761681

The ability of large language models (LLMs) to execute complex instructions is essential for their real-world applications. However, several recent studies indicate that LLMs struggle with challenging instructions (Zhou et al., 2023;Qin et al., 2024;Jiang et al., 2023b). In this paper, we propose Evolutionary Contrastive Distillation (ECD), a novel method for generating high-quality synthetic preference data designed to enhance the complex instruction-following capability of language models. ECD generates data that specifically illustrates the difference between a response that successfully follows a set of complex instructions and a response that is high-quality, but nevertheless makes some subtle mistakes. This is done by prompting LLMs to progressively evolve simple instructions into more complex instructions. When an instruction is made more complex, the original successful response mostly meets the new requirements but misses one or two, thus becoming a "hard negative" example for the new instruction. By pairing a good response with such a hard negative response, and employing contrastive learning algorithms such as DPO (Rafailov et al., 2023), we improve language models' ability to follow complex instructions. empirically, we observe that our method yields a 7B model that exceeds the complex instruction-following performance of current state-of-the-art (SOTA) 7B models and is competitive even with open-source 70B models. © 2024 Association for Computational Linguistics.

关键词： Contrastive Learning

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Advancing Vision-language Models with Adapter Ensemble Strategies

Advancing Vision-Language Models with Adapter Ensemble Strat...

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

作者： Bai, Yue Zhao, Handong Lin, Zhe Kale, Ajinkya Gu, Jiuxiang Yu, Tong Kim, Sungchul Fu, Yun Northeastern University United States Adobe United States

ISBN: (纸本)9798891761681

CLIP (Radford et al., 2021) revolutes vision-language pretraining by using contrastive learning on paired web data. However, the sheer size of these pretrained models makes full-model finetuning exceedingly costly. One common solution is the "adapter", which finetunes a few additional parameters while freezing the backbone. It harnesses the heavy-duty backbone while offering a light finetuning for small downstream tasks. This synergy prompts us to explore the potential of augmenting large-scale backbones with traditional machine learning techniques. Often employed in traditional fields and overlooked in the large-scale era, these techniques could provide valuable enhancements. Herein, we delve into the "adapter ensembles" in the realm of pretrained large-scale vision-language models. We begin with a proof-of-concept study to establish the efficacy of combining multiple adapters. We then present extensive evidence showing these ensembles excel in a variety of settings, particularly when employing a Multi-Scale Attention (MSA) approach thoughtfully integrated into the ensemble framework. We further incorporate the LoRA to mitigate the additional parameter burden. We focus on vision-language retrieval, using different backbones under constraints of minimal data, parameters, and finetuning budgets. This research paves the way for a synergistic blend of traditional, yet effective, strategies with modern large-scale networks. © 2024 Association for Computational Linguistics.

关键词： Contrastive Learning

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Free your mouse! Command Large language Models to Generate Code to Format Word Documents

Free your mouse! Command Large Language Models to Generate C...

引用

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

作者： Rao, Shihao Li, Liang Liu, Jiapeng Guan, Weinxin Gao, Xiyan Li, Bing Ma, Can Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798891761643

Recently, LLMs have significantly improved code generation, making it increasingly accessible to users. As a result, LLM-powered code generation applications have sprung up, vastly boosting user productivity. This paper mainly explores how to improve the efficiency and experience of users in formatting the document. Specifically, we propose an automatic document formatting method, TEXT-TO-FORMAT, which is driven by various prompting strategies. TEXT-TO-FORMAT takes the user's formatting instructions and then generates code that can be run in Microsoft Word to format the content in a document. Further, to evaluate automatic document formatting approaches and advance the document formatting task, we build an evaluation specification including a high-quality dataset DOCFORMEVAL, a code runtime environment, and evaluation metrics. Extensive experimental results on DOCFORMEVAL reveal that the prompting strategy's effect positively correlates with how much knowledge it introduces related to document formatting task. We believe the constructed DOCFORMEVAL and the exploration about TEXT-TO-FORMAT can help developers build more intelligent tools for automatic document formatting, especially in offline scenarios, where the data privacy is the top priority. © 2024 Association for Computational Linguistics.

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A New Pipeline for Knowledge Graph Reasoning Enhanced by Large language Models Without Fine-Tuning

A New Pipeline for Knowledge Graph Reasoning Enhanced by Lar...

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

作者： Chen, Zhongwu Bai, Long Li, Zixuan Huang, Zhen Jin, Xiaolong Dou, Yong National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology China CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)9798891761643

Conventional Knowledge Graph Reasoning (KGR) models learn the embeddings of KG components over the structure of KGs, but their performances are limited when the KGs are severely incomplete. Recent LLM-enhanced KGR models input KG structural information into LLMs. However, they require fine-tuning on open-source LLMs and are not applicable to closed-source LLMs. Therefore, in this paper, to leverage the knowledge in LLMs without fine-tuning to assist and enhance conventional KGR models, we propose a new three-stage pipeline, including knowledge alignment, KG reasoning and entity reranking. Specifically, in the alignment stage, we propose three strategies to align the knowledge in LLMs to the KG schema by explicitly associating unconnected nodes with semantic relations. Based on the enriched KGs, we train structure-aware KGR models to integrate aligned knowledge to original knowledge existing in KGs. In the reranking stage, after obtaining the results of KGR models, we rerank the top-scored entities with LLMs to recall correct answers further. Experiments show our pipeline can enhance the KGR performance in both incomplete and general situations. © 2024 Association for Computational Linguistics.

关键词： Semantics

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ChatGPT Doesn't Trust Chargers Fans: Guardrail Sensitivity in Context

ChatGPT Doesn't Trust Chargers Fans: Guardrail Sensitivity i...

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

作者： Li, Victoria R. Chen, Yida Saphra, Naomi Harvard University United States Kempner Institute for the Study of Natural and Artificial Intelligence Harvard University United States

ISBN: (纸本)9798891761643

While the biases of language models in production are extensively documented, the biases of their guardrails have been neglected. This paper studies how contextual information about the user influences the likelihood of an LLM to refuse to execute a request. By generating user biographies that offer ideological and demographic information, we find a number of biases in guardrail sensitivity on GPT-3.5. Younger, female, and Asian-American personas are more likely to trigger a refusal guardrail when requesting censored or illegal information. Guardrails are also sycophantic, refusing to comply with requests for a political position the user is likely to disagree with. We find that certain identity groups and seemingly innocuous information, e.g., sports fandom, can elicit changes in guardrail sensitivity similar to direct statements of political ideology. For each demographic category and even for American football team fandom, we find that ChatGPT appears to infer a likely political ideology and modify guardrail behavior accordingly. © 2024 Association for Computational Linguistics.

关键词： Guard rails

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Large language Models Know What is Key Visual Entity: An LLM-assisted Multimodal Retrieval for VQA

Large Language Models Know What is Key Visual Entity: An LLM...

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

作者： Jian, Pu Yu, Donglei Zhang, Jiajun Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China Wuhan AI Research China Shanghai Artificial Intelligence Laboratory China

ISBN: (纸本)9798891761643

Visual question answering (VQA) tasks, often performed by visual language model (VLM), face challenges with long-tail knowledge. Recent retrieval-augmented VQA (RA-VQA) systems address this by retrieving and integrating external knowledge sources. However, these systems still suffer from redundant visual information irrelevant to the question during retrieval. To address these issues, in this paper, we propose LLM-RA, a novel method leveraging the reasoning capability of a large language model (LLM) to identify key visual entities, thus minimizing the impact of irrelevant information in the query of retriever. Furthermore, key visual entities are independently encoded for multimodal joint retrieval, preventing cross-entity interference. Experimental results demonstrate that our method outperforms other strong RA-VQA systems. In two knowledge-intensive VQA benchmarks, our method achieves the new state-of-the-art performance among those with similar scale of parameters and even performs comparably to models with 1-2 orders larger parameters. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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