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Interpreting Arithmetic Mechanism in Large language Models through Comparative Neuron Analysis

Interpreting Arithmetic Mechanism in Large Language Models t...

引用

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

作者： Yu, Zeping Ananiadou, Sophia Department of Computer Science National Centre for Text Mining The University of Manchester United Kingdom

ISBN: (纸本)9798891761643

We find arithmetic ability resides within a limited number of attention heads, with each head specializing in distinct operations. To delve into the reason, we introduce the Comparative Neuron Analysis (CNA) method, which identifies an internal logic chain consisting of four distinct stages from input to prediction: feature enhancing with shallow FFN neurons, feature transferring by shallow attention layers, feature predicting by arithmetic heads, and prediction enhancing among deep FFN neurons. Moreover, we identify the human-interpretable FFN neurons within both feature-enhancing and feature-predicting stages. These findings lead us to investigate the mechanism of LoRA, revealing that it enhances prediction probabilities by amplifying the coefficient scores of FFN neurons related to predictions. Finally, we apply our method in model pruning for arithmetic tasks and model editing for reducing gender bias. Code is on https://***/zepingyu0512/arithmetic-mechanism. © 2024 Association for Computational Linguistics.

关键词： Neurons

来源：评论

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From Wrong To Right: A Recursive Approach Towards Vision-language Explanation

From Wrong To Right: A Recursive Approach Towards Vision-Lan...

引用

conference on empirical methods in natural language processing (EMNLP)

作者： Ge, Jiaxin Subramanian, Sanjay Darrell, Trevor Li, Boyi Univ Calif Berkeley Berkeley CA 94720 USA Peking Univ Beijing Peoples R China

ISBN: (纸本)9798891760608

Addressing the challenge of adapting pretrained vision-language models for generating insightful explanations for visual reasoning tasks with limited annotations, we present ReVisE: a Recursive Visual Explanation algorithm. Our method iteratively computes visual features (conditioned on the text input), an answer, and an explanation, to improve the explanation quality step by step until the answer converges. We find that this multi-step approach guides the model to correct its own answers and outperforms single-step explanation generation. Furthermore, explanations generated by ReVisE also serve as valuable annotations for few-shot self-training. Our approach outperforms previous methods while utilizing merely 5% of the human-annotated explanations across 10 metrics, demonstrating up to a 4.2 and 1.3 increase in BLEU-1 score on the VCR and VQA-X datasets, underscoring the efficacy and data-efficiency of our method.

关键词： Iterative methods

来源：评论

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CoLLiE: Collaborative Training of Large language Models in an Efficient Way

CoLLiE: Collaborative Training of Large Language Models in a...

引用

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

作者： Lv, Kai Zhang, Shuo Gu, Tianle Xing, Shuhao Hong, Jiawei Chen, Keyu Liu, Xiaoran Yang, Yuqing Guo, Honglin Liu, Tengxiao Sun, Yu Guo, Qipeng Yan, Hang Qiu, Xipeng School of Computer Science Fudan University China Shanghai AI Laboratory China

Large language models (LLMs) are increasingly pivotal in a wide range of natural language processing tasks. Access to pre-trained models, courtesy of the open-source community, has made it possible to adapt these models to specific applications for enhanced performance. However, the substantial resources required for training these models necessitate efficient solutions. This paper introduces CoLLiE, an efficient library that facilitates collaborative training of large language models using 3D parallelism, parameter-efficient finetuning (PEFT) methods, and optimizers such as Lion, Adan, Sophia, and LOMO. With its modular design and comprehensive functionality, CoLLiE offers a balanced blend of efficiency, ease of use, and customization. CoLLiE has proven superior training efficiency in comparison with prevalent solutions in pre-training and fine-tuning scenarios. Furthermore, we provide an empirical evaluation of the correlation between model size and GPU memory consumption under different optimization methods, as well as an analysis of the throughput. Lastly, we carry out a comprehensive comparison of various optimizers and PEFT methods within the instruction-tuning context. CoLLiE is available at https://***/OpenLMLab/collie. © 2023 Association for Computational Linguistics.

关键词： Efficiency

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Assessing "Implicit" Retrieval Robustness of Large language Models

Assessing "Implicit" Retrieval Robustness of Large Language ...

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

作者： Shen, Xiaoyu Blloshmi, Rexhina Zhu, Dawei Pei, Jiahuan Zhang, Wei Digital Twin Institute Eastern Institute of Technology Ningbo China Amazon AGI United States Saarland University Saarland Informatics Campus United States Vrije Universiteit Amsterdam Netherlands

ISBN: (纸本)9798891761643

Retrieval-augmented generation has gained popularity as a framework to enhance large language models with external knowledge. However, its effectiveness hinges on the retrieval robustness of the model. If the model lacks retrieval robustness, its performance is constrained by the accuracy of the retriever, resulting in significant compromises when the retrieved context is irrelevant. In this paper, we evaluate the "implicit" retrieval robustness of various large language models, instructing them to directly output the final answer without explicitly judging the relevance of the retrieved context. Our findings reveal that fine-tuning on a mix of gold and distracting context significantly enhances the model's robustness to retrieval inaccuracies, while still maintaining its ability to extract correct answers when retrieval is accurate. This suggests that large language models can implicitly handle relevant or irrelevant retrieved context by learning solely from the supervision of the final answer in an end-to-end manner. Introducing an additional process for explicit relevance judgment can be unnecessary and disrupts the end-to-end approach. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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"Flex Tape Can't Fix That": Bias and Misinformation in Edited language Models

"Flex Tape Can't Fix That": Bias and Misinformation in Edite...

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

作者： Halevy, Karina Sotnikova, Anna AlKhamissi, Badr Montariol, Syrielle Bosselut, Antoine École Polytechnique fédérale de Lausanne Switzerland Carnegie Mellon University United States University of Maryland College Park United States

ISBN: (纸本)9798891761643

Weight-based model editing methods update the parametric knowledge of language models post-training. However, these methods can unintentionally alter unrelated parametric knowledge representations, potentially increasing the risk of harm. In this work, we investigate how weight editing methods unexpectedly amplify model biases after edits. We introduce a novel benchmark dataset, SEESAW-CF, for measuring bias amplification of model editing methods for demographic traits such as race, geographic origin, and gender. We use SEESAW-CF to examine the impact of model editing on bias in five large language models. Our results demonstrate that edited models exhibit, to various degrees, more biased behavior for certain demographic groups than before they were edited, specifically becoming less confident in properties for Asian and African subjects. Additionally, editing facts about place of birth, country of citizenship, or gender has particularly negative effects on the model's knowledge about unrelated properties, such as field of work, a pattern observed across multiple models. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Somali Information Retrieval Corpus: Bridging the Gap between Query Translation and Dedicated language Resources

Somali Information Retrieval Corpus: Bridging the Gap betwee...

引用

conference on empirical methods in natural language processing (EMNLP)

作者： Badel, Abdisalam Mahamed Zhong, Ting Tai, Wenxin Zhou, Fan Univ Elect Sci & Technol China Chengdu Sichuan Peoples R China Kashi Inst Elect & Informat Ind Kashi Xinjiang Peoples R China

ISBN: (纸本)9798891760608

Despite the growing use of the Somali language in various online domains, research on Somali language information retrieval remains limited and primarily relies on query translation due to the lack of a dedicated corpus. To address this problem, we collaborated with language experts and natural language processing (NLP) researchers to create an annotated corpus for Somali information retrieval. This corpus comprises 2335 documents collected from various well-known online sites, such as hiiraan online, dhacdo net, and Somali poetry books. We explain how the corpus was constructed, and develop a Somali language information retrieval system using a pseudo-relevance feedback (PRF) query expansion technique on the corpus. Note that collecting such a data set for the low-resourced Somali language can help overcome NLP barriers, such as the lack of electronically available data sets. Which, if available, can enable the development of various NLP tools and applications such as question-answering and text classification. It also provides researchers with a valuable resource for investigating and developing new techniques and approaches for Somali.

关键词： Information retrieval

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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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Leading Whitespaces of language Models' Subword Vocabulary Pose a Confound for Calculating Word Probabilities

Leading Whitespaces of Language Models' Subword Vocabulary P...

引用

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

作者： Oh, Byung-Doh Schuler, William Center for Data Science New York University United States Department of Linguistics The Ohio State University United States

ISBN: (纸本)9798891761643

Predictions of word-by-word conditional probabilities from Transformer-based language models are often evaluated to model the incremental processing difficulty of human readers. In this paper, we argue that there is a confound posed by the most common method of aggregating subword probabilities of such language models into word probabilities. This is due to the fact that tokens in the subword vocabulary of most language models have leading whitespaces and therefore do not naturally define stop probabilities of words. We first prove that this can result in distributions over word probabilities that sum to more than one, thereby violating the axiom that P(Ω) = 1. This property results in a misallocation of word-by-word surprisal, where the unacceptability of the end of the current word is incorrectly carried over to the next word. Additionally, this implicit prediction of word boundaries incorrectly models psycholinguistic experiments where human subjects directly observe upcoming word boundaries. We present a simple decoding technique to reaccount the probability of the trailing whitespace into that of the current word, which resolves this confound. Experiments show that this correction reveals lower estimates of garden-path effects in transitive/intransitive sentences and poorer fits to naturalistic reading times. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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DVD: Dynamic Contrastive Decoding for Knowledge Amplification in Multi-Document Question Answering

DVD: Dynamic Contrastive Decoding for Knowledge Amplificatio...

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

作者： Jin, Jing Wang, Houfeng Zhang, Hao Li, Xiaoguang Guo, Zhijiang National Key Laboratory of Multimedia Information Processing School of Computer Science Peking University China Huawei Noah's Ark Lab Canada

ISBN: (纸本)9798891761643

Large language models (LLMs) are widely used in question-answering (QA) systems but often generate information with hallucinations. Retrieval-augmented generation (RAG) offers a potential remedy, yet the uneven retrieval quality and irrelevant contents may distract LLMs. In this work, we address these issues at the generation phase by treating RAG as a multi-document QA task. We propose a novel decoding strategy, Dynamic Contrastive Decoding (DVD), which dynamically amplifies knowledge from selected documents during the generation phase. DVD involves constructing inputs batchwise, designing new selection criteria to identify documents worth amplifying, and applying contrastive decoding with a specialized weight calculation to adjust the final logits used for sampling answer tokens. Zero-shot experimental results on ALCE-ASQA, NQ, TQA and PopQA benchmarks show that our method outperforms other decoding strategies. Additionally, we conduct experiments to validate the effectiveness of our selection criteria, weight calculation, and general multi-document scenarios. Our method requires no training and can be integrated with other methods to improve the RAG performance. Our codes will be publicly available at https://***/JulieJin-km/Dynamic_Contrastive_Decoding. © 2024 Association for Computational Linguistics.

关键词： Question answering

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***: Real-Time Multilingual Sign language Translation Application

***: Real-Time Multilingual Sign Language Translation Applic...

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

作者： Moryossef, Amit

ISBN: (纸本)9798891761674

This paper presents ***, an open-source application for real-time multilingual bidirectional translation between spoken and signed languages. Harnessing state-of-the-art open-source models, this tool aims to address the communication divide between the hearing and the deaf, facilitating seamless translation in both spoken-to-signed and signed-to-spoken translation directions. To provide reliable and unrestricted communication, *** offers offline functionality, crucial in areas with limited internet connectivity. It enhances user engagement by providing customizable photorealistic sign language avatars, encouraging a more personalized and authentic user experience. Licensed under CC BY-NC-SA 4.0, *** signifies an important stride towards open, inclusive communication. The app can be used and modified for personal and academic purposes and even supports a translation API, fostering integration into a wider range of applications. However, it is by no means a finished product. We invite the NLP community to contribute towards the evolution of ***. Whether it be the integration of more refined models, the development of innovative pipelines, or user experience improvements, your contributions can propel this project to new heights. Available at https://***, it stands as a testament to what we can achieve together, as we strive to make communication accessible to all. © 2024 Association for Computational Linguistics.

关键词： Open systems

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