GenAI4ELab/papercli-papers huggingface dataset:
torchdistill Meets Hugging Face Libraries for Reproducible, Coding-Free Deep Learning Studies: A Case Study on NLP
Authors: Yoshitomo Matsubara
Venue: EMNLP 2023 (Proceedings of the 3rd Workshop for Natural Language Processing Open Source Software (NLP-OSS 2023))

Abstract: Reproducibility in scientific work has been becoming increasingly important in research communities such as machine learning, natural language processing, and computer vision communities due to the rapid development of the research domains supported by recent advances in deep learning. In this work, we present a significantly upgraded version of torchdistill, a modular-driven coding-free deep learning framework significantly upgraded from the initial release, which supports only image classification and object detection tasks for reproducible knowledge distillation experiments. To demonstrate that the upgraded framework can support more tasks with third-party libraries, we reproduce the GLUE benchmark results of BERT models using a script based on the upgraded torchdistill, harmonizing with various Hugging Face libraries. All the 27 fine-tuned BERT models and configurations to reproduce the results are published at Hugging Face, and the model weights have already been widely used in research communities. We also reimplement popular small-sized models and new knowledge distillation methods and perform additional experiments for computer vision tasks.
Using Captum to Explain Generative Language Models
Authors: Vivek Miglani; Aobo Yang; Aram Markosyan; Diego Garcia-Olano; Narine Kokhlikyan
Venue: EMNLP 2023 (Proceedings of the 3rd Workshop for Natural Language Processing Open Source Software (NLP-OSS 2023))

Abstract: Captum is a comprehensive library for model explainability in PyTorch, offering a range of methods from the interpretability literature to enhance users’ understanding of PyTorch models. In this paper, we introduce new features in Captum that are specifically designed to analyze the behavior of generative language models. We provide an overview of the available functionalities and example applications of their potential for understanding learned associations within generative language models.
nerblackbox: A High-level Library for Named Entity Recognition in Python
Authors: Felix Stollenwerk
Venue: EMNLP 2023 (Proceedings of the 3rd Workshop for Natural Language Processing Open Source Software (NLP-OSS 2023))

Abstract: We present **nerblackbox**, a python library to facilitate the use of state-of-the-art transformer-based models for named entity recognition. It provides simple-to-use yet powerful methods to access data and models from a wide range of sources, for fully automated model training and evaluation as well as versatile model inference. While many technical challenges are solved and hidden from the user by default, **nerblackbox** also offers fine-grained control and a rich set of customizable features. It is thus targeted both at application-oriented developers as well as machine learning experts and researchers.
News Signals: An NLP Library for Text and Time Series
Authors: Chris Hokamp; Demian Ghalandari; Parsa Ghaffari
Venue: EMNLP 2023 (Proceedings of the 3rd Workshop for Natural Language Processing Open Source Software (NLP-OSS 2023))

Abstract: We present an open-source Python library for building and using datasets where inputs are clusters of textual data, and outputs are sequences of real values representing one or more timeseries signals. The news-signals library supports diverse data science and NLP problem settings related to the prediction of time series behaviour using textual data feeds. For example, in the news domain, inputs are document clusters corresponding to daily news articles about a particular entity, and targets are explicitly associated real-valued timeseries: the volume of news about a particular person or company, or the number of pageviews of specific Wikimedia pages. Despite many industry and research usecases for this class of problem settings, to the best of our knowledge, News Signals is the only open-source library designed specifically to facilitate data science and research settings with natural language inputs and timeseries targets. In addition to the core codebase for building and interacting with datasets, we also conduct a suite of experiments using several popular Machine Learning libraries, which are used to establish baselines for timeseries anomaly prediction using textual inputs.
PyTAIL: An Open Source Tool for Interactive and Incremental Learning of NLP Models with Human in the Loop for Online Data
Authors: Shubhanshu Mishra; Jana Diesner
Venue: EMNLP 2023 (Proceedings of the 3rd Workshop for Natural Language Processing Open Source Software (NLP-OSS 2023))

Abstract: Online data streams make training machine learning models hard because of distribution shift and new patterns emerging over time. For natural language processing (NLP) tasks that utilize a collection of features based on lexicons and rules, it is important to adapt these features to the changing data. To address this challenge we introduce PyTAIL, a python library, which allows a human in the loop approach to actively train NLP models. PyTAIL enhances generic active learning, which only suggests new instances to label by also suggesting new features like rules and lexicons to label. Furthermore, PyTAIL is flexible enough for users to accept, reject, or update rules and lexicons as the model is being trained. Finally, we simulate the performance of PyTAIL on existing social media benchmark datasets for text classification. We compare various active learning strategies on these benchmarks. The model closes the gap with as few as 10% of the training data. Finally, we also highlight the importance of tracking evaluation metric on remaining data (which is not yet merged with active learning) alongside the test dataset. This highlights the effectiveness of the model in accurately annotating the remaining dataset, which is especially suitable for batch processing of large unlabelled corpora. PyTAIL will be open sourced and available at https://github.com/socialmediaie/pytail.
Antarlekhaka: A Comprehensive Tool for Multi-task Natural Language Annotation
Authors: Hrishikesh Terdalkar; Arnab Bhattacharya
Venue: EMNLP 2023 (Proceedings of the 3rd Workshop for Natural Language Processing Open Source Software (NLP-OSS 2023))

Abstract: One of the primary obstacles in the advancement of Natural Language Processing (NLP) technologies for low-resource languages is the lack of annotated datasets for training and testing machine learning models. In this paper, we present Antarlekhaka, a tool for manual annotation of a comprehensive set of tasks relevant to NLP. The tool is Unicode-compatible, language-agnostic, Web-deployable and supports distributed annotation by multiple simultaneous annotators. The system sports user-friendly interfaces for 8 categories of annotation tasks. These, in turn, enable the annotation of a considerably larger set of NLP tasks. The task categories include two linguistic tasks not handled by any other tool, namely, sentence boundary detection and deciding canonical word order, which are important tasks for text that is in the form of poetry. We propose the idea of sequential annotation based on small text units, where an annotator performs several tasks related to a single text unit before proceeding to the next unit. The research applications of the proposed mode of multi-task annotation are also discussed. Antarlekhaka outperforms other annotation tools in objective evaluation. It has been also used for two real-life annotation tasks on two different languages, namely, Sanskrit and Bengali. The tool is available at https://github.com/Antarlekhaka/code
GPTCache: An Open-Source Semantic Cache for LLM Applications Enabling Faster Answers and Cost Savings
Authors: Fu Bang
Venue: EMNLP 2023 (Proceedings of the 3rd Workshop for Natural Language Processing Open Source Software (NLP-OSS 2023))

Abstract: The rise of ChatGPT1 has led to the development of artificial intelligence (AI) applications, particularly those that rely on large language models (LLMs). However, recalling LLM APIs can be expensive, and the response speed may slow down during LLMs’ peak times, causing frustration among developers. Potential solutions to this problem include using better LLM models or investing in more computing resources. However, these options may increase product development costs and decrease development speed. GPTCache2 is an open-source semantic cache that stores LLM responses to address this issue. When integrating an AI application with GPTCache, user queries are first sent to GPTCache for a response before being sent to LLMs like ChatGPT. If GPTCache has the answer to a query, it quickly returns the answer to the user without having to query the LLM. This approach saves costs on API recalls and makes response times much faster. For instance, integrating GPTCache with the GPT service offered by OpenAI can increase response speed 2-10 times when the cache is hit. Moreover, network fluctuations will not affect GPTCache’s response time, making it highly stable. This paper presents GPTCache and its architecture, how it functions and performs, and the use cases for which it is most advantageous.
trlX: A Framework for Large Scale Open Source RLHF
Authors: Louis Castricato
Venue: EMNLP 2023 (Proceedings of the 3rd Workshop for Natural Language Processing Open Source Software (NLP-OSS 2023))

Abstract: Reinforcement learning from human feedback (RLHF) utilizes human feedback to better align large language models with human preferences via online optimization against a learned reward model. Current RLHF paradigms rely on Proximal Policy Optimization (PPO), which quickly becomes a challenge to implement and scale up to large architectures. To address this difficulty we created the trlX library as a feature-complete open-source framework for RLHF fine-tuning of models up to and exceeding 70 billion parameters. We implemented support for multiple types of distributed training including distributed data parallel, model sharded, as well as tensor, sequential, and pipeline parallelism.
Towards Explainable and Accessible AI
Authors: Brandon Duderstadt; Yuvanesh Anand
Venue: EMNLP 2023 (Proceedings of the 3rd Workshop for Natural Language Processing Open Source Software (NLP-OSS 2023))

Abstract: Large language models (LLMs) have recently achieved human-level performance on a range of professional and academic benchmarks. Unfortunately, the explainability and accessibility of these models has lagged behind their performance. State-of-the-art LLMs require costly infrastructure, are only accessible via rate-limited, geo-locked, and censored web interfaces, and lack publicly available code and technical reports. Moreover, the lack of tooling for understanding the massive datasets used to train and produced by LLMs presents a critical challenge for explainability research. This talk will be an overview of Nomic AI’s efforts to address these challenges through its two core initiatives: GPT4All and Atlas
Nearest Neighbor Search over Vectorized Lexico-Syntactic Patterns for Relation Extraction from Financial Documents
Authors: Pawan Rajpoot; Ankur Parikh
Venue: EMNLP 2023 (Proceedings of the 2nd Workshop on Pattern-based Approaches to NLP in the Age of Deep Learning)

Abstract: Relation extraction (RE) has achieved remarkable progress with the help of pre-trained language models. However, existing RE models are usually incapable of handling two situations: implicit expressions and long-tail relation classes, caused by language complexity and data sparsity. Further, these approaches and models are largely inaccessible to users who don’t have direct access to large language models (LLMs) and/or infrastructure for supervised training or fine-tuning. Rule-based systems also struggle with implicit expressions. Apart from this, Real world financial documents such as various 10-X reports (including 10-K, 10-Q, etc.) of publicly traded companies pose another challenge to rule-based systems in terms of longer and complex sentences. In this paper, we introduce a simple approach that consults training relations at test time through a nearest-neighbor search over dense vectors of lexico-syntactic patterns and provides a simple yet effective means to tackle the above issues. We evaluate our approach on REFinD and show that our method achieves state-of-the-art performance. We further show that it can provide a good start for human in the loop setup when a small number of annotations are available and it is also beneficial when domain experts can provide high quality patterns. Our code is available at 1.
LEAF: Linguistically Enhanced Event Temporal Relation Framework
Authors: Stanley Lim; Da Yin; Nanyun Peng
Venue: EMNLP 2023 (Proceedings of the 2nd Workshop on Pattern-based Approaches to NLP in the Age of Deep Learning)

Abstract: Linguistic structures can implicitly imply diverse types of event relations that have been previously underexplored. For example, the sentence “John was cooking freshly made noodles for the family gathering” contains no explicit temporal indicators between the events, such as before. Despite this, it is easy for humans to conclude, based on syntax, that the noodles were made before John started cooking, and that the family gathering starts after John starts cooking. We introduce Linguistically enhanced Event TemporAl relation Framework (LEAF), a simple and effective approach to acquiring rich temporal knowledge of events from large-scale corpora. This method improves pre-trained language models by automatically extracting temporal relation knowledge from unannotated corpora using diverse temporal knowledge patterns. We begin by manually curating a comprehensive list of atomic patterns that imply temporal relations between events. These patterns involve event pairs in which one event is contained within the argument of the other. Using transitivity, we discover compositional patterns and assign labels to event pairs involving these patterns. Finally, we make language models learn the rich knowledge by pre-training with the acquired temporal relation supervision. Experiments show that our method outperforms or rivals previous models on two event relation datasets: MATRES and TB-Dense. Our approach is also simpler from past works and excels at identifying complex compositional event relations.
A Graph-Guided Reasoning Approach for Open-ended Commonsense Question Answering
Authors: Zhen Han; Yue Feng; Mingming Sun
Venue: EMNLP 2023 (Proceedings of the 2nd Workshop on Pattern-based Approaches to NLP in the Age of Deep Learning)

Abstract: Recently, end-to-end trained models for multiple-choice commonsense question answering (QA) have delivered promising results. However, such question-answering systems cannot be directly applied in real-world scenarios where answer candidates are not provided. Hence, a new benchmark challenge set for open-ended commonsense reasoning (OpenCSR) has been recently released, which contains natural science questions without any predefined choices. On the OpenCSR challenge set, many questions require implicit multi-hop reasoning and have a large decision space, reflecting the difficult nature of this task. Existing work on OpenCSR sorely focuses on improving the retrieval process, which extracts relevant factual sentences from a textual knowledge base, leaving the important and non-trivial reasoning task outside the scope. In this work, we extend the scope to include a reasoner that constructs a question-dependent open knowledge graph based on retrieved supporting facts and employs a sequential subgraph reasoning process to predict the answer. The subgraph can be seen as a concise and compact graphical explanation of the prediction. Experiments on two OpenCSR datasets show that the proposed model achieves great performance on benchmark OpenCSR datasets.
Generating Irish Text with a Flexible Plug-and-Play Architecture
Authors: Simon Mille; Elaine Uí Dhonnchadha; Lauren Cassidy; Brian Davis; Stamatia Dasiopoulou; Anya Belz
Venue: EMNLP 2023 (Proceedings of the 2nd Workshop on Pattern-based Approaches to NLP in the Age of Deep Learning)

Abstract: In this paper, we describe M-FleNS, a multilingual flexible plug-and-play architecture designed to accommodate neural and symbolic modules, and initially instantiated with rule-based modules. We focus on using M-FleNS for the specific purpose of building new resources for Irish, a language currently under-represented in the NLP landscape. We present the general M-FleNS framework and how we use it to build an Irish Natural Language Generation system for verbalising part of the DBpedia ontology and building a multilayered dataset with rich linguistic annotations. Via automatic and human assessments of the output texts we show that with very limited resources we are able to create a system that reaches high levels of fluency and semantic accuracy, while having very low energy and memory requirements.
Towards Zero-Shot Frame Semantic Parsing with Task Agnostic Ontologies and Simple Labels
Authors: Danilo Neves Ribeiro; Jack Goetz; Omid Abdar; Mike Ross; Annie Dong; Kenneth Forbus; Ahmed Mohamed
Venue: EMNLP 2023 (Proceedings of the 2nd Workshop on Pattern-based Approaches to NLP in the Age of Deep Learning)

Abstract: Frame semantic parsing is an important component of task-oriented dialogue systems. Current models rely on a significant amount training data to successfully identify the intent and slots in the user’s input utterance. This creates a significant barrier for adding new domains to virtual assistant capabilities, as creation of this data requires highly specialized NLP expertise. In this work we propose OpenFSP, a framework that allows for easy creation of new domains from a handful of simple labels that can be generated without specific NLP knowledge. Our approach relies on creating a small, but expressive, set of domain agnostic slot types that enables easy annotation of new domains. Given such annotation, a matching algorithm relying on sentence encoders predicts the intent and slots for domains defined by end-users. Experiments on the TopV2 dataset shows that our model trained on these simple labels have strong performance against supervised baselines.
Co-evolving data-driven and NLU-driven Synthesizers for Generating Code in Domain Growth and Data Scarcity
Authors: Jiasheng Gu; Zifan Nan; Zhiyuan Peng; Xipeng Shen; Dongkuan Xu
Venue: EMNLP 2023 (Proceedings of the 2nd Workshop on Pattern-based Approaches to NLP in the Age of Deep Learning)

Abstract: Natural language programming automatically generates code based on a user’s text query. Recent solutions are either data-driven or natural language understanding (NLU)-driven. However, the data-driven synthesizer requires a large number of query-code pairs for training, which hinders its application to low-resource programming languages with growing domains whose functionality and grammar can be actively updated. NLU-driven synthesizers solve this problem, but their code generation is slow and their performance rapidly saturates in the presence of ever-increasing data. In this paper, we propose a circular training framework, Colead, which co-evolves both the data-driven synthesizer and the NLU-driven synthesizer to achieve high-quality code generation in the presence of data scarcity and domain growth. The NLU-driven synthesizer generates query-code pairs to update the data-driven synthesizer, which shares a part of its updated model to improve the NLU-driven synthesizers, enabling the co-evolution of both. Experiments show that Colead gives better results than the baselines in the presence of domain growth and data scarcity, and Colead consistently improves the performance of both data-driven and NLU-driven synthesizers over the co-evolvement.
Complementary Roles of Inference and Language Models in QA
Authors: Liang Cheng; Mohammad Javad Hosseini; Mark Steedman
Venue: EMNLP 2023 (Proceedings of the 2nd Workshop on Pattern-based Approaches to NLP in the Age of Deep Learning)

Abstract: Answering open-domain questions through unsupervised methods poses challenges for both machine-reading (MR) and language model (LM) -based approaches. The MR-based approach suffers from sparsity issues in extracted knowledge graphs (KGs), while the performance of the LM-based approach significantly depends on the quality of the retrieved context for questions. In this paper, we compare these approaches and propose a novel methodology that leverages directional predicate entailment (inference) to address these limitations. We use entailment graphs (EGs), with natural language predicates as nodes and entailment as edges, to enhance parsed KGs by inferring unseen assertions, effectively mitigating the sparsity problem in the MR-based approach. We also show EGs improve context retrieval for the LM-based approach. Additionally, we present a Boolean QA task, demonstrating that EGs exhibit comparable directional inference capabilities to large language models (LLMs). Our results highlight the importance of inference in open-domain QA and the improvements brought by leveraging EGs.
Controlled Data Augmentation for Training Task-Oriented Dialog Systems with Low Resource Data
Authors: Sebastian Steindl; Ulrich Schäfer; Bernd Ludwig
Venue: EMNLP 2023 (Proceedings of the 2nd Workshop on Pattern-based Approaches to NLP in the Age of Deep Learning)

Abstract: Modern dialog systems rely on Deep Learning to train transformer-based model architectures. These notoriously rely on large amounts of training data. However, the collection of conversational data is often a tedious and costly process. This is especially true for Task-Oriented Dialogs, where the system ought to help the user achieve specific tasks, such as making reservations. We investigate a controlled strategy for dialog synthesis. Our method generates utterances based on dialog annotations in a sequence-to-sequence manner. Besides exploring the viability of the approach itself, we also explore the effect of constrained beam search on the generation capabilities. Moreover, we analyze the effectiveness of the proposed method as a data augmentation by studying the impact the synthetic dialogs have on training dialog systems. We perform the experiments in multiple settings, simulating various amounts of ground-truth data. Our work shows that a controlled generation approach is a viable method to synthesize Task-Oriented Dialogs, that can in turn be used to train dialog systems. We were able to improve this process by utilizing constrained beam search.