ymoslem/acl-abstracts huggingface dataset:
Monitoring Hate Speech in Indonesia: An NLP-based Classification of Social Media Texts (2024)

Online hate speech propagation is a complex issue, deeply influenced by both the perpetrator and the target`s cultural, historical, and societal contexts. Consequently, developing a universally robust hate speech classifier for diverse social media texts remains a challenging and unsolved task. The lack of mechanisms to track the spread and severity of hate speech further complicates the formulation of effective solutions. In response to this, to monitor hate speech in Indonesia during the recent 2024 presidential election, we have employed advanced Natural Language Processing (NLP) technologies to create an improved hate speech classifier tailored for a narrower subset of texts; specifically, texts that target vulnerable groups that have historically been the targets of hate speech in Indonesia. Our focus is on texts that mention these six vulnerable minority groups in Indonesia: Shia, Ahmadiyyah, Christians, LGBTQ+, Indonesian Chinese, and people with disabilities, as well as one additional group of interest: Jews. The insights gained from our dashboard have assisted stakeholders in devising more effective strategies to counteract hate speech. Notably, our dashboard has persuaded the General Election Supervisory Body in Indonesia (BAWASLU) to collaborate with our institution and the Alliance of Independent Journalists (AJI) to monitor social media hate speech in vulnerable areas in the country known for hate speech dissemination or hate-related violence in the upcoming Indonesian regional elections. This dashboard is available online at \url{https://aji.or.id/hate-speech-monitoring}.
CAVA: A Tool for Cultural Alignment Visualization \& Analysis (2024)

It is well-known that language models are biased; they have patchy knowledge of countries and cultures that are poorly represented in their training data. We introduce CAVA, a visualization tool for identifying and analyzing country-specific biases in language models.Our tool allows users to identify whether a language model successful captures the perspectives of people of different nationalities. The tool supports analysis of both longform and multiple-choice models responses and comparisons between models.Our open-source code easily allows users to upload any country-based language model generations they wish to analyze.To showcase CAVA`s efficacy, we present a case study analyzing how several popular language models answer survey questions from the World Values Survey.
ReDel: A Toolkit for LLM-Powered Recursive Multi-Agent Systems (2024)

Recently, there has been increasing interest in using Large Language Models (LLMs) to construct complex multi-agent systems to perform tasks such as compiling literature reviews, drafting consumer reports, and planning vacations. Many tools and libraries exist for helping create such systems, however none support *recursive* multi-agent systems{---}where the models themselves flexibly decide when to delegate tasks and how to organize their delegation structure. In this work, we introduce ReDel: a toolkit for recursive multi-agent systems that supports custom tool-use, delegation schemes, event-based logging, and interactive replay in an easy-to-use web interface. We show that, using ReDel, we are able to achieve significant performance gains on agentic benchmarks and easily identify potential areas of improvements through the visualization and debugging tools. Our code, documentation, and PyPI package are open-source at https://github.com/zhudotexe/redel, and free to use under the MIT license.
BattleAgent: Multi-modal Dynamic Emulation on Historical Battles to Complement Historical Analysis (2024)

This paper presents \textbf{BattleAgent}, a detailed emulation demonstration system that combines the Large Vision-Language Model (VLM) and Multi-Agent System (MAS). This novel system aims to emulate complex dynamic interactions among multiple agents, as well as between agents and their environments, over a period of time. The emulation showcases the current capabilities of agents, featuring fine-grained multi-modal interactions between agents and landscapes. It develops customizable agent structures to meet specific situational requirements, for example, a variety of battle-related activities like scouting and trench digging. These components collaborate to recreate historical events in a lively and comprehensive manner. This methodology holds the potential to substantially improve visualization of historical events and deepen our understanding of historical events especially from the perspective of decision making. The data and code for this project are accessible at \url{https://github.com/agiresearch/battleagent} and the demo is accessible at \url{https://drive.google.com/file/d/1I5B3KWiYCSSP1uMiPGNmXlTmild-MzRJ/view?usp=sharing}.
sign.mt: Real-Time Multilingual Sign Language Translation Application (2024)

This paper presents \textit{sign.mt}, an open-source application for real-time multilingual bi-directional 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, \textit{sign.mt} 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, \textit{sign.mt} 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 \textit{sign.mt}. 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 \url{https://sign.mt}, it stands as a testament to what we can achieve together, as we strive to make communication accessible to all.
WebOlympus: An Open Platform for Web Agents on Live Websites (2024)

Web agents are emerging as powerful tools capable of performing complex tasks across diverse web environments. The rapid development of large multimodal models is further enhancing this advancement. However, there is a lack of standardized and user-friendly tools for research and development, as well as experimental platforms on live websites. To address this challenge, we present WebOlympus, an open platform for web agents operating on live websites. WebOlympus offers a Chrome extension-based UI, enabling users without programming experience to easily utilize the platform. It allows users to run web agents with various designs using only a few lines of code or simple clicks on the Chrome extension. To ensure the trustworthiness of web agents, a safety monitor module that prevents harmful actions through human supervision or model-based control is incorporated. WebOlympus supports diverse applications, including annotation interfaces for web agent trajectories and data crawling.
TAIL: A Toolkit for Automatic and Realistic Long-Context Large Language Model Evaluation (2024)

As long-context large language models (LLMs) are attracting increasing attention for their ability to handle context windows exceeding 128k tokens, the need for effective evaluation methods for these models becomes 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 LLMs experience varyingdegrees of performance degradation as contextlengths increase.
OpenResearcher: Unleashing AI for Accelerated Scientific Research (2024)

The rapid growth of scientific literature imposes significant challenges for researchers endeavoring to stay updated with the latest advancements in their fields and delve into new areas. We introduce OpenResearcher, an innovative platform that leverages Artificial Intelligence (AI) techniques to accelerate the research process by answering diverse questions from researchers. OpenResearcher is built based on Retrieval-Augmented Generation (RAG) to integrate Large Language Models (LLMs) with up-to-date, domain-specific knowledge. Moreover, we develop various tools for OpenResearcher to understand researchers' queries, search from the scientific literature, filter retrieved information, provide accurate and comprehensive answers, and self-refine these answers. OpenResearcher can flexibly use these tools to balance efficiency and effectiveness. As a result, OpenResearcher enables researchers to save time and increase their potential to discover new insights and drive scientific breakthroughs. Demo, video, and code are available at: https://github.com/GAIR-NLP/OpenResearcher.
OpenFactCheck: A Unified Framework for Factuality Evaluation of LLMs (2024)

The increased use of large language models (LLMs) across a variety of real-world applications calls for automatic tools to check the factual accuracy of their outputs, as LLMs often hallucinate. This is difficult as it requires assessing the factuality of free-form open-domain responses. While there has been a lot of research on this topic, different papers use different evaluation benchmarks and measures,which makes them hard to compare and hampers future progress. To mitigate these issues, we developed OpenFactCheck, a unified framework, with three modules: (i) RESPONSEEVAL, which allows users to easily customize an automatic fact-checking system and to assess the factuality of all claims in an input document using that system, (ii) LLMEVAL, which assesses the overall factuality of an LLM, and (iii) CHECKEREVAL, a module to evaluate automatic fact-checking systems. OpenFactCheck is open-sourced (https://github.com/mbzuai-nlp/openfactcheck) and publicly released as a Python library (https://pypi.org/project/openfactcheck/) and also as a web service (http://app.openfactcheck.com). A video describing the system is available at https://youtu.be/-i9VKL0HleI.
ULLME: A Unified Framework for Large Language Model Embeddings with Generation-Augmented Learning (2024)

Large Language Models (LLMs) excel in various natural language processing tasks, but leveraging them for dense passage embedding remains challenging. This is due to their causal attention mechanism and the misalignment between their pre-training objectives and the text ranking tasks. Despite some recent efforts to address these issues, existing frameworks for LLM-based text embeddings have been limited by their support for only a limited range of LLM architectures and fine-tuning strategies, limiting their practical application and versatility. In this work, we introduce the Unified framework for Large Language Model Embedding (ULLME), a flexible, plug-and-play implementation that enables bidirectional attention across various LLMs and supports a range of fine-tuning strategies. We also propose Generation-augmented Representation Learning (GRL), a novel fine-tuning method to boost LLMs for text embedding tasks. GRL enforces consistency between representation-based and generation-based relevance scores, leveraging LLMs' powerful generative abilities for learning passage embeddings. To showcase our framework`s flexibility and effectiveness, we release three pre-trained models from ULLME with different backbone architectures, ranging from 1.5B to 8B parameters, all of which demonstrate strong performance on the Massive Text Embedding Benchmark. Our framework is publicly available at: https://github.com/nlp-uoregon/ullme. A demo video for ULLME can also be found at https://rb.gy/ws1ile.
To the Globe (TTG): Towards Language-Driven Guaranteed Travel Planning (2024)

Travel planning is a challenging and time-consuming task that aims to find an itinerary which satisfies multiple, interdependent constraints regarding flights, accommodations, attractions, and other travel arrangements. In this paper, we propose To the Globe (TTG), a real-time demo system that takes natural language requests from users, translates it to symbolic form via a fine-tuned Large Language Model, and produces optimal travel itineraries with Mixed Integer Linear Programming solvers. The overall system takes {\textasciitilde}5seconds to reply to the user request with guaranteed itineraries. To train TTG, we develop a synthetic data pipeline that generates userrequests, flight and hotel information in symbolic form without human annotations, based on the statistics of real-world datasets, and fine-tune an LLM to translate NL user requests to their symbolic form, which is sent to the symbolic solver to compute optimal itineraries. Our NL-symbolic translation achieves {\textasciitilde}91{\%} exact match in a backtranslation metric (i.e., whether the estimated symbolic form of generated natural language matches the groundtruth), and its returned itineraries have a ratio of 0.979 compared to the optimal cost of the ground truth user request. When evaluated by users, TTG achieves consistently high Net Promoter Scores (NPS) of 35-40{\%} on generated itinerary.
MATSA: Multi-Agent Table Structure Attribution (2024)

Large Language Models (LLMs) have significantly advanced QA tasks through in-context learning but often suffer from hallucinations. Attributing supporting evidence grounded in source documents has been explored for unstructured text in the past. However, tabular data present unique challenges for attribution due to ambiguities (e.g., abbreviations, domain-specific terms), complex header hierarchies, and the difficulty in interpreting individual table cells without row and column context. We introduce a new task, Fine-grained Structured Table Attribution (FAST-Tab), to generate row and column-level attributions supporting LLM-generated answers. We present MATSA, a novel LLM-based Multi-Agent system capable of post-hoc Table Structure Attribution to help users visually interpret factual claims derived from tables. MATSA augments tabular entities with descriptive context about structure, metadata, and numerical trends to semantically retrieve relevant rows and columns corresponding to facts in an answer. Additionally, we propose TabCite, a diverse benchmark designed to evaluate the FAST-Tab task on tables with complex layouts sourced from Wikipedia and business PDF documents. Extensive experiments demonstrate that MATSA significantly outperforms SOTA baselines on TabCite, achieving an 8-13{\%} improvement in F1 score. Qualitative user studies show that MATSA helps increase user trust in Generative AI by providing enhanced explainability for LLM-assisted table QA and enables professionals to be more productive by saving time on fact-checking LLM-generated answers.
OpenT2T: An Open-Source Toolkit for Table-to-Text Generation (2024)

Table data is pervasive in various industries, and its comprehension and manipulation demand significant time and effort for users seeking to extract relevant information. Consequently, an increasing number of studies have been directed towards table-to-text generation tasks. However, most existing methods are benchmarked solely on a limited number of datasets with varying configurations, leading to a lack of unified, standardized, fair, and comprehensive comparison between methods. This paper presents OpenT2T, the first open-source toolkit for table-to-text generation, designed to reproduce existing large language models (LLMs) for performance comparison and expedite the development of new models.We have implemented and compared a wide range of LLMs under zero- and few-shot settings on 9 table-to-text generation datasets, covering data insight generation, table summarization, and free-form table question answering. Additionally, we maintain a public leaderboard to provide insights for future work into how to choose appropriate table-to-text generation systems for real-world scenarios.
ChatHF: Collecting Rich Human Feedback from Real-time Conversations (2024)

We introduce ChatHF, an interactive annotation framework for chatbot evaluation, which integrates configurable annotation within a chat interface. ChatHF can be flexibly configured to accommodate various chatbot evaluation tasks, for example detecting offensive content, identifying incorrect or misleading information in chatbot responses, and chatbot responses that might compromise privacy. It supports post-editing of chatbot outputs and supports visual inputs, in addition to an optional voice interface. ChatHF is suitable for collection and annotation of NLP datasets, and Human-Computer Interaction studies, as demonstrated in case studies on image geolocation and assisting older adults with daily activities. ChatHF is publicly accessible at https://chat-hf.com.
KMatrix: A Flexible Heterogeneous Knowledge Enhancement Toolkit for Large Language Model (2024)

Knowledge-Enhanced Large Language Models (K-LLMs) system enhances Large Language Models (LLMs) abilities using external knowledge. Existing K-LLMs toolkits mainly focus on free-textual knowledge, lacking support for heterogeneous knowledge like tables and knowledge graphs, and fall short in comprehensive datasets, models, and user-friendly experience. To address this gap, we introduce KMatrix: a flexible heterogeneous knowledge enhancement toolkit for LLMs including verbalizing-retrieval and parsing-query methods. Our modularity and control-logic flow diagram design flexibly supports the entire lifecycle of various complex K-LLMs systems, including training, evaluation, and deployment. To assist K-LLMs system research, a series of related knowledge, datasets, and models are integrated into our toolkit, along with performance analyses of K-LLMs systems enhanced by different types of knowledge. Using our toolkit, developers can rapidly build, evaluate, and deploy their own K-LLMs systems.
Xinference: Making Large Model Serving Easy (2024)

The proliferation of open-source large models necessitates dedicated tools for deployment and accessibility. To mitigate the complexities of model serving, we develop Xinference, an open-source library designed to simplify the deployment and management of large models. Xinference effectively simplifies deployment complexities for users by (a) preventing users from writing code and providing built-in support for various models and OpenAI-compatible APIs; (b) enabling full model serving lifecycle management; (c) guaranteeing efficient and scalable inference and achieving high throughput and low latency. In comparative experiments with similar products like BentoML and Ray Serve, Xinference outperforms these tools and offers superior ease of use.Xinference is available at https://github.com/xorbitsai/inference.
RETAIN: Interactive Tool for Regression Testing Guided LLM Migration (2024)

Large Language Models (LLMs) are increasingly integrated into diverse applications. The rapid evolution of LLMs presents opportunities for developers to enhance applications continuously. However, this constant adaptation can also lead to performance regressions during model migrations. While several interactive tools have been proposed to streamline the complexity of prompt engineering, few address the specific requirements of regression testing for LLM Migrations. To bridge this gap, we introduce RETAIN (REgression Testing guided LLM migrAtIoN), a tool designed explicitly for regression testing in LLM Migrations. RETAIN comprises two key components: an interactive interface tailored to regression testing needs during LLM migrations, and an error discovery module that facilitates understanding of differences in model behaviors. The error discovery module generates textual descriptions of various errors or differences between model outputs, providing actionable insights for prompt refinement. Our automatic evaluation and empirical user studies demonstrate that RETAIN, when compared to manual evaluation, enabled participants to identify twice as many errors, facilitated experimentation with 75{\%} more prompts, and achieves 12{\%} higher metric scores in a given time frame.
ClaimLens: Automated, Explainable Fact-Checking on Voting Claims Using Frame-Semantics (2024)

We present ClaimLens, an automated fact-checking system focused on voting-related factual claims. Existing fact-checking solutions often lack transparency, making it difficult for users to trust and understand the reasoning behind the outcomes. In this work, we address the critical need for transparent and explainable automated fact-checking solutions. We propose a novel approach that leverages frame-semantic parsing to provide structured and interpretable fact verification. By focusing on voting-related claims, we can utilize publicly available voting records from official United States congressional sources and the established Vote semantic frame to extract relevant information from claims. Furthermore, we propose novel data augmentation techniques for frame-semantic parsing, a task known to lack robust annotated data, which leads to a +9.5{\%} macro F1 score on frame element identification over our baseline.
RAGViz: Diagnose and Visualize Retrieval-Augmented Generation (2024)

Retrieval-augmented generation (RAG) combines knowledge from domain-specific sources into large language models to ground answer generation. Current RAG systems lack customizable visibility on the context documents and the model`s attentiveness towards such documents. We propose RAGViz, a RAG diagnosis tool that visualizes the attentiveness of the generated tokens in retrieved documents. With a built-in user interface, retrieval index, and Large Language Model (LLM) backbone, RAGViz provides two main functionalities: (1) token and document-level attention visualization, and (2) generation comparison upon context document addition and removal. As an open-source toolkit, RAGViz can be easily hosted with a custom embedding model and HuggingFace-supported LLM backbone. Using a hybrid ANN (Approximate Nearest Neighbor) index, memory-efficient LLM inference tool, and custom context snippet method, RAGViz operates efficiently with a median query time of about 5 seconds on a moderate GPU node. Our code is available at https://github.com/cxcscmu/RAGViz. A demo video of RAGViz can be found at https://youtu.be/cTAbuTu6ur4.