United States

We introduce \textbf{DAHL}, a benchmark dataset and automated evaluation system designed to assess hallucination in long-form text generation, specifically within the biomedical domain. Our benchmark dataset, meticulously curated from biomedical research papers, consists of 8,573 questions across 29 categories. DAHL evaluates fact-conflicting hallucinations in Large Language Models (LLMs) by deconstructing responses into atomic units, each representing a single piece of information. The accuracy of these responses is averaged to produce the \textit{DAHL Score}, offering a more in-depth evaluation of hallucinations compared to previous methods that rely on multiple-choice tasks. We conduct experiments with 8 different models, finding that larger models tend to hallucinate less; however, beyond a model size of 7 to 8 billion parameters, further scaling does not significantly improve factual accuracy. The \textit{DAHL Score} holds potential as an efficient alternative to human-annotated preference labels, being able to be expanded to other specialized domains. We release the dataset and code in [https://github.com/seemdog/DAHL].

EMNLP 2024

DAHL: Domain-specific Automated Hallucination Evaluation of Long-Form Text through a Benchmark Dataset in Biomedicine

hallucination evaluation

long-form text

biomedicine

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ACL 2024

The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. More information will be announced soon.

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Tables play a crucial role in conveying information in various domains. We propose a Plan-then-Reason framework to answer different types of user queries over tables with sentence context. The framework first plans the reasoning paths over the context, then assigns each step to program-based or textual reasoning to reach the final answer. This framework enhances the table reasoning abilities for both in-context learning and fine-tuning methods. GPT-3.5-Turbo following Plan-then-Reason framework surpasses other prompting baselines without self-consistency while using less API calls and in-context demonstrations. We also construct an instruction tuning set TrixInstruct to evaluate the effectiveness of fine-tuning with this framework. We present ProTrix model family by finetuning models on TrixInstruct. Our experiments show that ProTrix family generalizes to diverse unseen tabular tasks with only 6k training instances. We further demonstrate that ProTrix can generate accurate and faithful explanations to answer complex free-form questions. Our work underscores the importance of the planning and reasoning abilities towards a model over tabular tasks with generalizability and interpretability. We will open-source our dataset and models.

ProTrix: Building Models for Planning and Reasoning over Tables with Sentence Context

Misinformation on social media can stem from truthful news articles that are cited in misleading ways. In contrast to traditional media, the collaborative nature of information propagation on these platforms means that facts presented on posts are known second-hand or third-hand, and as such often contain transformations of information that may be misleading or incomplete, potentially leading to a propagation of falsehoods. As a first step in analyzing misinformation occurrences in information transfer scenarios on social media, we propose the task of classifying social media posts that quote news articles from the perspective of information transformation. Specifically, the task is composed of classifying the posts based on the faithfulness of factual claims and the degree to which the posts are misleading. On the basis of the proposed annotation criteria, 200 tweets (X posts) quoting news articles on COVID-19 were labeled by 3 annotators, with the result that 20% of posts were labeled as contained potentially misleading information concerning the article by a majority of annotators. We conduct exploratory experiments leveraging large language models (LMs) to classify information transformation with few-shot prompting, with performance on misleadingness classification approaching that of some human annotators.

Detecting Misleading News Representations on Social Media Posts

Detecting and tracking emerging trends and weak signals in large, evolving text corpora is vital for applications such as monitoring scientific literature, managing brand reputation, surveilling critical infrastructure and more generally to any kind of text-based event detection. Existing solutions often fail to capture the nuanced context or dynamically track evolving patterns over time. BERTrend, a novel method, addresses these limitations using neural topic modeling in an online setting. It introduces a new metric to quantify topic popularity over time by considering both the number of documents and update frequency. This metric classifies topics as noise, weak, or strong signals, flagging emerging, rapidly growing topics for further investigation. Experimentation on two large real-world datasets demonstrates BERTrend's ability to accurately detect and track meaningful weak signals while filtering out noise, offering a comprehensive solution for monitoring emerging trends in large-scale, evolving text corpora. The method can also be used for retrospective analysis of past events. In addition, the use of Large Language Models together with BERTrend offers efficient means for the interpretability of trends of events.

BERTrend: Neural Topic Modeling for Emerging Trends Detection

Event detection in text streams is a crucial task for the analysis of online media and social networks. One of the current challenges in this field is establishing a performance standard while maintaining an acceptable level of computational complexity. In our study, we use an incremental clustering algorithm combined with recent advancements in sentence embeddings. Our objective is to compare our findings with previous studies, specifically those by Cao et al. (2024) and Mazoyer et al. (2020). Our results demonstrate significant improvements and could serve as a relevant baseline for future research in this area.

An Incremental Clustering Baseline for Event Detection on Twitter

Language models (LMs) show exceptional promise in the area of few-shot event extraction, but they suffer from certain limitations. In particular, DEGREE (Hsu et al., 2022) is an LM-based event extraction model that has recently been supplanted by other large language model-based state-of-the-art systems, but it suffers from an inability to cope with multiple events in the same region of an input document. In this work, we present a simple method for extending this system with the ability to gracefully handle different densities of events within documents, thereby rendering it competitive with the state-of-the-art once more, and additionally explore a novel evaluation metric that can be used to qualitatively compare the outputs of different event extraction systems. Finally, we show that our extension allows models to break apart documents into less small pieces during processing without sacrificing accuracy.

DEGREE^2: Efficient Extraction of Multiple Events Using Language Models

Information extraction has led the way for event detection from text for many years. Recent advances in neural models, such as Large Language Models (LLMs) and Vision-Language Models (VLMs), have enabled the integration of multiple modalities, providing richer sources of information about events. Concurrently, the development of schema graphs and 3D reconstruction methods has enhanced our ability to visualize and annotate complex events. Building on these innovations, we introduce the MUMOSA (MUlti-MOdal Situation Awareness) interactive dashboard that brings these diverse resources together. MUMOSA aims to provide a comprehensive platform for event situational awareness, offering users a powerful tool for understanding and analyzing complex scenarios across modalities.

MUMOSA, Interactive Dashboard for MUlti-MOdal Situation Awareness

Language models (LMs) trained on web-scale datasets are largely successful due to their ability to memorize large amounts of training data, even if only present in a few examples. These capabilities are often desirable in evaluation on tasks such as question answering but raise questions about whether these models can exhibit genuine reasoning or succeed only at mimicking patterns from the training data. This distinction is particularly salient in forecasting tasks, where the answer is not present in the training data, and the model must reason to make logical deductions. We present Reasoning and Tools for Forecasting (RTF), a framework of reasoning-and-acting (ReAct) agents that can dynamically retrieve updated information and run numerical simulation with equipped tools. We evaluate our model with questions from competitive forecasting platforms and demonstrate that our method is competitive with and can outperform human predictions. This suggests that LMs, with the right tools, can indeed think and adapt like humans, offering valuable insights for real-world decision-making.

Reasoning and Tools for Human-Level Forecasting

Document-level information extraction (doc-IE) plays a pivotal role in the realm of natural language processing (NLP). This paper embarks on a comprehensive review and discussion of contemporary literature related to doc-IE. In addition, we conduct a thorough error analysis using state-of-the-art algorithms, shedding light on their limitations and remaining challenges for tackling the task of doc-IE. Our findings demonstrate that issues like entity coreference resolution and the lack of robust reasoning significantly hinder the effectiveness of document-level information extraction (doc-IE). Additionally, we uncover new challenges, including labeling noise and relation transitivity. The overarching objective of this survey paper is to provide valuable insights that can empower NLP researchers to further advance the performance of doc-IE.

A Comprehensive Survey on Document-Level Information Extraction

This paper aims to map out the current landscape of generative approaches to the task of event extraction. In surveying the emerging literature on the topic, we identify the distinctive properties of existing studies and catalogue them to build a comprehensive view of the various techniques employed. Finally, looking ahead, we argue for a new generative formulation of event extraction, allowing for a better fit between methodology and task - a proposal that could also pertain to many other traditional NLP tasks currently based on annotations of text-spans.

Generative Approaches to Event Extraction: Survey and Outlook

We introduce a neural architecture finetuned for the task of scenario context generation: The relevant location and time of an event or entity mentioned in text. Contextualizing information extraction helps to scope the validity of automated finings when aggregating them as knowledge graphs. Our approach uses a high-quality curated dataset of time and location annotations in a corpus of epidemiology papers to train an encoder-decoder architecture. We also explored the use of data augmentation techniques during training. Our findings suggest that a relatively small fine-tuned encoder-decoder model performs better than out-of-the-box LLMs and semantic role labeling parsers to accurate predict the relevant scenario information of a particular entity or event.

Next from EMNLP 2024

ProTrix: Building Models for Planning and Reasoning over Tables with Sentence Context

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