Mexico

In this paper, we present our work to the MEDIQA-M3G 2024 shared task, which tackles multilingual and multimodal medical answer generation. Our system consists of a lightweight Vision-and-Language Transformer (ViLT) model which is fine-tuned for the clinical dermatology visual question-answering task. In the official leaderboard for the task, our system ranks 6th. After the challenge, we experiment with training the ViLT model on more data. We also explore the capabilities of large Vision-Language Models (VLMs) such as Gemini and LLaVA.

NAACL 2024

LTRC-IIITH at MEDIQA-M3G 2024: Medical Visual Question Answering with Vision-Language Models

gemini

vilt

visual question-answering

llava

vision-language models

dermatology

healthcare

## Welcome to 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics!
This year, the conference is in Mexico City. NAACL was actually already planned for Mexico City in 2021, but due to the pandemic the entire conference was moved online. This year, finally, we get to go! So it is my sincere pleasure to welcome you to Mexico City, whether in person or virtually. Having the conference in Mexico City is a good opportunity to emphasize that NAACL is our flagship conference for ACL members not only in North America but also in Central and South America, even though NAACL has been bearing “North” in its name. At this year’s conference, we have a theme to match, with a theme track on the Languages of Latin America to showcase the linguistic diversity of the region.
 
The opportunity to present at NAACL should not depend on a researcher’s travel budget, or their family status. This is why it is so important to make virtual participation at NAACL as good an experience as possible – but we want to also provide a good experience for in-person participants. As a community, we are still working out the best way to do that. This year at NAACL, we are trying out a big virtual poster session ahead of the conference, with the hope that this will make make for a lively and interactive experience. At the same time, we are reducing virtual oral presentations, which seem to be particularly tricky to make to work well. A big thanks to the NAACL program chairs and to Luciana Benotti for all their ideas and work to improve the virtual experience. And participants, virtual as well as in-person: Please let us know what worked for you and what didn’t, so we can continue to improve hybrid conferences. 
 
I have been lucky to work with many amazing people. Without their insight, dedication and patience, and without the many hours of work they put in, NAACL would not have been possible. A huge thank you to the program chairs Helena Gomez, Kevin Duh, and Steve Bethard – you are the best! 
 
Finally, I would like to thank all authors, invited speakers and panelists, area chairs and reviewers, the volunteers organizing and chairing sessions, and all attendees, in-person and virtual. Thank you for helping us make NAACL 2024 come to life. 
 
Welcome and hope you all enjoy the conference! 
Katrin Erk 
The University of Texas at Austin 
NAACL-2024 General Chair 
*You can read the full Welcome message in the [Conference Handbook (downloadable)](https://drive.google.com/file/d/1H1NvW0VASQjkSCYgw3mr-3l4yYDSxz6B/view?usp=sharing)*

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2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics

workshop paper

The introduction of conversational systems have made synthesized speech technologies common tools for daily activities. However, not all synthetic speech systems are designed with the needs of people with disabilities in mind. This paper describes a study in which 198 people -- 80 participants with self-reported disabilities and 118 participants without -- were recruited to listen to navigation instructions from a spoken dialogue system with different prosodic features. Results showed that slowing down speech rate aids in participants' number recall, but not in noun recall. From our results, we provide suggestions for developers for building accessible synthetic speech systems.

Examining Prosody in Spoken Navigation Instructions for People with Disabilities

The self-rationalising capabilities of large language models (LLMs) have been explored in restricted settings, using task-specific data sets. However, current LLMs do not (only) rely on specifically annotated data; nonetheless, they frequently explain their outputs. The properties of the generated explanations are influenced by the pre-training corpus and by the target data used for instruction fine-tuning. As the pre-training corpus includes a large amount of human-written explanations ``in the wild'', we hypothesise that LLMs adopt common properties of human explanations. By analysing the outputs for a multi-domain instruction fine-tuning data set, we find that generated explanations show selectivity and contain illustrative elements, but less frequently are subjective or misleading. We discuss reasons and consequences of the properties' presence or absence. In particular, we outline positive and negative implications depending on the goals and user groups of the self-rationalising system.

Properties and Challenges of LLM-Generated Explanations

When users want to write a story with a language model (LM) assistant, it is often very difficult to provide a prompt that clearly specifies all their interests. For the developers of LM assistants, it is also difficult to ensure their output stories come from a dataset without copyright concerns. Motivated by these limitations, we propose a coarse-to-fine (C2F) tree-based story generation framework, which is called C2F-StoryTree, where the LM iteratively generates more and more specific story prompts based on a user's input prompt and the desired plot selected by the user. To realize our C2F-StoryTree framework, we propose an entailment hierarchy (EH) text structure, in which a more specific response entails more general prompt (e.g., a story entails a summary). We also propose novel annotation tasks, decoding methods, and a human-and-machine-in-the-loop procedure to minimize the annotation cost of building the text structure. We build an entailment hierarchy dataset on top of the story datasets with desired licenses and styles, on which the developers can fine-tune or evaluate their LMs. In our experiments, we demonstrate that our C2F-StoryTree system not only allows users to collaborate with a LM in a new paradigm, but also generates the short stories with human-level quality. Furthermore, our entailment hierarchy and C2F generation substantially make the generated stories more diverse, coherent, engaging, and creative.

Fine-to-Coarse Entailment Hierarchy Construction for Coarse-to-Fine Story Generation

In the realm of research support tools, there exists a notable void in resources tailored specifically for aiding researchers during the crucial ideation phase of the research life-cycle. We address this gap by introducing 'Acceleron', a `Co-Pilot' for researchers, designed specifically to accelerate the ideation phase of the research life-cycle. Leveraging the reasoning and domain-specific skills of Large Language Models (LLMs) within an agent-based architecture with distinct personas, Acceleron aids researchers through the formulation of a comprehensive research proposals. It emulates the ideation process, engaging researchers in an interactive fashion to validate the novelty of the proposal and generate plausible set-of hypotheses. Notably, it addresses challenges inherent in LLMs, such as hallucinations, implements a two-stage aspect-based retrieval to manage precision-recall trade-offs, and tackles issues of unanswerability. Our observations and end-user evaluations illustrate the efficacy of Acceleron as an enhancer of researcher's productivity.

An Interactive Co-Pilot for Accelerated Research Ideation

In times of crisis, the human mind is often a voracious information forager. It might not be immediately apparent what one wants or needs, and people frequently look for answers to their most pressing questions and worst fears. In that context, the pandemic has demonstrated that social media sources, like erstwhile Twitter, are a rich medium for data-driven communication between experts and the public. However, as lay users, we must find needles in a haystack to distinguish credible and actionable information signals from the noise. In this work, we leverage the literature on crisis communication to propose an AI-driven sensemaking model that bridges the gap between what people seek and what they need during a crisis. Our model learns to contrast social media messages concerning expert guidance with subjective opinion and enables semantic interpretation of message characteristics based on the communicative intent of the message author. We provide examples from our tweet collection and present a hypothetical social media usage scenario to demonstrate the efficacy of our proposed model.

Sensemaking of Socially-Mediated Crisis Information

With the rapid proliferation of artificial intelligence, there is growing concern over its potential to exacerbate existing biases and societal disparities and introduce novel ones. This issue has prompted widespread attention from academia, policymakers, industry, and civil society. While evidence suggests that integrating human perspectives can mitigate bias-related issues in AI systems, it also introduces challenges associated with cognitive biases inherent in human decision-making. Our research focuses on reviewing existing methodologies and ongoing investigations aimed at understanding annotation attributes that contribute to bias.

Blind Spots and Biases: Exploring the Role of Annotator Cognitive Biases in NLP

Interpretability tools that offer explanations in the form of a dialogue have demonstrated their efficacy in enhancing users' understanding (Slack et al., 2023; Shen et al., 2023), as one-off explanations may fall short in providing sufficient information to the user. Current solutions for dialogue-based explanations, however, often require external tools and modules and are not easily transferable to tasks they were not designed for. With LLMCheckup, we present an easily accessible tool that allows users to chat with any state-of-the-art large language model (LLM) about its behavior. We enable LLMs to generate explanations and perform user intent recognition without fine-tuning, by connecting them with a broad spectrum of Explainable AI (XAI) methods, including white-box explainability tools such as feature attributions, and self-explanations (e.g., for rationale generation). LLM-based (self-)explanations are presented as an interactive dialogue that supports follow-up questions and generates suggestions. LLMCheckup provides tutorials for operations available in the system, catering to individuals with varying levels of expertise in XAI and supporting multiple input modalities. We introduce a new parsing strategy that substantially enhances the user intent recognition accuracy of the LLM. Finally, we showcase for the tasks of fact checking and commonsense question answering.

LLMCheckup: Conversational Examination of Large Language Models via Interpretability Tools and Self-Explanations

Significant progress has been made on text generation by pre-trained language models (PLMs), yet distinguishing between human and machine-generated text poses an escalating challenge. This paper offers an in-depth evaluation of three distinct methods used to address this task: traditional shallow learning, Language Model (LM) fine-tuning, and Multilingual Model fine-tuning. These approaches are rigorously tested on a wide range of machine-generated texts, providing a benchmark of their competence in distinguishing between human-authored and machine-authored linguistic constructs. The results reveal considerable differences in performance across methods, thus emphasizing the continued need for advancement in this crucial area of NLP. This study offers valuable insights and paves the way for future research aimed at creating robust and highly discriminative models.

Beyond Turing: A Comparative Analysis of Approaches for Detecting Machine-Generated Text

Safety classifiers are critical in mitigating toxicity on online forums such as social media and in chatbots. 
Still, they continue to be vulnerable to emergent, and often innumerable, adversarial attacks.
Traditional automated adversarial data generation methods, however, tend to produce attacks that are not diverse, but variations of previously observed harm types.
We formalize the task of automated adversarial discovery for safety classifiers - to find new attacks along previously unseen harm dimensions that expose new weaknesses in the classifier.
We measure progress on this task along two key axes (1) adversarial success: does the attack fool the classifier? and (2) dimensional diversity: does the attack represent a previously unseen harm type?
Our evaluation of existing attack generation methods on the CivilComments toxicity task reveals their limitations: Word perturbation attacks fail to fool classifiers, while prompt-based LLM attacks have more adversarial success, but lack dimensional diversity.
Even our best-performing prompt-based method finds new successful attacks on unseen harm dimensions of attacks only 5\% of the time.
Automatically finding new harmful dimensions of attack is crucial and there is substantial headroom for future research on our new task.

Automated Adversarial Discovery for Safety Classifiers

Current natural language processing (NLP) research tends to focus on only one or, less frequently, two dimensions -- e.g., performance, interpretability, or efficiency -- at a time, which may lead to suboptimal conclusions. Work on adapter modules
focuses on improving performance and efficiency, with no investigation of unintended consequences on other aspects such as fairness. To address this gap, we conduct experiments on three text classification datasets by either (1) finetuning all parameters or (2) using adapter modules. Regarding performance and efficiency, we confirm prior findings that the accuracy of adapter-enhanced models is roughly on par with that of fully finetuned models, while training time is substantially reduced. Regarding fairness, we show that adapter modules result in mixed fairness across sensitive groups. Further investigation reveals that, when the standard finetuned model exhibits limited biases, adapter modules typically do not introduce extra bias. On the other hand, when the finetuned model exhibits increased bias, the use of adapter modules poses the potential danger of amplifying these biases to a significant extent. Our findings highlight the need for a case-by-case evaluation rather than a one-size-fits-all judgment.

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Next from NAACL 2024

Examining Prosody in Spoken Navigation Instructions for People with Disabilities

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IKIM at MEDIQA-M3G 2024: Multilingual Visual Question-Answering for Dermatology through VLM Fine-tuning and LLM Translations

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