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Recently, work in NLP has shifted to few-shot (in-context) learning, with large language models (LLMs) performing well across a range of tasks. However, while fairness evaluations have become a standard for supervised methods, little is known about the fairness of LLMs as prediction systems. Further, common standard methods for fairness involve access to model weights or are applied during finetuning, which are not applicable in few-shot learning. Do LLMs exhibit prediction biases when used for standard NLP tasks?

In this work, we analyze the effect of shots, which directly affect the performance of models, on the fairness of LLMs as NLP classification systems. We consider how different shot selection strategies, both existing and new demographically sensitive methods, affect model fairness across three standard fairness datasets. We find that overall the performance of LLMs is not indicative of their fairness, and there is not a single method that fits all scenarios. In light of these facts, we discuss how future work can include LLM fairness in evaluations.

EMNLP 2024

Selecting Shots for Demographic Fairness in Few-Shot Learning with Large Language Models

in-context learning

social bias

fairness

ECAI2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI, including:




**An extensive programme of workshops, tutorials, and invited speakers**

**The Prestigious Applications of Intelligent Systems conference (PAIS)**

**The Frontiers in AI track sessions**

**The Lab To Market event**

**The Women in AI time**

**The EU Challenges forum**

… and many more!

ECAI 2020

ECAI 2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI.

## Welcome to EMNLP 2024! 
We are excited to welcome you to one of the most prominent conferences in the field of Natural Language Processing. This year, EMNLP 2024 is being held in a hybrid format,
offering both virtual and in-person participation in beautiful Miami. Due to a record-breaking number of submissions, we've expanded the total number of accepted papers to accommodate more cutting-edge research from around the globe.
### [Conference Handbook](https://drive.google.com/file/d/1WPROgxjLAC96AJL7Ugy0tEnYm7dkrbHt/view?usp=sharing)

You are required to register for this event. **Please register [here](https://2024.emnlp.org/registration/).** The EMNLP 2024 event page on Underline will be open to public one week prior to the event.

Please register!

EMNLP 2024 will take place in Miami, Florida from Nov 12th to Nov 16th, 2024, at the Hyatt Regency Miami Hote and on Underline for remote participants.

This study investigates the potential of Large Language Models (LLMs) for reconstructing and constructing the physical world solely based on textual knowledge. It explores the impact of model performance on spatial understanding abilities. To enhance the comprehension of geometric and spatial relationships in the complex physical world, the study introduces a set of geometric conventions and develops a workflow based on multi-layer graphs and multi-agent system frameworks. It examines how LLMs achieve multi-step and multi-objective geometric inference in a spatial environment using multi-layer graphs under unified geometric conventions. Additionally, the study employs a genetic algorithm, inspired by large-scale model knowledge, to solve geometric constraint problems. In summary, this work innovatively explores the feasibility of using text-based LLMs as physical world builders and designs a workflow to enhance their capabilities.

Navigate Complex Physical Worlds via Geometrically Constrained LLM

Communication barriers have long posed challenges for users of Alternate and Augmentative Communication (AAC). In AAC, effective conversational aids are not solely about harnessing Artificial Intelligence (AI) capabilities but more about ensuring these technologies resonate deeply with AAC user's unique communication challenges. We aim to bridge the gap between generic outputs and genuine human interactions by integrating advanced Conversational AI with personal narratives. While existing solutions offer generic responses, a considerable gap in tailoring outputs reflecting an AAC user's intent must be addressed. Thus, we propose to create a custom conversational dataset centered on the experiences and words of a primary AAC user to fine-tune advanced language models. Additionally, we employ a Retrieval-Augmented Generation (RAG) method, drawing context from a summarized version of authored content by the AAC user. This combination ensures that responses are contextually relevant and deeply personal. Preliminary evaluations underscore its transformative potential, with automated metrics and human assessments showcasing significantly enhanced response quality.

Empowering AAC Users: A Systematic Integration of Personal Narratives with Conversational AI

Product classification is a crucial task in international trade, as compliance regulations are verified and taxes and duties are applied based on product categories. Manual classification of products is time-consuming and error-prone, and the sheer volume of products imported and exported renders the manual process infeasible. Consequently, e-commerce platforms and enterprises involved in international trade have turned to automatic product classification using machine learning. However, current approaches do not consider the real-world challenges associated with product classification, such as very abbreviated and incomplete product descriptions. In addition, recent advancements in generative Large Language Models (LLMs) and their reasoning capabilities are mainly untapped in product classification and e-commerce. In this research, we explore the real-life challenges of industrial classification and we propose data perturbations that allow for realistic data simulation. Furthermore, we employ LLM-based product classification to improve the robustness of the prediction in presence of incomplete data. Our research shows that LLMs with in-context learning outperform the supervised approaches in the clean-data scenario. Additionally, we illustrate that LLMs are significantly more robust than the supervised approaches when data attacks are present.

LLM-Based Robust Product Classification in Commerce and Compliance

The success of large language models (LLMs) facilitate many parties to fine-tune LLMs on their own private data. However, this practice raises privacy concerns due to the memorization of LLMs. Existing solutions, such as utilizing synthetic data for substitution, struggle to simultaneously improve performance and preserve privacy. They either rely on a local model for generation, resulting in a performance decline, or take advantage of APIs, directly exposing the data to API servers. To address this issue, we propose KnowledgeSG, a novel client-server framework which enhances synthetic data quality and improves model performance while ensuring privacy. We achieve this by learning local knowledge from the private data with differential privacy (DP) and distilling professional knowledge from the server. Additionally, inspired by federated learning, we transmit models rather than data between the client and server to prevent privacy leakage. Extensive experiments in medical and financial domains demonstrate the effectiveness of KnowledgeSG. Our code is now publicly available at https://github.com/wwh0411/KnowledgeSG.

KnowledgeSG: Privacy-Preserving Synthetic Text Generation with Knowledge Distillation from Server

Our work studies Multilingual Federated Learning (FL), a decentralized paradigm that, although promising, grapples with issues such as client drift and suboptimal generalization in diverse, multilingual settings. We highlight limitations in existing approaches to generalize across both actively participating and inactive client language pairs. To mitigate these challenges, we introduce FedSparseNet, which incorporates sparse-network training, and LoRA, based on Low-Rank Adaptation. These approaches maintain the model's fidelity to its pretraining distribution, thereby ensuring robust performance on both seen and unseen language pairs, while simultaneously enhancing communication efficiency by selectively transmitting trainable parameters. Our empirical evaluations demonstrate that FedSparseNet outperforms conventional FL models on both seen and unseen clients, while LoRA shows remarkable improvements in unseen client performance. Additionally, we propose the Continuous Relative Robustness Metric, a novel metric to uniformly assess a model's performance across diverse language pairs

Less is Fed More: Sparsity Reduces Feature Distortion in Federated Learning

To address the challenges of Multimodal Fake News Detection (MFND), numerous Multimodal Small Language Models (MSLMs) have been developed. 
However, these models lack the breadth of knowledge, depth of language understanding, which results in unsatisfactory adaptability, generalization, and explainability performance.
Large Vision-Language Models (LVLMs) have shown significant performance in various tasks. Naturally, we hope to apply the common sense understanding and logical reasoning abilities of LVLMs to the task of MFND.
We found that LVLMs can generate reasonable analyses of the news from a specific perspective quite well.
However, when tasked with synthesizing these analyses to render a final judgment and explanations, their performance deteriorates significantly, failing to meet the accuracy standards set by existing detection models.
Based on these findings, we propose the Explainable Adaptive Rationales-Augmented Multimodal (EARAM) framework, which adaptively extracts useful rationales for news detection from the multi-perspective analyses of Retrieval-Augmented LVLMs through MSLMs.After making judgments based on these rationales, EARAM then assists LVLMs in generating more reliable explanations. Experiments demonstrate that our model not only achieves excellent results on widely used datasets but also significantly outperforms other models in terms of generalization and explainability.

From Predictions to Analyses: Explainable Rationales-Augmented Fake News Detection with Large Vision-Language Models

While various approaches have recently been studied for bias identification, little is known about how implicit language that does not explicitly convey a viewpoint affects bias amplification in large language models. To examine the severity of bias toward a view, we evaluated the performance of two downstream tasks where the implicit and explicit knowledge of social groups were used. First, we present a stress test evaluation by using a biased model in edge cases of excessive bias scenarios. Then, we evaluate how LLMs calibrate linguistically in response to both implicit and explicit opinions when they are aligned with conflicting viewpoints. 
Our findings reveal a discrepancy in LLM performance in identifying implicit and explicit opinions, with a general tendency of bias toward explicit opinions of opposing stances. Moreover, the bias-aligned models generate more cautious responses using uncertainty phrases compared to the unaligned (zero-shot) base models. The direct, incautious responses of the unaligned models suggest a need for further refinement of decisiveness by incorporating uncertainty markers to enhance their reliability, especially on socially nuanced topics with high subjectivity.

Covert Bias: The Severity of Social Views' Unalignment in Language Models Towards Implicit and Explicit Opinion

Technological advancements have enabled citizens to collectively share their opinions and report on election-related issues in real time. Systematically organizing (e.g., categorizing by attributes) and geotagging large amounts of crowdsourced information is crucial to ensuring that accurate and meaningful insights can be drawn from this data and used by policy makers to bring about positive change. However, these tasks typically require extensive manual efforts. In this paper we present Uchaguzi-2022, a dataset of 14k crowdsourced geotagged reports related to the 2022 Kenyan General Election. We use this dataset to investigate whether language models can assist in scalably categorizing and geotagging reports and show that they can achieve reasonable performance on these tasks.

Uchaguzi-2022: A Dataset of Citizen Reports on the 2022 Kenyan Election

Reference at the Heart of Natural Language Processing

The capabilities of AI in the realm of science span a wide spectrum, from the atomic level, where it solves partial differential equations for quantum systems, to the molecular level, predicting chemical or protein structures, and even extending to societal predictions like infectious disease outbreaks. Recent advancements in large language models (LLMs), exemplified by models like ChatGPT, have showcased significant prowess in tasks involving natural language, such as translating languages, constructing chatbots, and answering questions. When we consider scientific data, we notice a resemblance to natural language in terms of sequences – scientific literature and health records presented as text, bio-omics data arranged in sequences, or sensor data like brain signals. The question arises: Can we harness the potential of these recent LLMs to drive scientific progress? In this tutorial, we will explore the application of large language models to three crucial categories of scientific data: 1) textual data, 2) biomedical sequences, and 3) brain signals. Furthermore, we will delve into LLMs’ challenges in scientific research, including ensuring trustworthiness, achieving personalization, and adapting to multi-modal data representation.

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Navigate Complex Physical Worlds via Geometrically Constrained LLM

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Learning Fairness-aware Relational Structures

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Next from EMNLP 2024

Navigate Complex Physical Worlds via Geometrically Constrained LLM

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Learning Fairness-aware Relational Structures

Stay up to date with the latest Underline news!

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