pwc-archive/papers-with-abstracts huggingface dataset:
Assisted Debate Builder with Large Language Models
Authors: Elliot Faugier, Frédéric Armetta, Angela Bonifati, Bruno Yun
Date: 2024-05-14 00:00:00
Tasks: Argument Mining, Language Modeling, Language Modelling, Large Language Model, Relation

We introduce ADBL2, an assisted debate builder tool. It is based on the capability of large language models to generalise and perform relation-based argument mining in a wide-variety of domains. It is the first open-source tool that leverages relation-based mining for (1) the verification of pre-established relations in a debate and (2) the assisted creation of new arguments by means of large language models. ADBL2 is highly modular and can work with any open-source large language models that are used as plugins. As a by-product, we also provide the first fine-tuned Mistral-7B large language model for relation-based argument mining, usable by ADBL2, which outperforms existing approaches for this task with an overall F1-score of 90.59% across all domains.
The Evolution of Darija Open Dataset: Introducing Version 2
Authors: Aissam Outchakoucht, Hamza Es-Samaali
Date: 2024-05-14 00:00:00

Darija Open Dataset (DODa) represents an open-source project aimed at enhancing Natural Language Processing capabilities for the Moroccan dialect, Darija. With approximately 100,000 entries, DODa stands as the largest collaborative project of its kind for Darija-English translation. The dataset features semantic and syntactic categorizations, variations in spelling, verb conjugations across multiple tenses, as well as tens of thousands of translated sentences. The dataset includes entries written in both Latin and Arabic alphabets, reflecting the linguistic variations and preferences found in different sources and applications. The availability of such dataset is critical for developing applications that can accurately understand and generate Darija, thus supporting the linguistic needs of the Moroccan community and potentially extending to similar dialects in neighboring regions. This paper explores the strategic importance of DODa, its current achievements, and the envisioned future enhancements that will continue to promote its use and expansion in the global NLP landscape.
Using Combinatorial Optimization to Design a High quality LLM Solution
Authors: Samuel Ackerman, Eitan Farchi, Rami Katan, Orna Raz
Date: 2024-05-15 00:00:00
Tasks: AutoML, Combinatorial Optimization

We introduce a novel LLM based solution design approach that utilizes combinatorial optimization and sampling. Specifically, a set of factors that influence the quality of the solution are identified. They typically include factors that represent prompt types, LLM inputs alternatives, and parameters governing the generation and design alternatives. Identifying the factors that govern the LLM solution quality enables the infusion of subject matter expert knowledge. Next, a set of interactions between the factors are defined and combinatorial optimization is used to create a small subset $P$ that ensures all desired interactions occur in $P$. Each element $p \in P$ is then developed into an appropriate benchmark. Applying the alternative solutions on each combination, $p \in P$ and evaluating the results facilitate the design of a high quality LLM solution pipeline. The approach is especially applicable when the design and evaluation of each benchmark in $P$ is time-consuming and involves manual steps and human evaluation. Given its efficiency the approach can also be used as a baseline to compare and validate an autoML approach that searches over the factors governing the solution.
A survey on fairness of large language models in e-commerce: progress, application, and challenge
Authors: Qingyang Ren, Zilin Jiang, Jinghan Cao, Sijia Li, Chiqu Li, Yiyang Liu, Shuning Huo, Tiange He, Yuan Chen
Date: 2024-05-15 00:00:00
Tasks: Fairness, Survey

This survey explores the fairness of large language models (LLMs) in e-commerce, examining their progress, applications, and the challenges they face. LLMs have become pivotal in the e-commerce domain, offering innovative solutions and enhancing customer experiences. This work presents a comprehensive survey on the applications and challenges of LLMs in e-commerce. The paper begins by introducing the key principles underlying the use of LLMs in e-commerce, detailing the processes of pretraining, fine-tuning, and prompting that tailor these models to specific needs. It then explores the varied applications of LLMs in e-commerce, including product reviews, where they synthesize and analyze customer feedback; product recommendations, where they leverage consumer data to suggest relevant items; product information translation, enhancing global accessibility; and product question and answer sections, where they automate customer support. The paper critically addresses the fairness challenges in e-commerce, highlighting how biases in training data and algorithms can lead to unfair outcomes, such as reinforcing stereotypes or discriminating against certain groups. These issues not only undermine consumer trust, but also raise ethical and legal concerns. Finally, the work outlines future research directions, emphasizing the need for more equitable and transparent LLMs in e-commerce. It advocates for ongoing efforts to mitigate biases and improve the fairness of these systems, ensuring they serve diverse global markets effectively and ethically. Through this comprehensive analysis, the survey provides a holistic view of the current landscape of LLMs in e-commerce, offering insights into their potential and limitations, and guiding future endeavors in creating fairer and more inclusive e-commerce environments.
Leveraging Human Revisions for Improving Text-to-Layout Models
Authors: Amber Xie, Chin-Yi Cheng, Forrest Huang, Yang Li
Date: 2024-05-16 00:00:00

Learning from human feedback has shown success in aligning large, pretrained models with human values. Prior works have mostly focused on learning from high-level labels, such as preferences between pairs of model outputs. On the other hand, many domains could benefit from more involved, detailed feedback, such as revisions, explanations, and reasoning of human users. Our work proposes using nuanced feedback through the form of human revisions for stronger alignment. In this paper, we ask expert designers to fix layouts generated from a generative layout model that is pretrained on a large-scale dataset of mobile screens. Then, we train a reward model based on how human designers revise these generated layouts. With the learned reward model, we optimize our model with reinforcement learning from human feedback (RLHF). Our method, Revision-Aware Reward Models ($\method$), allows a generative text-to-layout model to produce more modern, designer-aligned layouts, showing the potential for utilizing human revisions and stronger forms of feedback in improving generative models.
DuetSim: Building User Simulator with Dual Large Language Models for Task-Oriented Dialogues
Authors: Xiang Luo, Zhiwen Tang, Jin Wang, Xuejie Zhang
Date: 2024-05-16 00:00:00
Tasks: Diversity, Response Generation, Task-Oriented Dialogue Systems

User Simulators play a pivotal role in training and evaluating task-oriented dialogue systems. Traditional user simulators typically rely on human-engineered agendas, resulting in generated responses that often lack diversity and spontaneity. Although large language models (LLMs) exhibit a remarkable capacity for generating coherent and contextually appropriate utterances, they may fall short when tasked with generating responses that effectively guide users towards their goals, particularly in dialogues with intricate constraints and requirements. This paper introduces DuetSim, a novel framework designed to address the intricate demands of task-oriented dialogues by leveraging LLMs. DuetSim stands apart from conventional approaches by employing two LLMs in tandem: one dedicated to response generation and the other focused on verification. This dual LLM approach empowers DuetSim to produce responses that not only exhibit diversity but also demonstrate accuracy and are preferred by human users. We validate the efficacy of our method through extensive experiments conducted on the MultiWOZ dataset, highlighting improvements in response quality and correctness, largely attributed to the incorporation of the second LLM. Our code is accessible at: https://github.com/suntea233/DuetSim.
Crowdsourcing with Enhanced Data Quality Assurance: An Efficient Approach to Mitigate Resource Scarcity Challenges in Training Large Language Models for Healthcare
Authors: P. Barai, G. Leroy, P. Bisht, J. M. Rothman, S. Lee, J. Andrews, S. A. Rice, A. Ahmed
Date: 2024-05-16 00:00:00
Tasks: Decision Making

Large Language Models (LLMs) have demonstrated immense potential in artificial intelligence across various domains, including healthcare. However, their efficacy is hindered by the need for high-quality labeled data, which is often expensive and time-consuming to create, particularly in low-resource domains like healthcare. To address these challenges, we propose a crowdsourcing (CS) framework enriched with quality control measures at the pre-, real-time-, and post-data gathering stages. Our study evaluated the effectiveness of enhancing data quality through its impact on LLMs (Bio-BERT) for predicting autism-related symptoms. The results show that real-time quality control improves data quality by 19 percent compared to pre-quality control. Fine-tuning Bio-BERT using crowdsourced data generally increased recall compared to the Bio-BERT baseline but lowered precision. Our findings highlighted the potential of crowdsourcing and quality control in resource-constrained environments and offered insights into optimizing healthcare LLMs for informed decision-making and improved patient care.
Can formal argumentative reasoning enhance LLMs performances?
Authors: Federico Castagna, Isabel Sassoon, Simon Parsons
Date: 2024-05-16 00:00:00

Recent years witnessed significant performance advancements in deep-learning-driven natural language models, with a strong focus on the development and release of Large Language Models (LLMs). These improvements resulted in better quality AI-generated output but rely on resource-expensive training and upgrading of models. Although different studies have proposed a range of techniques to enhance LLMs without retraining, none have considered computational argumentation as an option. This is a missed opportunity since computational argumentation is an intuitive mechanism that formally captures agents' interactions and the information conflict that may arise during such interplays, and so it seems well-suited for boosting the reasoning and conversational abilities of LLMs in a seamless manner. In this paper, we present a pipeline (MQArgEng) and preliminary study to evaluate the effect of introducing computational argumentation semantics on the performance of LLMs. Our experiment's goal was to provide a proof-of-concept and a feasibility analysis in order to foster (or deter) future research towards a fully-fledged argumentation engine plugin for LLMs. Exploratory results using the MT-Bench indicate that MQArgEng provides a moderate performance gain in most of the examined topical categories and, as such, show promise and warrant further research.
Enhancing Dialogue State Tracking Models through LLM-backed User-Agents Simulation
Authors: Cheng Niu, Xingguang Wang, Xuxin Cheng, Juntong Song, Tong Zhang
Date: 2024-05-17 00:00:00
Tasks: Dialogue State Tracking, Task-Oriented Dialogue Systems

Dialogue State Tracking (DST) is designed to monitor the evolving dialogue state in the conversations and plays a pivotal role in developing task-oriented dialogue systems. However, obtaining the annotated data for the DST task is usually a costly endeavor. In this paper, we focus on employing LLMs to generate dialogue data to reduce dialogue collection and annotation costs. Specifically, GPT-4 is used to simulate the user and agent interaction, generating thousands of dialogues annotated with DST labels. Then a two-stage fine-tuning on LLaMA 2 is performed on the generated data and the real data for the DST prediction. Experimental results on two public DST benchmarks show that with the generated dialogue data, our model performs better than the baseline trained solely on real data. In addition, our approach is also capable of adapting to the dynamic demands in real-world scenarios, generating dialogues in new domains swiftly. After replacing dialogue segments in any domain with the corresponding generated ones, the model achieves comparable performance to the model trained on real data.
Surgical Feature-Space Decomposition of LLMs: Why, When and How?
Authors: Arnav Chavan, Nahush Lele, Deepak Gupta
Date: 2024-05-17 00:00:00
Tasks: Language Modelling

Low-rank approximations, of the weight and feature space can enhance the performance of deep learning models, whether in terms of improving generalization or reducing the latency of inference. However, there is no clear consensus yet on \emph{how}, \emph{when} and \emph{why} these approximations are helpful for large language models (LLMs). In this work, we empirically study the efficacy of weight and feature space decomposition in transformer-based LLMs. We demonstrate that surgical decomposition not only provides critical insights into the trade-off between compression and language modelling performance, but also sometimes enhances commonsense reasoning performance of LLMs. Our empirical analysis identifies specific network segments that intrinsically exhibit a low-rank structure. Furthermore, we extend our investigation to the implications of low-rank approximations on model bias. Overall, our findings offer a novel perspective on optimizing LLMs, presenting the low-rank approximation not only as a tool for performance enhancements, but also as a means to potentially rectify biases within these models. Our code is available at \href{https://github.com/nyunAI/SFSD-LLM}{GitHub}.
Assessing Political Bias in Large Language Models
Authors: Luca Rettenberger, Markus Reischl, Mark Schutera
Date: 2024-05-17 00:00:00
Tasks: Text Generation

The assessment of bias within Large Language Models (LLMs) has emerged as a critical concern in the contemporary discourse surrounding Artificial Intelligence (AI) in the context of their potential impact on societal dynamics. Recognizing and considering political bias within LLM applications is especially important when closing in on the tipping point toward performative prediction. Then, being educated about potential effects and the societal behavior LLMs can drive at scale due to their interplay with human operators. In this way, the upcoming elections of the European Parliament will not remain unaffected by LLMs. We evaluate the political bias of the currently most popular open-source LLMs (instruct or assistant models) concerning political issues within the European Union (EU) from a German voter's perspective. To do so, we use the "Wahl-O-Mat," a voting advice application used in Germany. From the voting advice of the "Wahl-O-Mat" we quantize the degree of alignment of LLMs with German political parties. We show that larger models, such as Llama3-70B, tend to align more closely with left-leaning political parties, while smaller models often remain neutral, particularly when prompted in English. The central finding is that LLMs are similarly biased, with low variances in the alignment concerning a specific party. Our findings underline the importance of rigorously assessing and making bias transparent in LLMs to safeguard the integrity and trustworthiness of applications that employ the capabilities of performative prediction and the invisible hand of machine learning prediction and language generation.
ASIF: Coupled Data Turns Unimodal Models to Multimodal Without Training
Authors: Antonio Norelli, Marco Fumero, Valentino Maiorca, Luca Moschella, Emanuele Rodolà, Francesco Locatello
Date: 2022-10-04 00:00:00
Proceeding: NeurIPS 2023 11

CLIP proved that aligning visual and language spaces is key to solving many vision tasks without explicit training, but required to train image and text encoders from scratch on a huge dataset. LiT improved this by only training the text encoder and using a pre-trained vision network. In this paper, we show that a common space can be created without any training at all, using single-domain encoders (trained with or without supervision) and a much smaller amount of image-text pairs. Furthermore, our model has unique properties. Most notably, deploying a new version with updated training samples can be done in a matter of seconds. Additionally, the representations in the common space are easily interpretable as every dimension corresponds to the similarity of the input to a unique image-text pair in the multimodal dataset. Experiments on standard zero-shot visual benchmarks demonstrate the typical transfer ability of image-text models. Overall, our method represents a simple yet surprisingly strong baseline for foundation multimodal models, raising important questions on their data efficiency and on the role of retrieval in machine learning.
Appearance-based Gaze Estimation using Attention and Difference Mechanism
Authors: Murthy L.R.D., Pradipta Biswas
Date: 2021-06-01 00:00:00
Proceeding: CVPR 2021 6
Tasks: Gaze Estimation

Appearance-based gaze estimation problem received wide attention over the past few years. Even though model-based approaches existed earlier, availability of large datasets and novel deep learning techniques made appearance-based methods achieve superior accuracy than model-based approaches. In this paper, we proposed two novel techniques to improve gaze estimation accuracy. Our first approach, I2D-Net uses a difference layer to eliminate any common features from left and right eyes of a participant that are not pertinent to gaze estimation task. Our second approach, AGE-Net adapted the idea of attention mechanism and assigns weights to the features extracted from eye images. I2D-Net performed on par with the existing state-of-the-art approaches while AGE-Net reported state-of-the-art accuracy of 4.09◦ and 7.44◦ error on MPIIGaze and RT-Gene datasets respectively. We performed ablation studies to understand the effectiveness of the proposed approaches followed by analysis of gaze error distribution with respect to various factors of MPIIGaze dataset.
New Datasets for Automatic Detection of Textual Entailment and of Contradictions between Sentences in French
Authors: Maximos Skandalis, Richard Moot, Christian Retoré, Simon Robillard
Date: 2024-05-23 00:00:00
Proceeding: Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) 2024 5
Tasks: Natural Language Inference, RTE, Sentence, Sentence-Pair Classification, Text Pair Classification

This paper introduces DACCORD, an original dataset in French for automatic detection of contradictions between sentences. It also presents new, manually translated versions of two datasets, namely the well known dataset RTE3 and the recent dataset GQNLI, from English to French, for the task of natural language inference / recognising textual entailment, which is a sentence-pair classification task. These datasets help increase the admittedly limited number of datasets in French available for these tasks. DACCORD consists of 1034 pairs of sentences and is the first dataset exclusively dedicated to this task and covering among others the topic of the Russian invasion in Ukraine. RTE3-FR contains 800 examples for each of its validation and test subsets, while GQNLI-FR is composed of 300 pairs of sentences and focuses specifically on the use of generalised quantifiers. Our experiments on these datasets show that they are more challenging than the two already existing datasets for the mainstream NLI task in French (XNLI, FraCaS). For languages other than English, most deep learning models for NLI tasks currently have only XNLI available as a training set. Additional datasets, such as ours for French, could permit different training and evaluation strategies, producing more robust results and reducing the inevitable biases present in any single dataset.
On Robust Controlled Invariants for Continuous-time Monotone Systems
Authors: Emmanuel Junior Wafo Wembe, Adnane Saoud
Date: 2024-05-23 00:00:00

This paper delves into the problem of computing robust controlled invariants for monotone continuous-time systems, with a specific focus on lower-closed specifications. We consider the classes of state monotone (SM) and control-state monotone (CSM) systems, we provide the structural properties of robust controlled invariants for these classes of systems and show how these classes significantly impact the computation of invariants. Additionally, we introduce a notion of feasible points, demonstrating that their existence is sufficient to characterize robust controlled invariants for the considered class of systems. The study further investigates the necessity of reducing the feasibility condition for CSM and Lipschitz systems, unveiling conditions that guide this reduction. Leveraging these insights, we construct an algorithm for the computation of robust controlled invariants. To demonstrate the practicality of our approach, we applied the developed algorithm to the coupled tank problem.
Eliciting Informative Text Evaluations with Large Language Models
Authors: Yuxuan Lu, Shengwei Xu, Yichi Zhang, Yuqing Kong, Grant Schoenebeck
Date: 2024-05-23 00:00:00
Tasks: Multiple-choice, Prediction

Peer prediction mechanisms motivate high-quality feedback with provable guarantees. However, current methods only apply to rather simple reports, like multiple-choice or scalar numbers. We aim to broaden these techniques to the larger domain of text-based reports, drawing on the recent developments in large language models. This vastly increases the applicability of peer prediction mechanisms as textual feedback is the norm in a large variety of feedback channels: peer reviews, e-commerce customer reviews, and comments on social media. We introduce two mechanisms, the Generative Peer Prediction Mechanism (GPPM) and the Generative Synopsis Peer Prediction Mechanism (GSPPM). These mechanisms utilize LLMs as predictors, mapping from one agent's report to a prediction of her peer's report. Theoretically, we show that when the LLM prediction is sufficiently accurate, our mechanisms can incentivize high effort and truth-telling as an (approximate) Bayesian Nash equilibrium. Empirically, we confirm the efficacy of our mechanisms through experiments conducted on two real datasets: the Yelp review dataset and the ICLR OpenReview dataset. We highlight the results that on the ICLR dataset, our mechanisms can differentiate three quality levels -- human-written reviews, GPT-4-generated reviews, and GPT-3.5-generated reviews in terms of expected scores. Additionally, GSPPM penalizes LLM-generated reviews more effectively than GPPM.