Jiahui Geng


2024

pdf bib
OpenFactCheck: A Unified Framework for Factuality Evaluation of LLMs
Hasan Iqbal | Yuxia Wang | Minghan Wang | Georgi Nenkov Georgiev | Jiahui Geng | Iryna Gurevych | Preslav Nakov
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

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.

pdf bib
LLM-DetectAIve: a Tool for Fine-Grained Machine-Generated Text Detection
Mervat Abassy | Kareem Elozeiri | Alexander Aziz | Minh Ngoc Ta | Raj Vardhan Tomar | Bimarsha Adhikari | Saad El Dine Ahmed | Yuxia Wang | Osama Mohammed Afzal | Zhuohan Xie | Jonibek Mansurov | Ekaterina Artemova | Vladislav Mikhailov | Rui Xing | Jiahui Geng | Hasan Iqbal | Zain Muhammad Mujahid | Tarek Mahmoud | Akim Tsvigun | Alham Fikri Aji | Artem Shelmanov | Nizar Habash | Iryna Gurevych | Preslav Nakov
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

The ease of access to large language models (LLMs) has enabled a widespread of machine-generated texts, and now it is often hard to tell whether a piece of text was human-written or machine-generated. This raises concerns about potential misuse, particularly within educational and academic domains. Thus, it is important to develop practical systems that can automate the process. Here, we present one such system, LLM-DetectAIve, designed for fine-grained detection. Unlike most previous work on machine-generated text detection, which focused on binary classification, LLM-DetectAIve supports four categories: (i) human-written, (ii) machine-generated, (iii) machine-written, then machine-humanized, and (iv) human-written, then machine-polished. Category (iii) aims to detect attempts to obfuscate the fact that a text was machine-generated, while category (iv) looks for cases where the LLM was used to polish a human-written text, which is typically acceptable in academic writing, but not in education. Our experiments show that LLM-DetectAIve can effectively identify the above four categories, which makes it a potentially useful tool in education, academia, and other domains.LLM-DetectAIve is publicly accessible at https://github.com/mbzuai-nlp/LLM-DetectAIve. The video describing our system is available at https://youtu.be/E8eT_bE7k8c.

pdf bib
Reference-free Hallucination Detection for Large Vision-Language Models
Qing Li | Jiahui Geng | Chenyang Lyu | Derui Zhu | Maxim Panov | Fakhri Karray
Findings of the Association for Computational Linguistics: EMNLP 2024

Large vision-language models (LVLMs) have made significant progress in recent years. While LVLMs exhibit excellent ability in language understanding, question answering, and conversations of visual inputs, they are prone to producing hallucinations. While several methods are proposed to evaluate the hallucinations in LVLMs, most are reference-based and depend on external tools, which complicates their practical application. To assess the viability of alternative methods, it is critical to understand whether the reference-free approaches, which do not rely on any external tools, can efficiently detect hallucinations. Therefore, we initiate an exploratory study to demonstrate the effectiveness of different reference-free solutions in detecting hallucinations in LVLMs. In particular, we conduct an extensive study on three kinds of techniques: uncertainty-based, consistency-based, and supervised uncertainty quantification methods on four representative LVLMs across two different tasks. The empirical results show that the reference-free approaches are capable of effectively detecting non-factual responses in LVLMs, with the supervised uncertainty quantification method outperforming the others, achieving the best performance across different settings.

pdf bib
Factcheck-Bench: Fine-Grained Evaluation Benchmark for Automatic Fact-checkers
Yuxia Wang | Revanth Gangi Reddy | Zain Muhammad Mujahid | Arnav Arora | Aleksandr Rubashevskii | Jiahui Geng | Osama Mohammed Afzal | Liangming Pan | Nadav Borenstein | Aditya Pillai | Isabelle Augenstein | Iryna Gurevych | Preslav Nakov
Findings of the Association for Computational Linguistics: EMNLP 2024

The increased use of large language models (LLMs) across a variety of real-world applications calls for mechanisms to verify the factual accuracy of their outputs. In this work, we present Factcheck-Bench, a holistic end-to-end framework for annotating and evaluating the factuality of LLM-generated responses, which encompasses a multi-stage annotation scheme designed to yield detailed labels for fact-checking and correcting not just the final prediction, but also the intermediate steps that a fact-checking system might need to take. Based on this framework, we construct an open-domain factuality benchmark in three-levels of granularity: claim, sentence, and document. We further propose a system, Factcheck-GPT, which follows our framework, and we show that it outperforms several popular LLM fact-checkers. We make our annotation tool, annotated data, benchmark, and code available at https://github.com/yuxiaw/Factcheck-GPT.

pdf bib
A Survey of Confidence Estimation and Calibration in Large Language Models
Jiahui Geng | Fengyu Cai | Yuxia Wang | Heinz Koeppl | Preslav Nakov | Iryna Gurevych
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)

Large language models (LLMs) have demonstrated remarkable capabilities across a wide range of tasks in various domains. Despite their impressive performance, they can be unreliable due to factual errors in their generations. Assessing their confidence and calibrating them across different tasks can help mitigate risks and enable LLMs to produce better generations. There has been a lot of recent research aiming to address this, but there has been no comprehensive overview to organize it and to outline the main lessons learned. The present survey aims to bridge this gap. In particular, we outline the challenges and we summarize recent technical advancements for LLM confidence estimation and calibration. We further discuss their applications and suggest promising directions for future work.

2018

pdf bib
The RWTH Aachen University English-German and German-English Unsupervised Neural Machine Translation Systems for WMT 2018
Miguel Graça | Yunsu Kim | Julian Schamper | Jiahui Geng | Hermann Ney
Proceedings of the Third Conference on Machine Translation: Shared Task Papers

This paper describes the unsupervised neural machine translation (NMT) systems of the RWTH Aachen University developed for the English ↔ German news translation task of the EMNLP 2018 Third Conference on Machine Translation (WMT 2018). Our work is based on iterative back-translation using a shared encoder-decoder NMT model. We extensively compare different vocabulary types, word embedding initialization schemes and optimization methods for our model. We also investigate gating and weight normalization for the word embedding layer.

pdf bib
Improving Unsupervised Word-by-Word Translation with Language Model and Denoising Autoencoder
Yunsu Kim | Jiahui Geng | Hermann Ney
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Unsupervised learning of cross-lingual word embedding offers elegant matching of words across languages, but has fundamental limitations in translating sentences. In this paper, we propose simple yet effective methods to improve word-by-word translation of cross-lingual embeddings, using only monolingual corpora but without any back-translation. We integrate a language model for context-aware search, and use a novel denoising autoencoder to handle reordering. Our system surpasses state-of-the-art unsupervised translation systems without costly iterative training. We also analyze the effect of vocabulary size and denoising type on the translation performance, which provides better understanding of learning the cross-lingual word embedding and its usage in translation.