Papers by Yuxia Wang

M4: Multi-generator, Multi-domain, and Multi-lingual Black-Box Machine-Generated Text Detection (2024.eacl-long)

Challenge:	Existing methods to regularise noisy labels are ineffective in the face of noisy data.
Approach:	They propose a method that regularises noisy labels and prevents error propagation from the input layer.
Outcome:	The proposed method regularises noisy labels and improves generalisation performance over real-world human-disagreement annotations and randomly-corrupted and data-augmented labels.

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Yuxia Wang, Jonibek Mansurov, Petar Ivanov, Jinyan Su, Artem Shelmanov, Akim Tsvigun, Chenxi Whitehouse, Osama Mohammed Afzal, Tarek Mahmoud, Toru Sasaki, Thomas Arnold, Alham Aji, Nizar Habash, Iryna Gurevych, Preslav Nakov

Text Generation

Challenge:	Large language models generate fluent responses to user queries, but they are also susceptible to misuse in journalism, education, and academia.
Approach:	They propose a large-scale benchmark for machine-generated text detection that is a multi-generator, multi-domain, and multi-lingual corpus.
Outcome:	The proposed system can detect machine-generated text and pinpoint misuse . the proposed system is based on a large-scale benchmark dataset .

Zero-shot Text Classification via Reinforced Self-training (2020.acl-main)

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Zhiquan Ye, Yuxia Geng, Jiaoyan Chen, Jingmin Chen, Xiaoxiao Xu, SuHang Zheng, Feng Wang, Jun Zhang, Huajun Chen

Low-Resource NLP

Challenge:	Existing methods to learn from unlabeled data are difficult for zero-shot text classification tasks.
Approach:	They propose a self-training based method to efficiently leverage unlabeled data.
Outcome:	The proposed method significantly outperforms existing methods in zero-shot text classification tasks on benchmarks and a real-world e-commerce dataset.

Qorǵau: Evaluating Safety in Kazakh-Russian Bilingual Contexts (2025.findings-acl)

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Maiya Goloburda, Nurkhan Laiyk, Diana Turmakhan, Yuxia Wang, Mukhammed Togmanov, Jonibek Mansurov, Askhat Sametov, Nurdaulet Mukhituly, Minghan Wang, Daniil Orel, Zain Muhammad Mujahid, Fajri Koto, Timothy Baldwin, Preslav Nakov

Multilinguality

Challenge:	Large language models (LLMs) have the potential to generate harmful content, posing risks to users.
Approach:	They propose a dataset specifically designed for safety evaluation in Kazakh and Russian . they use a bilingual context in Kazakhstan where both Kazakh (a low-resource language) and Russian (a high-resourced language)
Outcome:	The proposed dataset is designed for safety evaluation in Kazakh and Russian . it shows that both multilingual and language-specific LLMs perform better than others .

A Chinese Dataset for Evaluating the Safeguards in Large Language Models (2024.findings-acl)

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Yuxia Wang, Zenan Zhai, Haonan Li, Xudong Han, Shom Lin, Zhenxuan Zhang, Angela Zhao, Preslav Nakov, Timothy Baldwin

FAID: Fine-grained AI-generated Text Detection using Multi-task Auxiliary and Multi-level Contrastive Learning (2026.eacl-long)

Challenge:	a recent study has shown that large language models can produce harmful responses, exposing users to unexpected risks.
Approach:	They propose a dataset for the safety evaluation of Chinese LLMs in Mandarin Chinese . they extend the dataset to better identify false negative and false positive examples .
Outcome:	The proposed dataset is for the safety evaluation of Chinese LLMs, and is based on a Chinese dataset.

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Minh Ngoc Ta, Dong Cao Van, Duc-Anh Hoang, Minh Le-Anh, Truong Nguyen, My Anh Tran Nguyen, Yuxia Wang, Preslav Nakov, Dinh Viet Sang

A Survey of Confidence Estimation and Calibration in Large Language Models (2024.naacl-long)

Challenge:	Existing binary detection frameworks for human-written, LLM-generated and human-LLM collaborative texts are challenging . a recent study focused on binary detection, i.e., human vs. LLM, or on fine-grained detection limited to English.
Approach:	They propose a fine-grained detection framework to classify text into three categories . they use multilingual datasets and a multi-domain, multi-generator dataset .
Outcome:	The proposed framework outperforms baselines on unseen domains and new LLMs.

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Jiahui Geng, Fengyu Cai, Yuxia Wang, Heinz Koeppl, Preslav Nakov, Iryna Gurevych

Rethinking STS and NLI in Large Language Models (2024.findings-eacl)

Challenge:	Large language models (LLMs) have demonstrated impressive capabilities across a wide range of tasks in various domains, but they can be unreliable due to factual errors in their generations.
Approach:	They summarize recent advances in LLM confidence estimation and calibration and outline their main lessons learned.
Outcome:	The proposed methods can be used to assess the reliability of models and to calibrate them across tasks.

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Yuxia Wang, Minghan Wang, Preslav Nakov

OpenFactCheck: Building, Benchmarking Customized Fact-Checking Systems and Evaluating the Factuality of Claims and LLMs (2025.coling-main)

Challenge:	Recent years have seen the rise of large language models (LLMs), where practitioners use task-specific prompts; this was shown to be effective for a variety of tasks.
Approach:	They propose to rethink semantic textual similarity (STS) and natural language inference (NLI) models with task-specific prompts and model overconfidence to capture disagreements between human judgements.
Outcome:	The proposed models are able to capture human opinions on individual examples without any parameter modifications.

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Yuxia Wang, Minghan Wang, Hasan Iqbal, Georgi N. Georgiev, Jiahui Geng, Iryna Gurevych, Preslav Nakov

Arabic Dataset for LLM Safeguard Evaluation (2025.naacl-long)

Challenge:	Large language models (LLMs) generate naturallysounding answers over a broad range of human inquiries, but they still produce content that deviates from real-world facts.
Approach:	They propose a framework for building customized automatic fact-checking systems, benchmarking their accuracy, evaluating factuality of LLMs, and verifying claims in a document.
Outcome:	The proposed framework assesses the factuality of free-form responses in open domains and evaluates factually of LLMs.

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Yasser Ashraf, Yuxia Wang, Bin Gu, Preslav Nakov, Timothy Baldwin

Robustness in NLP

Challenge:	Existing studies on large language models have focused on English, but the safety of LLMs in Arabic remains under-explored.
Approach:	They propose to use Arabic-region-specific questions to evaluate LLMs' safety . they use a dual-perspective evaluation framework to examine differences between LLM responses .
Outcome:	The proposed framework assesses the LLM responses from both governmental and opposition viewpoints.

FinChain: A Symbolic Benchmark for Verifiable Chain-of-Thought Financial Reasoning (2026.acl-long)

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Zhuohan Xie, Daniil Orel, Rushil Thareja, Dhruv Sahnan, Hachem Madmoun, Fan Zhang, Debopriyo Banerjee, Georgi Nenkov Georgiev, Xueqing Peng, Lingfei Qian, Jimin Huang, Jinyan Su, Aaryamonvikram Singh, Rui Xing, Rania Elbadry, Chen Xu, Haonan Li, Fajri Koto, Ivan Koychev, Tanmoy Chakraborty, Yuxia Wang, Salem Lahlou, Veselin Stoyanov, Sophia Ananiadou, Preslav Nakov

Reasoning

Challenge:	Existing benchmarks emphasize final numerical answers while neglecting intermediate reasoning steps.
Approach:	They propose a symbolic benchmark for verifiable Chain-of-Thought evaluation in finance . FINCHAIN spans 58 topics across 12 financial domains and three difficulty levels .
Outcome:	The proposed benchmark aims to bridge symbolic reasoning and factual verification.

Detection of Human and Machine-Authored Fake News in Urdu (2025.acl-long)

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Muhammad Zain Ali, Yuxia Wang, Bernhard Pfahringer, Tony C Smith

M4GT-Bench: Evaluation Benchmark for Black-Box Machine-Generated Text Detection (2024.acl-long)

Challenge:	Existing methods for fake news detection focus on binary classification and English texts, ignoring the distinction between machine-generated true vs. fake news and low-resource languages.
Approach:	They propose to include machine-generated news focusing on Urdu to improve accuracy and robustness.
Outcome:	The proposed strategy improves accuracy and robustness across four datasets in various settings.

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Yuxia Wang, Jonibek Mansurov, Petar Ivanov, Jinyan Su, Artem Shelmanov, Akim Tsvigun, Osama Mohammed Afzal, Tarek Mahmoud, Giovanni Puccetti, Thomas Arnold, Alham Aji, Nizar Habash, Iryna Gurevych, Preslav Nakov

Text Generation

Challenge:	Large Language Models (LLMs) have brought an unprecedented surge in machine-generated text (MGT) societal implications are posed by their potential misuse and lack of training data.
Approach:	They propose a benchmark to detect machine-generated text in multiple languages . they use multi-domain and multi-generator corpus to identify which model generated the text .
Outcome:	The proposed benchmark compares a multilingual, multi-domain and multi-generator corpus of MGTs with human-generated content.

AICD Bench: A Challenging Benchmark for AI-Generated Code Detection (2026.eacl-long)

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Daniil Orel, Dilshod Azizov, Indraneil Paul, Yuxia Wang, Iryna Gurevych, Preslav Nakov

Robustness in NLP

Challenge:	Existing benchmarks for detecting AI-generated code are limited to binary human–machine classification under in-distribution settings.
Approach:	They propose to use AICD Bench to build a robust binary classification framework for large language models.
Outcome:	The proposed benchmark spans 2M examples, 77 models across 11 families, and 9 programming languages.

Loki: An Open-Source Tool for Fact Verification (2025.coling-demos)

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Haonan Li, Xudong Han, Hao Wang, Yuxia Wang, Minghan Wang, Rui Xing, Yilin Geng, Zenan Zhai, Preslav Nakov, Timothy Baldwin

KazMMLU: Evaluating Language Models on Kazakh, Russian, and Regional Knowledge of Kazakhstan (2025.acl-long)

Challenge:	Loki is an open-source fact-checking tool designed to address the growing problem of misinformation.
Approach:	They propose a tool that breaks down the fact-checking task into five steps . they propose LOKI, which offers a semiautomated, human-in-the-loop approach .
Outcome:	a new open-source tool is designed to address the growing problem of misinformation . the tool breaks down the fact-checking task into five steps to assist human judgment .

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Mukhammed Togmanov, Nurdaulet Mukhituly, Diana Turmakhan, Jonibek Mansurov, Maiya Goloburda, Akhmed Sakip, Zhuohan Xie, Yuxia Wang, Bekassyl Syzdykov, Nurkhan Laiyk, Alham Fikri Aji, Ekaterina Kochmar, Preslav Nakov, Fajri Koto

Capture Human Disagreement Distributions by Calibrated Networks for Natural Language Inference (2022.findings-acl)

Challenge:	Kazakh language remains underrepresented in the field of natural language processing despite the country's population exceeding twenty million . however, there is a lack of dedicated models and benchmark evaluations specifically tailored to Kazakh languages.
Approach:	They propose to create a dataset specifically designed for Kazakh language with 23,000 questions sourced from authentic educational materials and manually validated by native speakers and educators.
Outcome:	The first MMLU-style dataset specifically designed for Kazakh language.

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Yuxia Wang, Minghan Wang, Yimeng Chen, Shimin Tao, Jiaxin Guo, Chang Su, Min Zhang, Hao Yang

Reasoning

Challenge:	Previously, it's common to disregard it as noise or as a sign of poor-quality data, as their annotations are heavily based on personal experience and opinions.
Approach:	They propose to capture the human disagreement distribution from the perspective of model calibration.
Outcome:	The proposed model can achieve competitive performance when well-calibrated, on divergence scores between predictive probability and the true human opinion distribution, and the accuracy.

Is Human-Like Text Liked by Humans? Multilingual Human Detection and Preference Against AI (2026.acl-long)

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Yuxia Wang, Rui Xing, Jonibek Mansurov, Giovanni Puccetti, Zhuohan Xie, Minh Ngoc Ta, Jiahui Geng, Jinyan Su, Mervat Abassy, Saadeldine Eletter, Kareem Elozeiri, Nurkhan Laiyk, Maiya Goloburda, Tarek Mahmoud, Raj Vardhan Tomar, Alexander Aziz, Ryuto Koike, Masahiro Kaneko, Artem Shelmanov, Ekaterina Artemova, Vladislav Mikhailov, Akim Tsvigun, Alham Fikri Aji, Nizar Habash, Iryna Gurevych, Preslav Nakov

Can Machines Resonate with Humans? Evaluating the Emotional and Empathic Comprehension of LMs (2024.findings-emnlp)

Challenge:	Prior studies have shown that distinguishing text generated by Large Language Models from human-written text is challenging for humans and often no better than random guessing.
Approach:	They conduct extensive case study to determine the upper bound of human detection accuracy.
Outcome:	The findings challenge previous conclusions on human detection accuracy across languages and domains.

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Muhammad Arslan Manzoor, Yuxia Wang, Minghan Wang, Preslav Nakov

LLM-DetectAIve: a Tool for Fine-Grained Machine-Generated Text Detection (2024.emnlp-demo)

Challenge:	Empathy plays a pivotal role in fostering prosocial behavior, often triggered by the sharing of personal experiences through narratives.
Approach:	They propose to use contrastive learning with masked LMs and supervised fine-tuning with large language models to improve empathy understanding in NLP models.
Outcome:	The proposed methods show that there is low agreement among annotators and that cultural differences are a factor in their interpretation of empathy.

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Mervat Abassy, Kareem Elozeiri, Alexander Aziz, Minh Ta, Raj Tomar, Bimarsha Adhikari, Saad Ahmed, Yuxia Wang, Osama Mohammed Afzal, Zhuohan Xie, Jonibek Mansurov, Ekaterina Artemova, Vladislav Mikhailov, Rui Xing, Jiahui Geng, Hasan Iqbal, Zain Mujahid, Tarek Mahmoud, Akim Tsvigun, Alham Aji, Artem Shelmanov, Nizar Habash, Iryna Gurevych, Preslav Nakov

Challenge:	a large number of machine-generated texts are often hard to distinguish between human-written and machine-generated text . this raises concerns about potential misuse, especially within educational and academic domains .
Approach:	They propose a system that can detect whether a text is human-written or machine-generated . they use a fine-grained classification schema to identify the use of machine-generated text .
Outcome:	The proposed system can distinguish between human-written and machine-generated text . it can detect attempts to obfuscate the fact that a text was machine- generated .

Factcheck-Bench: Fine-Grained Evaluation Benchmark for Automatic Fact-checkers (2024.findings-emnlp)

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Yuxia Wang, Revanth Gangi Reddy, Zain Mujahid, Arnav Arora, Aleksandr Rubashevskii, Jiahui Geng, Osama Mohammed Afzal, Liangming Pan, Nadav Borenstein, Aditya Pillai, Isabelle Augenstein, Iryna Gurevych, Preslav Nakov

Stereotype Bias in a Bilingual Setting: A Culturally Grounded Evaluation in Kazakhstan (2026.acl-long)

Challenge:	Large language models generate naturally sounding answers over a broad range of human inquiries, but they often generate answers that contradict real-world facts.
Approach:	They propose a framework for annotating and evaluating the factuality of large language models . they propose 'factcheck-bench' which provides a multi-stage annotation scheme .
Outcome:	The proposed framework outperforms several popular LLM fact-checkers in claim, sentence, and document levels.

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Nurkhan Laiyk, Daniil Orel, Ayana Mussabayeva, Maiya Goloburda, Kamila Kuishibekova, Liya Goloburda, Diana Turmakhan, Preslav Nakov, Yuxia Wang, Fajri Koto

Do-Not-Answer: Evaluating Safeguards in LLMs (2024.findings-eacl)

Challenge:	Stereotype bias in language models is largely understudied in English . language models perform strongly on downstream NLP tasks, but they are pre-trained on large text corpora .
Approach:	They use a dataset to assess stereotype bias in language models in Kazakhstan . they find that stereotype bias is most pronounced in code-mixed inputs .
Outcome:	The proposed dataset shows that stereotype bias is most pronounced in code-mixed inputs.

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Yuxia Wang, Haonan Li, Xudong Han, Preslav Nakov, Timothy Baldwin

SAHM: A Benchmark for Arabic Financial and Shari’ah-Compliant Reasoning (2026.acl-long)

Challenge:	a dataset evaluating harmful capabilities in large language models is available at https://github.com/Libr-AI/do-not-answer.
Approach:	They collect an open-source dataset to evaluate the safeguards in large language models . they find that simple BERT-style classifiers can achieve results comparable to GPT-4 .
Outcome:	The proposed dataset compares the safety of six popular LLMs to GPT-4 on automatic safety evaluation.

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Rania Elbadry, Sarfraz Ahmad, Ahmed Heakl, Dani Bouch, Momina Ahsan, Muhra AlMahri, Marwa Elsaid Khalil, Yuxia Wang, Salem Lahlou, Sophia Ananiadou, Veselin Stoyanov, Jimin Huang, Xueqing Peng, Preslav Nakov, Zhuohan Xie

UrduFactCheck: An Agentic Fact-Checking Framework for Urdu with Evidence Boosting and Benchmarking (2025.findings-emnlp)

Challenge:	English financial NLP has progressed rapidly through benchmarks for sentiment, document understanding, and financial question answering.
Approach:	They propose a document-grounded benchmark and instruction-tuning dataset for Arabic financial NLP and Shari’ah-compliant reasoning.
Outcome:	The proposed dataset contains 14,380 expert-verified instances spanning seven tasks . it includes financial sentiment analysis, extractive summarization, and event–cause reasoning .

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Sarfraz Ahmad, Hasan Iqbal, Momina Ahsan, Numaan Naeem, Muhammad Ahsan Riaz Khan, Arham Riaz, Muhammad Arslan Manzoor, Yuxia Wang, Preslav Nakov

Machine Translation

Challenge:	Existing automated fact-checking systems are predominantly developed for English . Existing systems focus on claim verification, but UrduFactQA targets factuality .
Approach:	They propose two hand-annotated benchmarks to enable fact-checking and factual consistency evaluation in Urdu.
Outcome:	The proposed benchmarks are the first of their kind for Urdu and are available online.

FIRE: Fact-checking with Iterative Retrieval and Verification (2025.findings-naacl)

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Zhuohan Xie, Rui Xing, Yuxia Wang, Jiahui Geng, Hasan Iqbal, Dhruv Sahnan, Iryna Gurevych, Preslav Nakov

A Multi-Expert Structural-Semantic Hybrid Framework for Unveiling Historical Patterns in Temporal Knowledge Graphs (2025.findings-acl)

Challenge:	Fact-checking long-form text is challenging, and breaking it down into multiple atomic claims is not cost-effective.
Approach:	They propose a novel agent-based framework that integrates evidence retrieval and claim verification in an iterative manner.
Outcome:	The proposed framework reduces large language model (LLM) costs by an average of 7.6 times and search costs by 16.5 times while retaining the same performance.

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Yimin Deng, Yuxia Wu, Yejing Wang, Guoshuai Zhao, Li Zhu, Qidong Liu, Derong Xu, Zichuan Fu, Xian Wu, Yefeng Zheng, Xiangyu Zhao, Xueming Qian

Knowledge Representation

Challenge:	Existing methods focus on graph structure learning or semantic reasoning, lacking the capability to capture the inherent differences between historical and non-historical events.
Approach:	They propose a temporal knowledge graph reasoning framework that integrates both structural and semantic information to guide the reasoning process for different events.
Outcome:	The proposed framework integrates structural and semantic information to predict future events . it can provide evidence for many downstream tasks, including situation analysis and political decision making .

Instruction Tuning on Public Government and Cultural Data for Low-Resource Language: a Case Study in Kazakh (2025.acl-long)

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Nurkhan Laiyk, Daniil Orel, Rituraj Joshi, Maiya Goloburda, Yuxia Wang, Preslav Nakov, Fajri Koto

Low-Resource NLP

Challenge:	Instruction tuning in low-resource languages remains underexplored due to limited text data, particularly in government and cultural domains.
Approach:	They propose to open-source a large-scale instruction-following dataset covering key institutional and cultural knowledge relevant to Kazakhstan.
Outcome:	The proposed dataset improves LLMs’ understanding of procedural, legal, and structural governance topics.

OpenFactCheck: A Unified Framework for Factuality Evaluation of LLMs (2024.emnlp-demo)

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Hasan Iqbal, Yuxia Wang, Minghan Wang, Georgi Georgiev, Jiahui Geng, Iryna Gurevych, Preslav Nakov

Collective Human Opinions in Semantic Textual Similarity (2023.tacl-1)

Challenge:	Large language models (LLMs) often produce content that deviates from real-world facts.
Approach:	They developed a unified framework to assess the factuality of large language models . open-sourced framework is publicly available as a Python library and web service .
Outcome:	OpenFactCheck is open-sourced and publicly released as a Python library and web service.

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Yuxia Wang, Shimin Tao, Ning Xie, Hao Yang, Timothy Baldwin, Karin Verspoor

Graph-guided Cross-composition Feature Disentanglement for Compositional Zero-shot Learning (2025.findings-acl)

Challenge:	Existing benchmarks for semantic textual similarity (STS) use averaged human ratings as gold standard.
Approach:	They propose to use a Chinese sentence-to-sentence dataset to study collective human opinions in semantic textual similarity (STS) neither a scalar nor a single Gaussian fits a set of observed judgments adequately, they argue .
Outcome:	The proposed dataset does not capture disagreements on individual instances, but rather the confidence over the aggregate dataset.

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Yuxia Geng, Runkai Zhu, Jiaoyan Chen, Jintai Chen, Xiang Chen, Zhuo Chen, Shuofei Qiao, Yuxiang Wang, Xiaoliang Xu, Sheng-Jun Huang

Explicit and Implicit Data Augmentation for Social Event Detection (2025.acl-long)

Challenge:	Disentanglement of visual features of primitives (i.e., attributes and objects) has shown exceptional results in Compositional Zero-shot Learning (CZSL).
Approach:	They propose a solution that takes multiple compositions as inputs and constrains disentangled primitive features to be general across compositions.
Outcome:	The proposed architecture significantly improves performance on three popular CZSL benchmarks and has been verified by solid ablation studies.

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Congbo Ma, Yuxia Wang, Jia Wu, Jian Yang, Jing Du, Zitai Qiu, Qing Li, Hu Wang, Preslav Nakov

VSCBench: Bridging the Gap in Vision-Language Model Safety Calibration (2025.findings-acl)

Challenge:	Social event detection relies on labeled data, but annotation is costly and labor-intensive.
Approach:	They propose a plug-and-play dual augmentation framework that combines explicit text-based and implicit feature-space augmentation to enhance data diversity and model robustness.
Outcome:	The proposed framework outperforms the best baseline model by 17.67% on the Twitter2012 dataset and 15.57% on the twitter2018 dataset in terms of the average F1 score.

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Jiahui Geng, Qing Li, Zongxiong Chen, Yuxia Wang, Derui Zhu, Zhuohan Xie, Chenyang Lyu, Xiuying Chen, Preslav Nakov, Fakhri Karray

Visual Data in NLP

Challenge:	Existing safety calibration methods focus on model undersafety, where the model responds to hazardous queries, while neglecting oversafetiness, where models refuse to answer safe queries.
Approach:	They propose safety calibration which addresses both undersafety and oversafetiness by comparing model responses to a novel dataset of 3,600 image-text pairs.
Outcome:	The proposed methods have been used to evaluate safety calibration across image-centric and text-centric scenarios.

HD-NDEs: Neural Differential Equations for Hallucination Detection in LLMs (2025.acl-long)

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Qing Li, Jiahui Geng, Zongxiong Chen, Derui Zhu, Yuxia Wang, Congbo Ma, Chenyang Lyu, Fakhri Karray

Information Retrieval

Challenge:	Hallucination is a significant challenge for large language models, but current methods struggle when non-factual information arises in the early or mid-sequence of outputs, reducing their reliability.
Approach:	They propose a method that captures the full dynamics of large language models by using neural differential equations to assess the truthfulness of statements.
Outcome:	The proposed method achieves 14% improvement in AUC-ROC on the True-False dataset compared to state-of-the-art methods.

Exploring the Potential of Multimodal LLM with Knowledge-Intensive Multimodal ASR (2024.findings-emnlp)

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Minghan Wang, Yuxia Wang, Thuy-Trang Vu, Ehsan Shareghi, Reza Haf

Multimodality

Challenge:	Recent advances in multimodal large language models have made significant progress in integrating information across various modalities, yet real-world applications in educational and scientific domains remain challenging.
Approach:	They propose a task that focuses on transcribing scientific conference videos by leveraging visual information from slides to enhance the accuracy of technical terminologies.
Outcome:	The proposed framework improves transcript quality through post-editing and improves performance over speech-only baselines.

Factuality of Large Language Models: A Survey (2024.emnlp-main)

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Yuxia Wang, Minghan Wang, Muhammad Arslan Manzoor, Fei Liu, Georgi Georgiev, Rocktim Das, Preslav Nakov

Demystifying Instruction Mixing for Fine-tuning Large Language Models (2024.acl-srw)

Challenge:	Large language models (LLMs) are factually incorrect, which limits their applicability in real-world scenarios.
Approach:	They analyze existing work to identify major challenges and their associated causes . they propose to evaluate LLMs using a variety of measures to mitigate factual errors .
Outcome:	The proposed methods are based on a variety of datasets and proposed strategies to mitigate factual errors.

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Renxi Wang, Haonan Li, Minghao Wu, Yuxia Wang, Xudong Han, Chiyu Zhang, Timothy Baldwin

Uncertainty Estimation and Reduction of Pre-trained Models for Text Regression (2022.tacl-1)

Challenge:	Instruction tuning is effective for aligning large language models with human instructions, but the procedure to optimizing the mixing of instruction datasets is still unclear.
Approach:	They categorize instructions into three primary types: NLP downstream tasks, coding, and general chat.
Outcome:	The proposed method improves performance of large language models (LLMs) but it is difficult to combine different instruction datasets to optimize overall performance.

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Yuxia Wang, Daniel Beck, Timothy Baldwin, Karin Verspoor