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CALL FOR PAPERS: KGLLM 2024 : Special session on Knowledge Graphs and Large Language Models, 19-20 October 2024
by Mourad Abbas 19 Jun '24

19 Jun '24

KGLLM 2024 : Special session on Knowledge Graphs and Large Language Models Oct 19, 2024 - Oct 19, 2024 Trento, Italy Link: https://www.icnlsp.org/2024welcome/#special_session The Special session on Knowledge Graphs and Large Language Models will be held within the 7th International Conference on Natural Language and Speech Processing (ICNLSP 2024 <https://www.icnlsp.org/2024welcome/>) on October 19, 2024. ** DESCRIPTION ** “In recent years, the fields of Knowledge Graphs (KGs) and Large Language Models (LLMs) have witnessed remarkable advancements, revolutionizing the landscape of artificial intelligence and natural language processing. KGs, structured representations of knowledge, and LLMs, powerful language models trained on vast amounts of text data, have individually demonstrated their prowess in various applications. However, the integration and synergy between KGs and LLMs have emerged as a new frontier, offering unprecedented opportunities for enhancing knowledge representation, understanding, and generation. This integration not only enriches the semantic understanding of textual data but also empowers AI systems with the ability to reason, infer, and generate contextually relevant responses. ** TOPICS ** This special session aims to delve into the theoretical foundations, historical perspectives, and practical applications of the fusion between Knowledge Graphs and Large Language Models. We invite contributions that explore the following areas: 1- Theoretical Frameworks: Papers elucidating the theoretical underpinnings of integrating KGs and LLMs, including methodologies, algorithms, and models for knowledge-enhanced language understanding and generation. 2- Historical Perspectives: Insights into the evolution of KGs and LLMs, tracing their development trajectories, seminal works, and transformative milestones leading to their integration. 3- Design and Implementation: Research articles focusing on the design principles, architectures, and techniques for effectively combining KGs and LLMs to facilitate tasks such as information retrieval, question answering, knowledge inference, and natural language understanding. 4- Explanatory Capabilities: Explorations into how the fusion of KGs and LLMs enables the development of explainable AI systems, providing transparent and interpretable insights into model decisions and outputs. 5- Human-Centered Intelligent Systems: Studies examining the design and deployment of interactive AI systems that leverage KGs and LLMs to facilitate seamless human- computer interaction, catering not only to experts but also to a broader lay audience. We encourage submissions that contribute to advancing our understanding of the synergistic relationship between Knowledge Graphs and Large Language Models, fostering interdisciplinary collaborations across computer science, artificial intelligence, linguistics, cognitive science, and beyond. By shedding light on this burgeoning area of research, this special session aims to propel the field forward and inspire future innovations in AI-driven knowledge representation and natural language processing.” ** SESSION ORGANIZERS ** Gérard Chollet, CNRS-SAMOVAR Institut Polytechnique de Paris, France. Hugues Sansen, Institut Polytechnique de Paris, France. ** IMPORTANT DEADLINES ** Submission deadline: 30 June 2024 11:59 PM (GMT) Notification of acceptance: 15 September 2024 Camera-ready paper due: 25 September 2024 ** PUBLICATION ** The accepted papers will be included in the ICNLSP Conference proceedings which will be published in ACL anthology. The extended versions will be published in a special issue of the Machine Learning and Knowledge Extraction Journal (MAKE), indexed in Web of Science, Scopus, etc. ** CONTACT ** icnlsp(at)gmail(dot)com

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PhD position, Grenoble, Structural Biases for Semantic Prediction, 3 years
by maximin coavoux 19 Jun '24

19 Jun '24

Title: Structural Biases for Compositional Semantic Prediction # Scientific context Compositionality is a foundational hypothesis in formal semantics and states that the semantic interpretation of an utterance is a function of its parts and how they are combined (i.e. their syntactic structure). In NLP, the current dominant paradigm is to design end-to-end models with no intermediate linguistically interpretable representations, which is often motivated by the fact that pretrained language models implicitly encode latent syntactical representations. However, recent studies suggest that the syntactic information learned by language models are insufficient and that, in their current form, they are unable to exploit the syntactic information provided in their input when they need to generate a structured output. Strikingly, most systems that obtained decent results on compositional generalization benchmarks either (i) include some data augmentation methods that increase the exposure of the model to diverse syntactic structures at training time, or (ii) resort to a natural language parser and hand-crafted rules to derive the semantic representation from the syntactic tree. These two approaches are efficient, but they still have limitations that need to be addressed. Firstly, data augmentation bypasses the issue altogether, is tied to a particular dataset or task and requires additional computation, both for generating new data and for re-training or fine-tuning models. Secondly, approach (ii) leaves the seq2seq framework for a more conceptually complex framework, and often uses architectures that are tied to specific data or tasks. In contrast, we believe that with proper built-in inductive biases, a seq2seq model might provide a simple, yet effective solution to the structural compositionality issue. # PhD Proposal The goal of this PhD wil be to explore inductive biases related to linguistic structures, in an attempt to build small NLP models with compositional skills, i.e. models with built-in knowledge making them able to infer generalization rules from few data points. Research directions will be defined together with the successful applicant (who is encouraged to bring their own ideas!) and may include: - Learning invariant language representations. A risk of learning from little data or rare phenomena is that a model may rely on spurious correlations and be unable to generalize outside a specific context. Developing representations that are invariant to noise has been proposed as a way of improving generalization (Peyrard et al 2022). We propose to formalize invariants related to syntactic and semantic structures and explore ways to integrate them during the training phase. - Syntactically constrained decoders. Unlike parsers, Seq2seq models are unable to generate structures unseen at train time. We propose to explore the use of structural constraints to guide decoding in seq2seq models. # Important information: - Starting date: between September and December 2024 (duration 3 years) - Place of work: Laboratoire d’Informatique de Grenoble, CNRS, Grenoble, France - Funding: ANR project ''COMPO: Inductive Biases for Compositionality-capable Deep Learning Models of Natural Language'’ (2024-2028) - Partners: Université Paris Cité, Université Aix-Marseille, Université Grenoble Alpes - The PhD will be supervised by Éric Gaussier and Maximin Coavoux, and in close collaborations with other partners from the COMPO consortium, the PhD candidate will be part of 2 teams of the LIG: GETALP and APTIKAL. - Salary: ~2300€ gross/month - Profile: Master’s degree in NLP, computer science, experience in NLP and machine learning To apply, please send cv, cover letter and most recent academic transcripts to eric.gaussier(a)univ-grenoble-alpes.fr and maximin.coavoux(a)univ-grenoble-alpes.fr References: SLOG: A Structural Generalization Benchmark for Semantic Parsing Bingzhi Li, Lucia Donatelli, Alexander Koller, Tal Linzen, Yuekun Yao, Najoung Kim <https://aclanthology.org/2023.emnlp-main.194/> Structural generalization is hard for sequence-to-sequence models Yuekun Yao, Alexander Koller <https://aclanthology.org/2022.emnlp-main.337/> Compositional Generalization Requires Compositional Parsers Pia Weißenhorn, Yuekun Yao, Lucia Donatelli, Alexander Koller <https://arxiv.org/abs/2202.11937> Invariant Language Modeling Maxime Peyrard, Sarvjeet Ghotra, Martin Josifoski, Vidhan Agarwal, Barun Patra, Dean Carignan, Emre Kiciman, Saurabh Tiwary, Robert West <https://aclanthology.org/2022.emnlp-main.387/>

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2nd CALL FOR PAPERS: ICNLSP 2024, 7th International Conference on Natural Language and Speech Processing
by Mourad Abbas 19 Jun '24

19 Jun '24

We are delighted to invite you to ICNLSP 2024 <https://www.icnlsp.org/2024welcome/>, the 7th edition of the International Conference on Natural Language and Speech Processing, which will be held at University of Trento from October 19th to 20th, 2024 (*HYBRID*). *Topics* - Signal processing, acoustic modeling. - Speech recognition (Architecture, search methods, lexical modeling, language modeling, language model adaptation, multimodal systems, applications in education and learning, zero-resource speech recognition, etc.). - Speech Analysis. - Paralinguistics in Speech and Language (Perception of paralinguistic phenomena, analysis of speaker states and traits, etc.). - Spoken Dialog Systems and Conversational Analysis - Speech Translation. - Speech synthesis. - Speaker verification and identification. - Language identification - Speech coding. - Speech enhancement - Speech intelligibility - Speech Perception - Speech Production - Brain studies on speech - Phonetics, phonology and prosody. - Speech and hearing disorders. - Paralinguistics of pathological speech and language. - Speech technology for disordered speech/hairing. - Cognition and natural language processing. - Machine translation. - Text categorization. - Summarization. - Sentiment analysis and opinion mining. - Computational Social Web. - Arabic dialects processing. - Under-resourced languages: tools and corpora. - Large language models. - Arabic OCR. - NLP tools for software requirements and engineering. - Knowledge fundamentals. - Knowledge management systems. - Information extraction. - Data mining and information retrieval. - Lexical semantics and knowledge representation. - Requirements engineering and NLP. - NLP for Arabic heritage documents. *Submission* Papers must be submitted via the link: https://cmt3.research.microsoft.com/ICNLSP2024/ <https://cmt3.research.microsoft.com/ICNLSP2024/> Each submitted paper will be reviewed by three program committee members.The reviewing process is double-blind. Authors can use the *ACL format*: *Latex <https://www.icnlsp.org/ACL%202023%20Proceedings%20Template.zip>*or Word. Authors have the choice to submit their papers as a full or short paper. Long papers consist of up to 8 pages of content + references. Short papers, up to 4 pages of content + references. *Important dates* *Submission deadline:* *30 June 2024 11:59 PM (GMT*) *Notification of acceptance:* 15 September 2024 *Camera-ready paper due:* 25 September 2024 *Conference dates:* 19, 20 October 2024 *Publication* *1- All accepted papers will be published in **ACL Anthology <https://aclanthology.org/>**.* *2- Selected papers will be published (after extension) in:* * 2-a-* A *SPECIAL ISSUE* <https://www.mdpi.com/journal/make/special_issues/POB4VNE0QP> of Machine Learning and Knowledge Extraction Journal <https://www.mdpi.com/journal/make> (MAKE), indexed in *Web of Science <https://mjl.clarivate.com/search-results>*, *Scopus* <https://www.scopus.com/sources.uri>, etc. *Special issue title*: <https://www.mdpi.com/journal/make/special_issues/POB4VNE0QP> <https://www.mdpi.com/journal/make/special_issues/POB4VNE0QP>*Knowledge Graphs and Large Language Models. <https://www.mdpi.com/journal/make/special_issues/POB4VNE0QP>* * 2-b-* Signals and Communication Technology (Springer), indexed in *Scopus* <https://www.scopus.com/> and *zbMATH* <https://zbmath.org/>.

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Shared Task on Quality Estimation at WMT'24
by Fred Blain 19 Jun '24

19 Jun '24

Dear all, we are happy to invite you to participate in the Shared Task on Quality Estimation at WMT'24. The details of the task can be found at: https://www2.statmt.org/wmt24/qe-task.html New this year: * We introduce a new language pair (zero-shot): English-Spanish * Continuing from the previous edition, we will also analyse the robustness of submitted QE systems to a set of different phenomena which will span from hallucinations and biases to localized errors, which can significantly impact real-world applications. * We also introduce a new task, seeking not only to detect but also to correct errors: Quality-aware Automatic Post-Editing! We invite participants to submit systems capable of automatically generating QE predictions for machine-translated text and the corresponding output corrections. 2024 QE Tasks: Task 1 -- Sentence-level quality estimation This task follows the same format as last year but with fresh test sets and a new language pair: English-Spanish. We will test the following language pairs: * English to German (MQM) * English to Spanish (MQM) * English to Hindi (MQM & DA) * English to Gujarati (DA) * English to Telugu (DA) * English to Tamil (DA) More details: https://www2.statmt.org/wmt24/qe-subtask1.html Task 2 -- Fine-grained error span detection Sequence labelling task: predict the error spans in each translation and the associated error severity: Major or Minor. We will test the following language pairs: * English to German (MQM) * English to Spanish (MQM) * English to Hindi (MQM) More details: https://www2.statmt.org/wmt24/qe-subtask2.html Task 3 -- Quality-aware Automatic Post-editing We expect submissions of post edits correcting detected error spans of the original translation. Although the task is focused on quality-informed APE, we also allow participants to submit APE output without QE predictions to understand the impact of their QE system. Submissions w/o QE predictions will also be considered official. We will test the following language pairs: * English to Hindi * English to Tamil More details: https://www2.statmt.org/wmt24/qe-subtask3.html Important dates: 1. Test sets will be released on July 15th. 2. Participants can submit their systems by July 23rd on codalab. 3. System paper submissions are due by 20th August [aligned with WMT deadlines]. Note: Like last year, we aligned with the General MT and Metrics shared tasks to facilitate cross-submission on the common language pairs: English-German, English-Spanish, and English-Hindi (MQM). We look forward to your submissions and feel free to contact us if you have any more questions! Best wishes, on behalf of the organisers.

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Full-Time Research Assistant (PhD-level) in AI & NLP for Mental Health
by jishaoxiong＠gmail.com 19 Jun '24

19 Jun '24

The original post is here: https://www.informatik.tu-darmstadt.de/ukp/ukp_home/jobs_ukp/2021_associate… Are you passionate about making a difference in the field of mental health through cutting-edge research in AI and Natural Language Processing? Do you have a strong background in computer science, data science, or a related field? If so, we invite you to join our dynamic and interdisciplinary team at the Technical University of Darmstadt! Position: Full-Time Research Assistant (i.e., doctoral candidate or PhD student) Duration: 1.10.2024 or soon afterward - 31.12.2027 with the possibility of extension. Location: Department of Computer Science, Technical University of Darmstadt Responsibilities: - Conduct cutting-edge research in NLP with a focus on mental health applications. - Focus on research topics, such as NLP and knowledge discovery for mental health, large language models for clinical applications, and multimodal clinical data analysis. - Develop and implement algorithms for analyzing therapist-patient conversations. - Collaborate with a diverse team of researchers from TU Darmstadt and other partner institutions. Ecosystem: We are part of DYNAMIC, the newly approved interdisciplinary LOEWE-funded center “Dynamic Network Approach of Mental Health to Stimulate Innovations for Change.” Our mission is to advance the understanding and treatment of mental health disorders using AI, NLP, and multimodal data analysis. Team: Dr. Shaoxiong Ji (https://www.helsinki.fi/~shaoxion/) will join TU Darmstadt this fall and establish a junior independent research group focusing on foundation models and their applications, such as healthcare. He has a wide range of research directions, including NLP for health, multilingual LLMs, and learning methods such as federated learning, multitask learning, and meta-learning. The newly established research group will closely collaborate with the research labs led by Prof. Iryna Gurevych and Prof. Kristian Kersting, and partners under the umbrella of the DYNAMIC project. Qualifications: - A Master’s degree in Computer Science, Data Science, AI, NLP, or a related field. - Strong programming skills in Python or other relevant languages. - Experience with deep learning frameworks - Excellent problem-solving abilities and a passion for research. - Previous experience in clinical NLP or multimodal data analysis is a plus but not required. - Strong communication skills and the ability to work effectively in a collaborative environment. What We Offer: - An exciting opportunity to contribute to impactful research in mental health. - A supportive and collaborative research environment. - Opportunities for professional development and growth within the DYNAMIC project and beyond. How to Apply: If you are enthusiastic about joining our team and contributing to groundbreaking research, please submit the following documents: - Detailed CV - Master’s degree certificates and the Bachelor and Master study transcripts - Cover letter outlining your motivation and relevant experience - Contact information for at least two academic or professional references Please send your application to Shaoxiong Ji <shaoxiong.ji(a)outlook.com> by July 31st, 2024. After that, the positions will remain open until filled. We will consider applications as soon as they are submitted. Join us in making a real-world impact on mental health through the power of AI and NLP!

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Shared task on Multilingual Grammatical Error Correction (MultiGEC-2025)
by Arianna Masciolini 18 Jun '24

18 Jun '24

Shared task on Multilingual Grammatical Error Correction (MultiGEC-2025) We invite you to participate in the shared task on Multilingual Grammatical Error Correction, MultiGEC-2025, covering over 10 languages, including Czech, English, Estonian, German, Icelandic, Italian, Latvian, Slovene, Swedish and Ukrainian. The results will be presented on March 5 (or 2), 2025, at the NLP4CALL workshop, colocated with the NoDaLiDa conference (https://www.nodalida-bhlt2025.eu/conference) to be held in Estonia, Tallinn, on 2--5 March 2025. The publication venue for system descriptions will be the proceedings of the NLP4CALL workshop. Official system evaluation will be carried out on CodaLab. * TASK DESCRIPTION In this shared task, your goal is to rewrite learner-written texts to make them grammatically correct or both grammatically correct and idiomatic, that is either adhering to the "minimal correction" principle or applying fluency edits. For instance, the text > My mother became very sad, no food. But my sister better five months later. can be corrected minimally as > My mother became very sad, and ate no food. But my sister felt better five months later. or with fluency edits as > My mother was very distressed and refused to eat. Luckily, my sister recovered five months later. For fair evaluation of both approaches to the correction task, we will provide two evaluation metrics, one favoring minimal correction, one suited for fluency-edited output (read more under Evaluation). We particularly encourage development of multilingual systems that can process all (or several) languages using a single model, but this is not a mandatory requirement to participate in the task. * DATA We provide training, development and test data for each of the languages. The training and development dataset splits will be made available through Github. Evaluation will be performed on a separate test set. See website for more detailed information: https://github.com/spraakbanken/multigec-2025/ * EVALUATION During the shared task, evaluation will be based on cross-lingually applicable automatic metrics, primarily: - GLEU score (reference-based) - Scribendi score (reference-free) For comparability with previous results, we will also provide F0.5 scores. After the shared task, we also plan on carrying out a human evaluation experiment on a subset of the submitted results. * TIMELINE (preliminary) - June 18, 2024 - first call for participation - September 20, 2024 - second call for participation - October 20, 2024 - third call for participation. Training and validation data released, CodaLab opens for team registrations - October 30, 2024 - reminder. Validation server released online - November 13, 2024 - test data released - November 20, 2024 - system submission deadline (system output) - November 29, 2024 - results announced - December 20, 2024 - paper submission deadline with system descriptions - January 20, 2025 - paper reviews sent to the authors - February 7, 2025 - camera-ready deadline - March 5 (or March 2), 2025 - presentations of the systems at the NLP4CALL workshop * PUBLICATION We encourage you to submit a paper with your system description to the NLP4CALL workshop special track. We follow the same requirements for paper submissions as the NLP4CALL workshop, i.e. we use the same template and apply the same page limit. All papers will be reviewed by the organizing committee. Upon paper publication, we encourage you to share models, code, fact sheets, extra data, etc. with the community through GitHub or other repositories. * ORGANIZERS - Arianna Masciolini, University of Gothenburg, Sweden - Andrew Caines, University of Cambridge, UK - Orphee De Clecrq, Ghent university, Belgium - Murathan Kurfali, Stockholm University, Sweden - Ricardo Muñoz Sánchez, University of Gothenburg, Sweden - Elena Volodina, University of Gothenburg, Sweden - Robert Östling, Stockholm University, Sweden * DATA PROVIDERS (more languages to come) - Czech: Alexandr Rosen, Charles University, Prague - English: Andrew Caines, University of Cambridge - Estonian: -- Mark Fishel, University of Tartu, Estonia -- Kais Allkivi-Metsoja, Tallinn University, Estonia -- Kristjan Suluste, Eesti Keele Instituut, Estonia - German: -- Torsten Zesch, Fernuniversität in Hagen, Germany -- Andrea Horbach, Fernuniversität in Hagen, Germany - Icelandic: Isidora Glisič, University of Iceland - Italian: Jennifer-Carmen Frey, Eurac Research Bolzano, Italy - Latvian: - Roberts Darģis, University of Latvia - Ilze Auzina, University of Latvia - Slovene: Špela Arhar Holdt, University of Ljubljana, Slovenia - Swedish: Arianna Masciolini, University of Gothenburg, Sweden - Ukrainian: -- Oleksiy Syvokon, Microsoft and -- Mariana Romanyshyn, Grammarly * CONTACT Please join the MultiGEC-2025 Google group (https://groups.google.com/g/multigec-2025) in order to ask questions, hold discussions and browse for already answered questions.

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Job Opening at Veeva - Medical NLP team - Senior/Principal Data Scientist (Remote)
by Ehsan Khoddam Mohammadi 18 Jun '24

18 Jun '24

Join Veeva Systems , a pioneer in cloud solutions for the life sciences industry, as a Senior/Principal Data Scientist focusing on NLP. Your role will primarily involve developing LLM-based agents that are specialized in searching and extracting detailed information about Key Opinion Leaders (KOLs) in the healthcare sector. You will craft an end-to-end human-in-the-loop pipeline to sift through a large array of unstructured medical documents—ranging from academic articles to clinical guidelines and meeting notes from therapeutic committees. You will also collaborate with over 2000 data curators and dedicated team of software developers and DevOps engineers to refine these models and deploy them into production environments. *What You'll Do* - Adopt the latest technologies and trends in NLP to your platform - Develop LLM-based agents capable of performing function calls and utilizing tools such as browsers for enhanced data interaction and retrieval. - Experience with Reinforcement Learning from Human Feedback (RLHF) methods such as Direct Preference Optimization (DPO) and Proximal Policy Optimization (PPO) for training LLMs based on human preferences. - Design, develop, and implement an end-to-end pipeline for extracting predefined categories of information from large-scale, unstructured data across multi-domain and multilingual settings - Create a robust semantic search functionality that effectively answers user queries related to various aspects of the data - Use and develop named entity recognition, entity-linking, slot-filling, few-shot learning, active learning, question/answering, dense passage retrieval and other statistical techniques and models for information extraction and machine reading - Deeply understand and analyze our data model per data source and geo-region and interpret model decisions - Collaborate with data quality teams to define annotation tasks, metrics, and perform qualitative and quantitative evaluation - Utilize cloud infrastructure for model development, ensuring seamless collaboration with our team of software developers and DevOps engineers for efficient deployment to production *Requirements* - 4+ years of experience as a data scientist (or 2+ years with a Ph.D. degree) - Master's or Ph.D. in Computer Science, Artificial Intelligence, Computational Linguistics, or a related field. - Strong theoretical knowledge of Natural Language Processing, Machine Learning, and Deep Learning techniques. - Proven experience working with large language models and transformer architectures, such as GPT, BERT, or similar. - Familiarity with large-scale data processing and analysis, preferably within the medical domain. - Proficiency in Python and relevant NLP libraries (e.g., NLTK, SpaCy, Hugging Face Transformers). - Experience in at least one framework for BigData (e.g. Ray, Spark) and one framework for Deep Learning (e.g. PyTorch, JAX) - Experience working with cloud infrastructure (e.g., AWS, GCP, Azure) and containerization technologies (e.g., Docker, Kubernetes) and experience with bashing script - Strong collaboration and communication skills, with the ability to work effectively in a cross-functional team - Used to start-up environments - Social competence and a team player - High energy and ambitious - Agile mindset *Application Links* You can work remotely anywhere in the UK, The Netherlands or Spain and you have to be a resident of one of the aforementioned countries and be legally authorized to work there without requiring Veeva’s support for visa or relocation. *If you do not meet this condition, but you think you are an exceptional candidate please clarify it in a separate note and we will consider it.* Spain: https://jobs.lever.co/veeva/2bf92570-a680-40e8-96b0-a8629e3feac7 <https://jobs.lever.co/veeva/61dc60d9-c888-4636-836e-2a75ff9f0567>UK: https://jobs.lever.co/veeva/f0e989b5-9d14-4f82-baaa-2fc56a76ba16 <https://jobs.lever.co/veeva/f0e989b5-9d14-4f82-baaa-2fc56a76ba16> Netherlands: https://jobs.lever.co/veeva/2bf92570-a680-40e8-96b0-a8629e3feac7 1. 2. -- Ehsan Khoddam Data Science Manager - Medical NLP Link Data Science Veeva Systems m +31623213197 ehsan.khoddam(a)veeva.com

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[CFP] Call for Abstract Submissions at the Conference of the German AI Service Centers (KonKIS24)
by Jennifer D'Souza 18 Jun '24

18 Jun '24

[apologies if you received multiple copies of this call] We are pleased to invite abstract submissions for session 3, "Large Language Models," at the upcoming "1st Conference of the German AI Service Centers (KonKIS24)" with a focus on "Advancing Secure AI in Critical Infrastructures for Health and Energy." Please visit the main event page https://events.gwdg.de/event/615/ for more details. We encourage submissions that align with the conference's theme, particularly in the following areas: - *Pretraining Techniques for LLMs*: Exploring foundational strategies and algorithms. - *Testing and Evaluating LLM Fitness*: Methods for assessing performance on well-known tasks and benchmarks. - *Application of LLMs in Scientific Research*: Case studies and examples of LLMs driving discovery and innovation. - *Innovative Insights Generation*: Strategies for leveraging LLMs to generate novel insights and accelerate research outcomes. - *Challenges and Solutions in LLM Application*: Discussing the practical challenges and potential solutions in scientific research. Accepted abstracts will be featured through short presentations during the session. The conference will take place on September 18-19 in picturesque Göttingen. For more information, to submit an abstract, book a stand, or register, please visit the program homepage https://events.gwdg.de/event/615/program. Feel free to contact me (jennifer[dot]dsouza[at]tib[dot]eu) directly with any questions about this session.

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CFP: 7th FEVER workshop and shared task at EMNLP 2024
by A. Vlachos 18 Jun '24

18 Jun '24

Dear all, We are excited to announce the 7th FEVER workshop and shared task collocated with EMNLP 2024. The full CFP is here: https://fever.ai/workshop.html , below are some highlights: New Shared Task: In this year’s workshop we will organise a new fact checking shared task AVeriTeC: A Dataset for Real-world Claim Verification with Evidence from the Web. It will consist of claims that are fact checked using evidence from the web. For each claim, systems must return a label (Supported, Refuted, Not Enough Evidence, Conflicting Evidence/Cherry-picking) and appropriate evidence. The evidence must be retrieved from the document collection provided by the organisers or from the Web (e.g. using a search API). For more information, see our shared task page<https://fever.ai/task.html>. The timeline for it is as follows: * Training/dev data release: April 2024 * Test data release: July 10, 2024 * Shared task deadline: July 20, 2024 * Shared task submission due: August 15, 2024 We invite long and short papers on all topics related to fact extraction and verification, including: * Information Extraction * Semantic Parsing * Knowledge Base Population * Natural Language Inference * Textual Entailment Recognition * Argumentation Mining * Machine Reading and Comprehension * Claim Validation/Fact checking * Question Answering * Information Retrieval and Seeking * Theorem Proving * Stance detection * Adversarial learning * Computational journalism * Descriptions of systems for the FEVER<http://fever.ai/2018/task.html>, FEVER 2.0<http://fever.ai/2019/task.html>, FEVEROUS<https://fever.ai/2021/task.html> and AVERITEC<https://fever.ai/dataset/averitec.html> Shared Tasks Important dates: * Submission deadline: August 15, 2024 (ARR and non-ARR submission deadline) * Commitment deadline: September 23, 2024 * Notification: September 27, 2024 * Camera-ready deadline: October 4, 2024 * Workshop: November 15 or 16, 2024 All deadlines are 11.59 pm UTC -12h ("anywhere on Earth"). Feel free to contact us on our slack channel<https://join.slack.com/t/feverworkshop/shared_invite/zt-4v1hjl8w-Uf4yg~dift…> or via email: fever-organisers(a)googlegroups.com with any questions. Looking forward to your participation! -- The FEVER workshop organizers

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Fwd: Searching participants for my quick study
by Dominik Schlechtweg 18 Jun '24

18 Jun '24

Hi everyone, Please find a request for participation in a very short study of one of my student's bachelor thesis below. Best, Dominik -------- Weitergeleitete Nachricht -------- Betreff: Searching participants for my quick study Datum: Thu, 13 Jun 2024 09:38:22 +0000 Von: Wolkober, Marcel <st163937(a)stud.uni-stuttgart.de> An: dominik.schlechtweg(a)ims.uni-stuttgart.de <dominik.schlechtweg(a)ims.uni-stuttgart.de> Hello! For my bachelor thesis I need participants in my quick online study. It will take approximately 5 to 10 minutes to complete and is in English. You can use your smartphone, but it's recommended to use a PC browser. Here you can get to the study: https://semantic-nlp-captcha.de/study <https://semantic-nlp-captcha.de/study> . Everything else will be explained there. If you have troubles on mobile, activate the desktop mode. It would be of great help if you can forward this study to others, thanks! Best wishes, Marcel Wolkober

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