Sources

Author

Kamil Filipek

Complete Bibliography

The references below cover all sources cited or recommended across the chapters of this course. They are organized by thematic area corresponding to the course structure.

Language, Machines, and Theoretical Foundations

  • Chomsky, N. (1957). Syntactic Structures. Mouton.
  • Jurafsky, D., & Martin, J. H. (2025). Speech and Language Processing (3rd ed. draft). Stanford University. https://web.stanford.edu/~jurafsky/slp3/
  • Manning, C. D., & Schütze, H. (1999). Foundations of Statistical Natural Language Processing. MIT Press.
  • Russell, S., & Norvig, P. (2010). Artificial Intelligence: A Modern Approach (3rd ed.). Prentice Hall.

Basic Data Engineering and Preprocessing

  • Bird, S., Klein, E., & Loper, E. (2009). Natural Language Processing with Python. O’Reilly Media. https://www.nltk.org/book/
  • Bostrom, K., & Durrett, G. (2020). Byte Pair Encoding is Suboptimal for Language Model Pretraining. Findings of EMNLP.
  • Sennrich, R., Haddow, B., & Birch, A. (2016). Neural Machine Translation of Rare Words with Subword Units. ACL.

Evolution of Embeddings

  • Harris, Z. S. (1954). Distributional Structure. Word, 10(2–3), 146–162.
  • Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space. ICLR. arXiv:1301.3781
  • Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013). Distributed Representations of Words and Phrases and their Compositionality. NeurIPS.
  • Pennington, J., Socher, R., & Manning, C. D. (2014). GloVe: Global Vectors for Word Representation. EMNLP.
  • Bojanowski, P., Grave, E., Joulin, A., & Mikolov, T. (2017). Enriching Word Vectors with Subword Information. Transactions of the ACL, 5, 135–146.

Contextual Embeddings

  • Peters, M. E., et al. (2018). Deep Contextualized Word Representations. NAACL. arXiv:1802.05365
  • Vaswani, A., et al. (2017). Attention Is All You Need. NeurIPS.
  • Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL. arXiv:1810.04805
  • Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018). Improving Language Understanding by Generative Pre-Training. OpenAI.
  • Radford, A., et al. (2019). Language Models are Unsupervised Multitask Learners. OpenAI.
  • Brown, T. B., et al. (2020). Language Models are Few-Shot Learners. NeurIPS. arXiv:2005.14165
  • Liu, Y., et al. (2019). RoBERTa: A Robustly Optimized BERT Pretraining Approach. arXiv:1907.11692
  • Sanh, V., Debut, L., Chaumond, J., & Wolf, T. (2019). DistilBERT, a distilled version of BERT. NeurIPS EMC2 Workshop.

Elementary Language Analysis

  • Ekman, P. (1972). Universals and Cultural Differences in Facial Expressions of Emotion. Nebraska Symposium on Motivation. University of Nebraska Press.
  • Ekman, P. (2003). Emotions Revealed. Times Books.
  • Russell, J. A. (1980). A Circumplex Model of Affect. Journal of Personality and Social Psychology, 39(6), 1161–1178.
  • Plutchik, R. (1980). Emotion: A Psychoevolutionary Synthesis. Harper and Row.
  • Barrett, L. F. (2017). How Emotions Are Made: The Secret Life of the Brain. Houghton Mifflin.
  • Barrett, L. F., Adolphs, R., Marsella, S., Martinez, A. M., & Pollak, S. D. (2019). Emotional Expressions Reconsidered: Challenges to Inferring Emotion from Human Facial Movements. Psychological Science in the Public Interest, 20(1), 1–68.
  • Crawford, K. (2021). Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. Yale University Press.
  • Hutto, C. J., & Gilbert, E. (2014). VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. ICWSM.
  • Mohammad, S. M., & Turney, P. D. (2013). Crowdsourcing a Word-Emotion Association Lexicon. Computational Intelligence, 29(3), 436–465.
  • Campos, R., et al. (2020). YAKE! Keyword Extraction from Single Documents using Multiple Local Features. Information Sciences, 509, 257–289.
  • Grootendorst, M. (2020). KeyBERT: Minimal keyword extraction with BERT. Zenodo. https://doi.org/10.5281/zenodo.4461265

Topic Modeling

  • Deerwester, S., Dumais, S. T., Furnas, G. W., Landauer, T. K., & Harshman, R. (1990). Indexing by Latent Semantic Analysis. Journal of the American Society for Information Science, 41(6), 391–407.
  • Lazarsfeld, P. F., & Henry, N. W. (1968). Latent Structure Analysis. Houghton Mifflin.
  • Lee, D. D., & Seung, H. S. (1999). Learning the Parts of Objects by Non-Negative Matrix Factorization. Nature, 401, 788–791.
  • Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.
  • Řehůřek, R., & Sojka, P. (2010). Software Framework for Topic Modelling with Large Corpora. LREC Workshop on New Challenges for NLP Frameworks.

Working with LLMs

  • Ouyang, L., et al. (2022). Training Language Models to Follow Instructions with Human Feedback. NeurIPS. arXiv:2203.02155
  • Wei, J., et al. (2022). Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. NeurIPS. arXiv:2201.11903
  • Kojima, T., Gu, S. S., Reid, M., Matsuo, Y., & Iwasawa, Y. (2022). Large Language Models are Zero-Shot Reasoners. NeurIPS. arXiv:2205.11916
  • Lewis, P., et al. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. NeurIPS. arXiv:2005.11401
  • Hu, E. J., et al. (2022). LoRA: Low-Rank Adaptation of Large Language Models. ICLR. arXiv:2106.09685
  • Dettmers, T., Pagnoni, A., Holtzman, A., & Zettlemoyer, L. (2023). QLoRA: Efficient Finetuning of Quantized LLMs. NeurIPS. arXiv:2305.14314
  • Papineni, K., Roukos, S., Ward, T., & Zhu, W.-J. (2002). BLEU: a Method for Automatic Evaluation of Machine Translation. ACL.
  • Lin, C.-Y. (2004). ROUGE: A Package for Automatic Evaluation of Summaries. ACL Workshop on Text Summarization.
  • Zhang, T., et al. (2020). BERTScore: Evaluating Text Generation with BERT. ICLR. arXiv:1904.09675

AI Agents

  • Sutton, R. S., & Barto, A. G. (1998). Reinforcement Learning: An Introduction. MIT Press.
  • Bond, A. H., & Gasser, L. (1988). Readings in Distributed Artificial Intelligence. Morgan Kaufmann.
  • Nakano, R., et al. (2021). WebGPT: Browser-assisted question-answering with human feedback. arXiv:2112.09332
  • Yao, S., et al. (2023). ReAct: Synergizing Reasoning and Acting in Language Models. ICLR. arXiv:2210.03629
  • Park, J. S., et al. (2023). Generative Agents: Interactive Simulacra of Human Behavior. UIST. arXiv:2304.03442
  • Wu, Q., et al. (2023). AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation. arXiv:2308.08155
  • Hong, S., et al. (2023). MetaGPT: Meta Programming for a Multi-Agent Collaborative Framework. arXiv:2308.00352
  • Weng, L. (2023). LLM-powered Autonomous Agents. Lilian Weng’s Blog. https://lilianweng.github.io/posts/2023-06-23-agent/
  • n8n.io. (2024). n8n Documentation. https://docs.n8n.io

All URLs verified as of April 2025. arXiv preprint versions are cited where journal versions are not yet available.