The hottest Computational linguistics Substack posts right now

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Five quick updates about that Apple reasoning paper that people can’t stop talking about

Marcus on AI • 9485 implied HN points • 17 Jun 25
🕹 Technology Artificial Intelligence Machine Learning Software Engineering Data science Computational linguistics
  1. A recent paper questions if large language models can really reason deeply, suggesting they struggle with even moderate complexity. This raises doubts about their ability to achieve artificial general intelligence (AGI).
  2. Some responses to this paper have been criticized as weak or even jokes, yet many continue to share them as if they are serious arguments. This shows confusion in the debate surrounding AI reasoning capabilities.
  3. New research supports the idea that AI systems perform poorly when faced with unfamiliar challenges, not just sticking to problems they are already good at solving.

What "language" is a language model a model of?

The Counterfactual • 99 implied HN points • 02 Aug 24
🕹 Technology AI Machine Learning Natural Language Processing Computational linguistics Data science
  1. Language models are trained on specific types of language, known as varieties. This includes different dialects, registers, and periods of language use.
  2. Using a representative training data set is crucial for language models. If the training data isn't diverse, the model can perform poorly for certain groups or languages.
  3. It's important for researchers to clearly specify which language and variety their models are based on. This helps everyone better understand what the model can do and where it might struggle.

Does GPT-3 read between the lines?

The Counterfactual • 39 implied HN points • 19 Sep 22
🕹 Technology AI Language Models Natural Language Processing Computational linguistics Machine Learning
  1. GPT-3 understands 'some' to mean 2 out of 3 letters, but it doesn't change this meaning based on how much information the speaker knows. Humans, however, adjust their understanding based on the context.
  2. When asked if the speaker knows how many letters have checks, GPT-3 gives the right answer if asked before the speaker uses specific words, like 'some' or 'all'. But afterwards, it relies on those words too much.
  3. GPT-3's way of interpreting language is different from how humans do it. It seems to have a fixed meaning for words without considering the situation, unlike humans who use context to understand better.

Aaron Lee's Master Framework: Axiom & Master Formula

Autodidact Obsessions • 4 implied HN points • 17 Feb 24
🔬 Science Logic Language Philosophy Computational linguistics
  1. Aaron Lee's Master Framework explores the relationship between language and logic through his First Axiom, emphasizing the potentiality of language and how it evolves into actual meanings through various logical systems.
  2. The integration of Aaron Lee's Axiom with advanced logical systems like non-monotonic logic, mereology, fuzzy logic, quantum logic, paraconsistent logic, and substructural logic offers a structured model for understanding linguistic semantics and the transition from potential to actual meanings.
  3. The Master Formula resulting from this integration showcases the dynamic nature of belief revision, compositional insights, gradations of truth, probabilistic potential meanings, tolerance for contradictions, and contextual sensitivity, providing deeper insight into the complexities of language and semantics.

Large Language Model Hallucination Mitigation Techniques

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 10 Jan 24
🕹 Technology AI Machine Learning Natural Language Processing Data science Computational linguistics
  1. There are many techniques to prevent hallucinations in large language models. They can be grouped into two types: methods that adjust the model itself and those that change how you ask it questions.
  2. Some effective techniques include using retrieval-augmented generation and prompting the model carefully. This means providing clear context and expected outcomes before asking for information.
  3. To best reduce hallucinations, combining different strategies is key. No single method works perfectly, so using a mix of approaches helps improve the model's accuracy and reliability.
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Synthetic dataset generation techniques: Self-Instruct

machinelearninglibrarian • 0 implied HN points • 15 May 24
🕹 Technology Machine Learning Artificial Intelligence Data generation Natural Language Processing Computational linguistics
  1. Self-Instruct helps create large sets of instructional data by using language models to generate instructions from initial examples. This saves a lot of time compared to writing everything by hand.
  2. The process involves generating new instructions from a seed dataset, filtering them, and ensuring diversity to avoid repetitive prompts. This way, the dataset expands effectively.
  3. The method is widely adopted in both research and practical applications, showing that using machine-generated data can improve instruction-following models without extensive manual input.