Over-Nite Evaluation

Do what others don't want to. Read pieces on making sense of computing and leadership. Monthly, I guess.

The hottest Substack posts of Over-Nite Evaluation

And their main takeaways

Chasing the Numbers: The Puzzle of AI Benchmarks

3 HN points • 04 Sep 23
  1. Compute-intensive black box AI models face challenges with data bottlenecks and evaluations
  2. NLP evaluation methodologies have evolved from specific pipelines to multi-task benchmarks like GLUE and SuperGLUE
  3. Models like BERT and GPT have changed the field but evaluating their capabilities requires out-of-distribution tasks, human-scored leaderboards, and red-teaming

Implicit Grammar: Language Modeling for Spell-Checking

1 HN point • 30 Sep 23
  1. Language models can help with spell-checking by analyzing the popularity of phrases on the internet.
  2. Language modeling computes the probability of word sequences and is a fundamental part of natural language processing.
  3. Using n-grams in language modeling can help with spell-checking by comparing the occurrence of phrases in real texts.

Reinforcement Learning from Human Feedback: When the Math Ain't Enough

1 HN point • 11 Aug 23
  1. Annotation is important in reinforcement learning from human feedback to align models with our preferences and evaluate outputs.
  2. For supervised fine-tuning, focus on smaller datasets of higher quality with good prompts and responses rather than larger uncontrolled datasets.
  3. Annotators must understand the task the same way as you do, so use synthetic data and cross-checks for quality control during annotation.

LLMFAO: Pairwise Human Ranking of Large Language Models

0 implied HN points • 10 Oct 23
  1. The study used pairwise human ranking to evaluate large language models.
  2. The top-performing model was GPT-4 by OpenAI, showing superiority over others.
  3. The results highlighted the importance of human judgment in evaluating language models.

Interviewing Researchers with Passion and Ability

0 implied HN points • 02 Aug 23
  1. When hiring researchers, focus on evaluating their passion and ability through specific interview questions.
  2. Researchers in the industry can be classified into research scientists, applied scientists, and research engineers.
  3. Interview process for researchers should include questions about the candidate's research experience and track record.
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Making Sense of Graphs with Watset

0 implied HN points • 31 Dec 23
  1. Watset is a graph clustering algorithm for making sense of graphs by identifying multiple meanings of words.
  2. This algorithm extracts and clusters neighborhoods of nodes in a graph to discover different senses of words.
  3. Watset outperformed other methods in tasks like synonymy discovery and frame semantics, providing accurate clusters of word senses.

Transition to Management in Technology

0 implied HN points • 05 Nov 23
  1. Communication is crucial for being a successful manager.
  2. Taking on challenging tasks can lead to more opportunities.
  3. Understanding organizational goals and collaborating effectively with others is key for career advancement.

Gemma Gemma Gemma

0 implied HN points • 26 Feb 24
🕹 Technology AI Open Source Benchmarking Evaluation Licensing
  1. Licensing agreements for pre-trained models like Gemma might need to find a better balance between protecting owners and encouraging innovation.
  2. Gemma's performance comparisons show it aligns with existing models in specific tasks, but more evaluation beyond familiar benchmarks is necessary.
  3. Gemma's release signifies Google's investment in the open large language model ecosystem, with future emphasis on model safety and hosting services.