The hottest Evaluation Substack posts right now

And their main takeaways
Category
Top Technology Topics

A safe harbor for AI evaluation and red teaming

AI Snake Oil • 307 implied HN points • 05 Mar 24
🕹 Technology AI Research Safety Evaluation Models
  1. Independent evaluation of AI models is crucial for uncovering vulnerabilities and ensuring safety, security, and trust
  2. Terms of service can discourage community-led evaluations of AI models, hindering essential research
  3. A legal and technical safe harbor is proposed to protect and encourage public interest research into AI safety, removing barriers and improving ecosystem norms

Will AI transform law?

AI Snake Oil • 648 implied HN points • 24 Jan 24
🕹 Technology AI Legal Predictions Research Evaluation
  1. The idea of AI replacing lawyers is plausible but not well-supported by current evidence.
  2. Applications of AI in law can be categorized into information processing, creativity/judgment tasks, and predicting the future.
  3. Evaluation of AI in law needs to advance beyond static benchmarks to real-world deployment scenarios.

📝 Guest Post: Evaluating LLM Applications*

TheSequence • 91 implied HN points • 11 Mar 24
🕹 Technology AI Machine Learning Evaluation Development Monitoring
  1. Traditional software development practices like automation and testing suites are valuable when evaluating Large Language Models (LLMs) for AI applications.
  2. Different types of evaluations, including judgment return types and sources, are important for assessing LLMs effectively.
  3. A robust evaluation process for LLM applications involves interactive, batch offline, and monitoring online stages to support rapid iteration cycles and performance improvements.
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Can Large Language Models Reason?

AI: A Guide for Thinking Humans • 4 HN points • 10 Sep 23
🔬 Science Reasoning Language Models Evaluation Pattern matching
  1. There is a debate about whether large language models have reasoning abilities similar to humans or rely more on memorization and pattern-matching.
  2. Models like CoT prompting try to elicit reasoning abilities in these language models and can enhance their performance.
  3. However, studies suggest that these models may rely more on memorization and pattern-matching from their training data than true abstract reasoning.

Step By Step Parsing of Mathematical Expressions From Scratch

Luminotes • 7 implied HN points • 21 Mar 23
🕹 Technology Mathematics Programming Algorithms Evaluation Implementation
  1. Balancing parenthesis in mathematical expressions can be achieved by using an array or stack.
  2. Identifying the correct operators, like handling "**" for power of, is crucial for parsing expressions accurately.
  3. Converting mathematical expressions to postfix notation simplifies the evaluation process, removing the need for brackets.

Issues with MATH()

The Irregular Voice • 0 implied HN points • 01 Apr 24
🔬 Science Mathematics Evaluation Language Models
  1. Some math problems in the MATH() dataset have incorrect answers marked during evaluation, possibly due to bugs in question generation or solution calculation code.
  2. Certain math problems in the MATH() dataset are overly complex, requiring lengthy computations or involving very large numbers, making them challenging for un-augmented language models.
  3. The MATH() dataset includes math problems with arithmetic or factorization involving extremely large numbers, which may not accurately test a language model's mathematical reasoning ability.

Death by RAG Evals

ScaleDown • 0 implied HN points • 31 Jan 24
🕹 Technology AI Evaluation LLMs Metrics Costs
  1. Evaluating RAG (Retrieval-Augmented Generation) systems is challenging due to the need for assessing accuracy, relevance, and context retrieval.
  2. Human annotation is accurate but time-consuming, error-prone, and not suitable for real-time systems.
  3. The evaluation process for RAG systems can be resource-intensive, time-consuming, and costly, impacting latency and efficiency.

Designing Better Evaluations of Generative Models

Tom’s Substack • 0 implied HN points • 11 Nov 23
🕹 Technology AI/ML Evaluation Generative models Red-Teaming
  1. Evaluation of models should focus on selecting the best performing model, giving confidence in AI outputs, identifying safety and ethical issues, and providing actionable insights for improvement.
  2. Standard evaluation approaches face challenges like broad performance metrics, data leakage from benchmarks, and lack of contextual understanding.
  3. To improve evaluations, embrace human-centered evaluation methods and red-teaming to understand user perceptions, uncover vulnerabilities, and ensure models are safe and effective.

Gemma Gemma Gemma

Over-Nite Evaluation • 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.

OLMo: Accelerating the Science of Language Models

Gonzo ML • 0 implied HN points • 10 Mar 24
🔬 Science Language Models Research Open Source Training Evaluation
  1. OLMo is an open language model created by Allen AI, differentiating itself by being completely open-source including logs, checkpoints, and evaluation scripts under the Apache 2.0 License.
  2. OLMo comprises three models: 1B, 7B, and 65B, demonstrating improvements in classic transformer decoders similar to GPT, such as specific tokenization for PII and non-parametric layer normalization.
  3. OLMo was trained on data from their own dataset Dolma with plans to expand beyond English, showcasing their training process with PyTorch FSDP and evaluation using their benchmark Paloma and the Catwalk framework.

[DeepMind SIMA] Scaling Instructable Agents Across Many Simulated Worlds

Gonzo ML • 0 implied HN points • 17 Mar 24
🕹 Technology AI Evaluation
  1. DeepMind developed SIMA, an agent that follows language instructions and operates in diverse 3D virtual environments using only keyboard and mouse commands.
  2. SIMA is trained on behavioral cloning and predictive models, with a focus on rich language interactions and interdisciplinary learning.
  3. Evaluation of SIMA involved overcoming challenges like asynchronous environments, and the agent showed promising results and varied performance across different tasks and environments.