The hottest Evaluation Substack posts right now

And their main takeaways
Category
Top Technology Topics

The Sequence Knowledge #550: Let's Talk About Safety Benchmarks

TheSequence • 42 implied HN points • 27 May 25
🕹 Technology AI safety Machine Learning Benchmarks Evaluation Risk Assessment
  1. Safety benchmarks are important tools that help evaluate AI systems. They make sure these systems are safe as they become more advanced.
  2. Different organizations have created their own frameworks to assess AI safety. Each framework focuses on different aspects of how AI systems can be safe.
  3. Understanding and using safety benchmarks is essential for responsible AI development. This helps manage risks and ensure that AI helps, rather than harms.

2024 Interconnects year in review

Democratizing Automation • 229 implied HN points • 31 Dec 24
🕹 Technology AI Policy Open Source Modeling Evaluation
  1. In 2024, AI continued to be the hottest topic, with major changes expected from OpenAI's new model. This shift will affect how AI is developed and used in the future.
  2. Writing regularly helped to clarify key AI ideas and track their importance. The focus areas included reinforcement learning, open-source AI, and new model releases.
  3. The landscape of open-source AI is changing, with fewer players and increased restrictions, which could impact its growth and collaboration opportunities.

The Sequence Knowledge # 555: Not All Benchmark are that Simple: An Intro to Multiturn Benchmarks

TheSequence • 14 implied HN points • 03 Jun 25
🕹 Technology AI Machine Learning Evaluation Natural Language Benchmarks
  1. Multi-turn benchmarks are important for testing AI because they make AIs more like real conversation partners. They help AIs keep track of what has already been said, making the chat more natural.
  2. These benchmarks are different from regular tests because they don’t just check if the AI can answer a question; they see if it can handle ongoing dialogue and adapt to new information.
  3. One big challenge for AIs is remembering details from previous chats. It's tough for them to keep everything consistent, but it's necessary for good performance in conversations.

GraphRAG Analysis, Part 1: How Indexing Elevates Knowledge Graph Performance in RAG

AI Encoder: Parsing Signal from Hype • 70 HN points • 09 Jul 24
🕹 Technology Analysis Research Evaluation Modeling Metrics
  1. Knowledge graphs do not significantly impact context retrieval in RAG, as all methods showed similar context relevancy scores.
  2. Neo4j with its own index improved answer relevancy and faithfulness compared to Neo4j without indexing and FAISS, showcasing the importance of effective indexing for precise content retrieval in RAG applications.
  3. Developers need to consider the trade-offs between ROI constraints and performance improvements when deciding to use GraphRAG, especially in high-precision applications that require accurate answers.

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.
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The NIH and Data . . .

The Good Science Project • 22 implied HN points • 25 Dec 24
🔬 Science Research Data Innovation Metrics Evaluation
  1. The NIH is starting a program to give scholars access to its internal data. This will help them answer important questions about the economic impact and effectiveness of research policies.
  2. They are creating a new metric called the S-index to reward scientists for sharing data with the wider community. This aims to encourage more collaboration rather than just focusing on personal achievements.
  3. The NIH is offering a $1 million prize for innovative ideas on how to implement the S-index metric, encouraging creativity and participation from the scientific community.

The Sequence Opinion #485: What's Wrong With AI Benchmarks

TheSequence • 56 implied HN points • 06 Feb 25
🕹 Technology AI Data Evaluation Machine Learning Benchmarking
  1. AI benchmarks are currently facing issues like data contamination and memorization, which affect how accurately they evaluate models. It's important to find better ways to test these systems.
  2. New benchmarks are popping up all the time, making it hard to keep track of what each one measures. This could lead to confusion in understanding AI capabilities.
  3. There's a need for clearer and more standard methods in AI evaluation to really see how well these models perform and improve their reliability.

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

Why I’m excited about AI-assisted human feedback

Musings on the Alignment Problem • 459 implied HN points • 29 Mar 22
🕹 Technology AI Feedback Machine Learning Automation Evaluation
  1. The use of reinforcement learning from human feedback (RLHF) has been successful in aligning models with human intent like following instructions.
  2. Training AI systems on tasks that are hard for humans to evaluate may not be directly solvable with RLHF due to challenges in generalization and evaluation.
  3. AI-assisted human feedback, like recursive reward modeling (RRM), can help tackle complex tasks by involving human evaluation in aligning AI systems.

📝 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.

How Should Large Language Models Be Evaluated?

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 19 implied HN points • 06 Nov 23
🕹 Technology AI Machine Learning Data science Natural Language Processing Evaluation
  1. When evaluating large language models (LLMs), it's important to define what you're trying to achieve. Know the problems you're solving so you can measure success and failure.
  2. Choosing the right data is crucial for evaluating LLMs. You'll need to think about what data to use and how it will be delivered in your application.
  3. The process of evaluation can be automated or involve human input. Deciding how to implement this process is key to building effective LLM applications.

The problem with how we evaluate LLMs

Conrado Miranda • 2 HN points • 28 May 24
🕹 Technology LLMs Evaluation Language Models Research
  1. Evaluating Large Language Models (LLMs) can be challenging, especially with traditional off-the-shelf metrics not always being suitable for broader LLM applications.
  2. Using an LLM-as-a-judge method for evaluation can provide insights, but there's a risk of over-reliance on the black-box model, leading to potential lack of understanding on improvements.
  3. Creating clear, specific evaluation criteria and considering use cases are crucial. Auto-criteria, like auto-prompting, may be future tools to enhance LLM evaluations.

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.

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.

How Should Large Language Models Be Evaluated?

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 01 Nov 23
🕹 Technology AI NLP Evaluation Safety Taxonomy
  1. Large Language Models (LLMs) should be evaluated based on their knowledge, alignment, and safety. This helps ensure they meet necessary standards.
  2. Evaluation has become more complex as LLMs can do higher-level tasks, rather than just basic language checks like syntax and vocabulary.
  3. Creating a clear taxonomy for LLM evaluation helps guide researchers and companies in assessing these models effectively.

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.

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.

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.

[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.

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.

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.

Society & Technology: Evaluation

The Digital Anthropologist • 0 implied HN points • 01 Mar 24
🕹 Technology Society Evaluation Adaptation Social Impact
  1. Before society fully adapts to a new technology, there is a crucial evaluation phase to understand its impact.
  2. Technologies, like societies, are ever-evolving and start reflecting values and power dynamics during the evaluation phase.
  3. During the evaluation phase, societies begin considering the positives and negatives of a technology and start to modify social norms accordingly.