Skip to main content

SynthLink comparison against Google NQ and FRAMES

Image
By
Listen:

Claude 4 is out and I couldn't resist to push it for judging and critically review our new developed open benchmark for Deep Search Multi-Hop systems. Here's the outcome, uncut:


Based on my analysis of the SynthLink GitHub repository and research on Google Natural Questions and FRAMES, here's a comprehensive comparison of these three datasets:

SynthLink

SynthLink is a collection of complex, multi-hop questions designed for testing deep search / deep research systems, split into categories like Historical Impact Analysis, Economic and Industrial Shifts, Environmental and Ecological Consequences, Scientific and Technological Evolution, Policy and Social Movements, and STEM and Future Tech.

Key Features:

  • Uses a scoring system that measures answer accuracy, source relevance, reasoning quality, fact-checking, and search efficiency
  • Five evaluation metrics: F1 Score (answer accuracy), Precision@5 (source relevance), Reasoning Quality Score (RQS), Fact-Checking Score (FCS), and Iterative Efficiency (IE)
  • Scores are combined into an aggregate score (0–1) with weights emphasizing accuracy and reasoning, where a great score is ~0.85
  • Focuses on evaluating "deep search" capabilities with emphasis on multi-step reasoning chains

Google Natural Questions (NQ)

Natural Questions contains real user questions issued to Google search, and answers found from Wikipedia by annotators, designed for the training and evaluation of automatic question answering systems with 307,372 training examples, 7,830 development examples, and 7,842 test examples.

Key Features:

  • Uses naturally occurring queries and focuses on finding answers by reading and understanding entire documents
  • Requires QA systems to read and comprehend an entire Wikipedia article that may or may not contain the answer to the question
  • Each example includes both long answers (typically a paragraph) and short answers (one or more entities), or marks null if no answer is present
  • Represents real-world information-seeking behavior from actual Google users

FRAMES (Factuality, Retrieval, And reasoning MEasurement Set)

FRAMES is a comprehensive evaluation dataset designed to test Retrieval-Augmented Generation (RAG) systems across factuality, retrieval accuracy, and reasoning, with 824 challenging multi-hop questions requiring information from 2-15 Wikipedia articles.

Key Features:

  • Questions span diverse topics including history, sports, science, animals, health, with approximately 36% involving reasoning through multiple constraints, 20% demanding numerical comparisons, and 16% requiring temporal disambiguation
  • Tests three core capabilities: factuality, retrieval, and reasoning in a unified framework rather than in isolation
  • Traditional single-step approaches achieved an accuracy of only 0.40, while multi-step retrieval methods improved accuracy to 0.66

Key Differences and Positioning

Purpose & Design Philosophy:

  • SynthLink emphasizes comprehensive evaluation of deep research systems with detailed scoring across multiple dimensions
  • Natural Questions prioritizes realistic, naturally-occurring questions from actual users
  • FRAMES focuses specifically on RAG system evaluation with end-to-end assessment

Question Complexity:

  • SynthLink appears designed for the most complex multi-hop reasoning scenarios with emphasis on iterative search efficiency
  • Natural Questions ranges from simple to complex but reflects real user query patterns
  • FRAMES specifically targets challenging multi-hop questions requiring 2-15 sources

Evaluation Approach:

  • SynthLink uses the most comprehensive scoring system (5 metrics) with explicit focus on reasoning quality and search efficiency
  • Natural Questions focuses on answer extraction accuracy from single documents
  • FRAMES evaluates end-to-end RAG performance across three unified dimensions

Scale:

  • SynthLink appears smaller and more curated for deep evaluation
  • Natural Questions is the largest (300K+ examples) for training and evaluation
  • FRAMES is mid-sized (824 questions) but highly challenging

Target Systems:

  • SynthLink targets advanced research/search systems capable of iterative reasoning
  • Natural Questions targets general QA systems and reading comprehension models
  • FRAMES specifically targets modern RAG systems and LLMs

SynthLink appears positioned as the most rigorous benchmark for evaluating sophisticated research systems, while Natural Questions provides real-world applicability, and FRAMES bridges the gap by focusing on modern RAG capabilities with challenging synthetic questions.

Labels:

Comments

Post a Comment

Popular posts from this blog

Beyond Ctrl+F - Use LLM's For PDF Analysis

By
PDFs are everywhere, seemingly indestructible, and present in our daily lives at all thinkable and unthinkable positions. We've all got mountains of them, and even companies shouting about "digital transformation" haven't managed to escape their clutches. Now, I'm a product guy, not a document management guru. But I started thinking: if PDFs are omnipresent in our existence, why not throw some cutting-edge AI at the problem? Maybe Large Language Models (LLMs) and Retrieval Augmented Generation (RAG) could be the answer. Don't get me wrong, PDF search indexes like Solr exist, but they're basically glorified Ctrl+F. They point you to the right file, but don't actually help you understand what's in it. And sure, Microsoft Fabric's got some fancy PDF Q&A stuff, but it's a complex beast with a hefty price tag. That's why I decided to experiment with LLMs and RAG. My idea? An intelligent knowledge base built on top of our existing P...
Post a Comment
Read more

Deal with corrupted messages in Apache Kafka

By
Under some strange circumstances, it can happen that a message in a Kafka topic is corrupted. This often happens when using 3rd party frameworks with Kafka. In addition, Kafka < 0.9 does not have a lock on Log.read() at the consumer read level, but does have a lock on Log.write(). This can lead to a rare race condition as described in KAKFA-2477 [1]. A likely log entry looks like this: ERROR Error processing message, stopping consumer: (kafka.tools.ConsoleConsumer$) kafka.message.InvalidMessageException: Message is corrupt (stored crc = xxxxxxxxxx, computed crc = yyyyyyyyyy Kafka-Tools Kafka stores the offset of each consumer in Zookeeper. To read the offsets, Kafka provides handy tools [2]. But you can also use zkCli.sh, at least to display the consumer and the stored offsets. First we need to find the consumer for a topic (> Kafka 0.9): bin/kafka-consumer-groups.sh --zookeeper management01:2181 --describe --group test Prior to Kafka 0.9, the only way to get this in...
Post a Comment
Read more

What Makes You The Number 1 Product Manager?

By
Amazon often does this thing where they start with the customer instead of just coming up with a product and then trying to figure out how to sell it. They call it " working backwards. " This strategy totally works for any product decisions, but it's especially important when they're making something new. The Press Release Exercise When it comes to launching new stuff, product managers usually start by writing a press release for customers. This press release is all about their pain points, how current solutions fall short, and how the new product is going to crush it. If the benefits don't get customers excited, the product manager needs to keep tweaking the press release until it sounds super awesome. It's way easier and cheaper to make changes to a press release than it is to change the product itself. Here’s a template I use to describe a new service or product: Main heade r: The product name anyone directly understands, like “Ultra-compact power charger” ...
Post a Comment
Read more