🏛️ Give AI the Error Point & It Will Correct the Whole World
💟 Can AI make its way through Wonderland?
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Welcome to AI Fridays, your weekly round-up of the boldest AI breakthroughs and innovations brought to you by HackerPulse and AIModels.fyi!
With AI Fridays, you’re spoilt for choice with all the latest happenings in AI.
Let’s dig in!
👸🏼 Alice in Wonderland: AI Language Models Fail Simple Reasoning Tests
⚠️ LLMs Can’t Find Reasoning Errors, but Can Correct Them Given the Error Location
♨️ Thermodynamic Linear Algebra
📈 Scalable Matmul-Free Language Modeling
🎆 Magicoder: Empowering Code Generation with OSS-Instruct
Alice in Wonderland: Simple Tasks Showing Complete Reasoning Breakdown In State-Of-the-Art LLMs (🔗 Read Paper)
A recent study investigates the limitations of state-of-the-art large language models (LLMs) in performing simple reasoning tasks, using “Alice in Wonderland” as a case study. Researchers found that even the most advanced LLMs struggle with straightforward logical reasoning and task completion when faced with the story's simple, fantastical scenarios. The models often provided overconfident yet nonsensical explanations for their incorrect answers. These findings highlight the significant gap between LLMs' impressive language generation capabilities and their ability to engage in true reasoning and problem-solving. Standard interventions, like enhanced prompting and multi-step re-evaluation, failed to improve their performance. This suggests an urgent need for re-assessment of current LLM evaluation procedures and the creation of benchmarks to detect reasoning deficits. The study's code and data are available here.
LLMs Cannot Find Reasoning Errors, but Can Correct Them Given the Error Location (🔗 Read Paper)
Recent research reveals that attempts to have large language models (LLMs) self-correct logical or reasoning errors often lead to worse overall performance, despite success in improving style and quality. The study shows that this poor performance is due to LLMs' inability to find logical mistakes rather than their capacity to correct them. Benchmarking several state-of-the-art models found they generally struggle to identify errors, even in clear-cut cases. However, when provided with ground truth mistake locations, LLMs demonstrated robust correction abilities, significantly boosting task performance. The authors also showed that training a small classifier with out-of-domain data to locate mistakes outperforms prompting a large model. To support further research, they released the BIG-Bench Mistake dataset of LLM-generated logical errors.
Thermodynamic Linear Algebra (🔗 Read Paper)
A new study explores using classical thermodynamics to accelerate linear algebra, which is essential for many algorithms in engineering, science, and machine learning. Traditional methods are slow, and while quantum computing holds promise, the resource requirements are currently too high.
Instead, researchers propose thermodynamic algorithms that connect solving linear algebra problems to the equilibrium state of coupled harmonic oscillators. These algorithms show significant speedups over digital methods, scaling with matrix size. The approach leverages principles like ergodicity and entropy, highlighting a deep connection between thermodynamics and linear algebra. This breakthrough opens possibilities for new, efficient computing hardware based on thermodynamic principles.
Scalable Matmul-Free Language Modeling (🔗 Read Paper)
This paper presents a novel language modeling approach that avoids the computationally expensive matrix multiplication (MatMul) operations typically used in transformer-based models. The proposed method, called Scalable MatMul-Free Language Modeling, aims to improve the efficiency and scalability of large language models (LLMs) without sacrificing performance. Key innovations include the use of Transformer-Lite and Integer-only Inference to enable efficient model execution. Experiments show that these MatMul-free models achieve performance on par with state-of-the-art transformers up to 2.7 billion parameters while reducing memory usage significantly. A GPU-efficient implementation further cuts memory consumption by over 10 times compared to unoptimized models, highlighting the potential for more efficient computational methods in AI.
Magicoder: Empowering Code Generation With OSS-Instruct (🔗 Read Paper)
Researchers have introduced Magicoder, a series of fully open-source Large Language Models (LLMs) for code that rival top code models with no more than 7 billion parameters. These models are trained on 75,000 synthetic instruction data using OSS-Instruct, a novel method utilizing open-source code snippets to generate diverse training data. The goal is to reduce bias in synthetic data and produce more realistic and controllable outputs. Magicoder models, including an enhanced version called MagicoderS, outperform state-of-the-art code models on various coding benchmarks, even surpassing ChatGPT in some tasks. This pioneering use of OSS-Instruct signals a new era in crafting synthetic instruction data for code, tapping into vast open-source resources to drive AI advancements in software development. The full project, including code, weights, and data, is available for public use, promising significant advancements in AI-powered code generation.
🎬 And that’s a wrap. Share the fun with your pal and stay tuned for the latest in the AI world!