My personal observation as a researcher is that LLMs are genuinely helpful in answering well-studied questions in areas where my own understanding is short of the research frontier. But as soon as I ask about anything beyond that frontier, I get nothing of use.
LLMS
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Multi-hop Reasoning and Tool Use Benchmark in Language Models
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Answering this question correctly is a good mini benchmark challenge, IMO. It requires multi-hop search and reasoning (since the answer isn't directly in the Llama-2 paper, so requires checking its references too) and tools (since it requires doing multi-digit arithmetic).
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Bing’s False Claims About Llama-2 Attention Mechanisms
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Bing claims that Llama-2 doesn't use attention (not true), links to an irrelevant paper, and provides the world's least-useful table. Although it does have a handy button to export it to Excel…
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LLM Prompt Challenge: Can Models Analyze Llama 2 Architecture?
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Can anyone find a way to get any LLM to follow this prompt?:
"Compare the number of params in attention modules vs MLPs in the recently released llama 2 model." GPT-4 has no success, even with a lot of help. -
7 Tips to Mitigate Hallucinations in Large Language Models
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Looking for more practical advice on overcoming the challenges with LLMs? Check out the newsletter at https://
louisbouchard.substack.com/p/7-tips-to-mi
tigate-hallucinations
… Subscribe to the newsletter for more AI insights! -
Cost-Effective Output Refinement Through Parameter Adjustment and Prompt Engineering
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Refining outputs is cost-effective through adjusting parameters like temperature and penalties. Implementing prompt engineering can control hallucinations and biases, promoting cautious, correct outputs rather than forced errors.
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Combat AI Hallucinations with RAG and Deep Memory Solutions
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Facing persistent hallucinations? Try Retrieval Augmented Generation or Deep Memory by @activeloop . It embeds input data, cross-references with documents and only answers when it matches, limiting biases.
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LLMs Hallucinations: Data Quality and Source Diversity Solutions
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LLMs often make up answers, intending to complete the text instead of recognizing their flaws. Mitigating these biases and illusory tendencies requires careful data cleaning and the use of varied, reliable sources.
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AI Model Hallucinations and Biases: Major LLM Challenges
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Is your AI model giving you unexpected results? Hallucinations and biases are the biggest challenges we face with large language models (LLMs) like ChatGPT. The gravity of the problem intensifies, especially when these models power commercial applications.
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GPT Timeouts During Content Generation: Network Issues and Solutions
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What are people doing when their GPT’s time out while outputting content? I’m constantly getting network error and only option to re-generate and not have the ability to tell it to continue. Asking for a friend