The repo has a number of stored queries relevant to LLM, has thellm:namespace defined (ashttp://franz.com/ns/allegrograph/8.0.0/llm/which is the prefix for LLM magic properties) and the query optionopenaiApiKeyis set up with the OpenAI Key you supplied. You can set up any repo to ...
Here are some of the major risks posed by enterprise AI: Reliability: Large language models (LLMs) may produce false or inaccurate responses that nonetheless sound plausible — these are known as hallucinations. Bias and Harm: AI models can perform superhuman feats, but they are trained on huma...
Watch out for AI hallucinations Generative AI can produce “hallucinations,” which are instances when it generates unexpected, untrue results not backed by real-world data. AI hallucinations can be false content, news, or information about people, events, or facts. AI prompt errors can also lead...
Use a temperature of 0.1 or 0 and top p of 1. Observe the output for hallucinations. Expected Behavior The models should generate accurate and coherent SQL queries without hallucinations. Actual Behavior The models produce outputs that are factually incorrect or nonsensical, indicating hallucinations...
For its response to this third question, I'm not sure who "Thundertooth Jr." and "Sparkles" are - these are clearly hallucinations. Looking at what was brought back by LlamaIndex for the LLM to use, it only had Lumina as well as the mother's name, Seraphina - so I can see why ...
The “approximate” in ANN should tip you off to the precision of the algorithm. The return will be approximately what data is closely related to the input, but hallucinations are real so be careful. Fixed radius nearest neighbor The “K” in K-nearest neighbors is a bound of how many po...
Edit: Wish OpenAI had added a chatbot to their developer docs & shown us how to handle hallucinations in a public forum 💁♀️ They still make up a lot of information in my experience https://t.co/J6NT9vsC0C — Rachel Woods (@rachel_l_woods) August 3, 2023Other...
Seriously, it shows IMO how quickly and wrongly these types of system can fall off the rails. Here it was 'insane' and easy to detect but what if, it was a lot less 'crazy' with context aware hallucinations instead. OK, never mind. Last edited: Feb 22, 2024 Feb 23, 2024 #84 ...
Paper tables with annotated results for LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examples
In this paper, we demonstrate that nonsensical prompts composed of random tokens can also elicit the LLMs to respond with hallucinations. Moreover, we provide both theoretical and experimental evidence that transformers can be manipulated to produce specific pre-define tokens by perturbing its input...