The blog post details how to use Google's Gemini Pro and other large language models (LLMs) for creative writing, specifically focusing on generating poetry. The author demonstrates how to "hallucinate" text with these models by providing evocative prompts related to existing literary works like Shakespeare's Sonnet 3.7 and two other poems labeled "o1" and "o3." The process involves using specific prompting techniques, including detailed scene setting and instructing the LLM to adopt the style of a given author or work. The post aims to make these powerful creative tools more accessible by explaining the methods in a straightforward manner and providing code examples for using the Gemini API.
The blog post details the author's successful attempt at getting OpenAI's language model, specifically GPT-3 (codenamed "o1"), to play the board game Codenames. The author found the AI remarkably adept at the game, demonstrating a strong grasp of word association, nuance, and even the ability to provide clues with appropriate "sneekiness" to mislead the opposing team. Through careful prompt engineering and a structured representation of the game state, the AI was able to both give and interpret clues effectively, leading the author to declare it a "super good" Codenames player. The author expresses excitement about the potential for AI in board games and the surprising level of strategic thinking exhibited by the language model.
HN users generally agreed that the demo was impressive, showcasing the model's ability to grasp complex word associations and game mechanics. Some expressed skepticism about whether the AI truly "understood" the game or was simply statistically correlating words, while others praised the author's clever prompting. Several commenters discussed the potential for future AI development in gaming, including personalized difficulty levels and even entirely AI-generated games. One compelling comment highlighted the significant progress in natural language processing, contrasting this demo with previous attempts at AI playing Codenames. Another questioned the fairness of judging the AI based on a single, potentially cherry-picked example, suggesting more rigorous testing is needed. There was also discussion about the ethics of using large language models for entertainment, given their environmental impact and potential societal consequences.
Summary of Comments ( 26 )
https://news.ycombinator.com/item?id=43222027
Hacker News commenters discussed the accessibility of the "hallucination" examples provided in the linked article, appreciating the clear demonstrations of large language model limitations. Some pointed out that these examples, while showcasing flaws, also highlight the potential for manipulation and the need for careful prompting. Others discussed the nature of "hallucination" itself, debating whether it's a misnomer and suggesting alternative terms like "confabulation" might be more appropriate. Several users shared their own experiences with similar unexpected LLM outputs, contributing anecdotes that corroborated the author's findings. The difficulty in accurately defining and measuring these issues was also raised, with commenters acknowledging the ongoing challenge of evaluating and improving LLM reliability.
The Hacker News post titled "Making o1, o3, and Sonnet 3.7 Hallucinate for Everyone" (https://news.ycombinator.com/item?id=43222027) has several comments discussing the linked article about prompting language models to produce nonsensical or unexpected outputs.
Several commenters discuss the nature of "hallucination" in large language models, debating whether the term is appropriate or if it anthropomorphizes the models too much. One commenter suggests "confabulation" might be a better term, as it describes the fabrication of information without the intent to deceive, which aligns better with how these models function. Another commenter points out that these models are essentially sophisticated prediction machines, and the outputs are just statistically likely sequences of words, not actual "hallucinations" in the human sense.
There's a discussion about the potential implications of this behavior, with some commenters expressing concern about the spread of misinformation and the erosion of trust in online content. The ease with which these models can generate convincing yet false information is seen as a potential problem. Another commenter argues that these "hallucinations" are simply a reflection of the biases and inconsistencies present in the training data.
Some commenters delve into the technical aspects of the article, discussing the specific prompts used and how they might be triggering these unexpected outputs. One commenter mentions the concept of "adversarial examples" in machine learning, where carefully crafted inputs can cause models to behave erratically. Another commenter questions whether these examples are truly "hallucinations" or just the model trying to complete a nonsensical prompt in the most statistically probable way.
A few comments also touch on the broader ethical implications of large language models and their potential impact on society. The ability to generate convincing fake text is seen as a powerful tool that can be used for both good and bad purposes. The need for better detection and mitigation strategies is highlighted by several commenters.
Finally, some comments provide additional resources and links related to the topic, including papers on adversarial examples and discussions on other forums about language model behavior. Overall, the comments section provides a lively discussion on the topic of "hallucinations" in large language models, covering various aspects from technical details to ethical implications.