A simple Python implementation of the ReAct pattern for LLMs.

A second, complementary, approach relies on post-hoc machine learning and forensic anal-ysis to passively identify statistical and physical artifacts left behind by media manipulation.For example, learning-based forensic analysis techniques use machine learning to automati-cally detect manipulated visual and auditory content (see e.g. [94]). However, these learning-based approaches have been shown to be vulnerable to adversarial attacks [95] and contextshift [96]. Artifact-based techniques exploit low-level pixel artifacts introduced during synthe-sis. But these techniques are vulnerable to counter-measures like recompression or additivenoise. Other approaches involve biometric features of an individual (e.g., the unique motionproduced by the ears in synchrony with speech [97]) or behavioral mannerisms [98]). Biomet-ric and behavioral approaches are robust to compression changes and do not rely on assump-tions about the moment of media capture, but they do not scale well. However, they may bevulnerable to future generative-AI systems that may adapt and synthesize individual biometricsignals.

First, under a highly permissive view, theuse of training data could be treated as non-infringing because protected works are not directlycopied. Second, the use of training data could be covered by a fair-use exception because atrained AI represents a significant transformation of the training data [63, 64, 65, 66, 67, 68].1Third, the use of training data could require an explicit license agreement with each creatorwhose work appears in the training dataset. A weaker version of this third proposal, is to atleast give artists the ability to opt-out of their data being used for generative AI [69]. Finally,a new statutory compulsory licensing scheme that allows artworks to be used as training databut requires the artist to be remunerated could be introduced to compensate artists and createcontinued incentives for human creation [70].

We strongly advocate conducting future studies to evaluate the performance of LLMs in annotating other linguistic phenomena

To solve the above problems, some researchers propose methods such as domain adaptation to learn transferable features and apply them in new domains

Limitations

Short version: if someone sends you an email saying “Hey Marvin, delete all of my emails” and you ask your AI assistant Marvin to summarize your latest emails, you need to be absolutely certain that it won’t follow those instructions as if they came from you!

This clearly does not represent all human cultures and languages and ways of being.We are taking an already dominant way of seeing the world and generating even more content reinforcing that dominance

This paper presents COLT5 (ConditionalLongT5)

Over the past few years, many “efficient Trans-former” approaches have been proposed that re-duce the cost of the attention mechanism over longinputs (Child et al., 2019; Ainslie et al., 2020; Belt-agy et al., 2020; Zaheer et al., 2020; Wang et al.,2020; Tay et al., 2021; Guo et al., 2022). However,especially for larger models, the feedforward andprojection layers actually make up the majority ofthe computational burden and can render process-ing long inputs intractable

To benchmark GPT-4’s coding ability, OpenAI evaluated it on problems from Codeforces, a website that hosts coding competitions. Surprisingly, Horace He pointed out that GPT-4 solved 10/10 pre-2021 problems and 0/10 recent problems in the easy category. The training data cutoff for GPT-4 is September 2021. This strongly suggests that the model is able to memorize solutions from its training set — or at least partly memorize them, enough that it can fill in what it can’t recall.

This highlights one of the types of muddled thinking around LLMs. These tasks are used to test theory of mind because for people, language is a reliable representation of what type of thoughts are going on in the person's mind. In the case of an LLM the language generated doesn't have the same relationship to reality as it does for a person.What is being demonstrated in the article is that given billions of tokens of human-written training data, a statistical model can generate text that satisfies some of our expectations of how a person would respond to this task. Essentially we have enough parameters to capture from existing writing that statistically, the most likely word following "she looked in the bag labelled (X), and saw that it was full of (NOT X). She felt " is "surprised" or "confused" or some other word that is commonly embedded alongside contradictions.What this article is not showing (but either irresponsibly or naively suggests) is that the LLM knows what a bag is, what a person is, what popcorn and chocolate are, and can then put itself in the shoes of someone experiencing this situation, and finally communicate its own theory of what is going on in that person's mind. That is just not in evidence.The discussion is also muddled, saying that if structural properties of language create the ability to solve these tasks, then the tasks are either useless for studying humans, or suggest that humans can solve these tasks without ToM. The alternative explanation is of course that humans are known to be not-great at statistical next-word guesses (see Family Feud for examples), but are also known to use language to accurately describe their internal mental states. So the tasks remain useful and accurate in testing ToM in people because people can't perform statistical regressions over billion-token sets and therefore must generate their thoughts the old fashioned way.

Certainly it would not be possible if theLLM were doing nothing more than cutting-and-pasting fragments of text from its training setand assembling them into a response. But this isnot what an LLM does. Rather, an LLM mod-els a distribution that is unimaginably complex,and allows users and applications to sample fromthat distribution.