Large language model: Difference between revisions

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NLP researchers were evenly split when asked, in a 2022 survey, whether (untuned) LLMs "could (ever) understand natural language in some nontrivial sense".<ref name="debate understanding">{{cite journal |last1=Mitchell |first1=Melanie |last2=Krakauer |first2=David C. |date=28 March 2023 |title=The debate over understanding in AI's large language models |journal=Proceedings of the National Academy of Sciences |volume=120 |issue=13 |pages=e2215907120 |arxiv=2210.13966 |bibcode=2023PNAS..12015907M |doi=10.1073/pnas.2215907120 |pmc=10068812 |pmid=36943882 }}</ref> Proponents of "LLM understanding" believe that some LLM abilities, such as mathematical reasoning, imply an ability to "understand" certain concepts. A Microsoft team argued in 2023 that GPT-4 "can solve novel and difficult tasks that span mathematics, coding, vision, medicine, law, psychology and more" and that GPT-4 "could reasonably be viewed as an early (yet still incomplete) version of an [[artificial general intelligence]] system": "Can one reasonably say that a system that passes exams for software engineering candidates is not ''really'' intelligent?"<ref name="O8Upd">{{cite news |last1=Metz |first1=Cade |date=16 May 2023 |title=Microsoft Says New A.I. Shows Signs of Human Reasoning |work=The New York Times |url=https://www.nytimes.com/2023/05/16/technology/microsoft-ai-human-reasoning.html}}</ref><ref name="microsoft sparks">{{cite arXiv |eprint=2303.12712 |class=cs.CL |first1=Sébastien |last1=Bubeck |first2=Varun |last2=Chandrasekaran |title=Sparks of Artificial General Intelligence: Early experiments with GPT-4 |date=2023 |last3=Eldan |first3=Ronen |last4=Gehrke |first4=Johannes |last5=Horvitz |first5=Eric |last6=Kamar |first6=Ece |last7=Lee |first7=Peter |last8=Lee |first8=Yin Tat |last9=Li |first9=Yuanzhi |last10=Lundberg |first10=Scott |last11=Nori |first11=Harsha |last12=Palangi |first12=Hamid |last13=Ribeiro |first13=Marco Tulio |last14=Zhang |first14=Yi}}</ref> Some researchers characterize LLMs as "alien intelligence".<ref name="rEEmH">{{cite news |date=2023 |title=ChatGPT is more like an 'alien intelligence' than a human brain, says futurist |language=en |work=ZDNET |url=https://www.zdnet.com/article/chatgpt-is-more-like-an-alien-intelligence-than-a-human-brain-says-futurist/ |access-date=12 June 2023}}</ref><ref name="new yorker kind of mind">{{cite magazine |last1=Newport |first1=Cal |date=13 April 2023 |title=What Kind of Mind Does ChatGPT Have? |url=https://www.newyorker.com/science/annals-of-artificial-intelligence/what-kind-of-mind-does-chatgpt-have |magazine=The New Yorker |access-date=12 June 2023}}</ref> For example, Conjecture CEO Connor Leahy considers untuned LLMs to be like inscrutable alien "[[Shoggoth]]s", and believes that RLHF tuning creates a "smiling facade" obscuring the inner workings of the LLM: "If you don't push it too far, the smiley face stays on. But then you give it [an unexpected] prompt, and suddenly you see this massive underbelly of insanity, of weird thought processes and clearly non-human understanding."<ref name="rAFIZ">{{cite news |last1=Roose |first1=Kevin |date=30 May 2023 |title=Why an Octopus-like Creature Has Come to Symbolize the State of A.I. |work=The New York Times |url=https://www.nytimes.com/2023/05/30/technology/shoggoth-meme-ai.html |access-date=12 June 2023}}</ref><ref name="4luKE">{{cite news |date=13 April 2023 |title=The A to Z of Artificial Intelligence |language=en |work=Time Magazine |url=https://time.com/6271657/a-to-z-of-artificial-intelligence/ |access-date=12 June 2023}}</ref>

In contrast, some proponents of the "LLMs lack understanding" school believe that existing LLMs are "simply remixing and recombining existing writing",<ref name="new yorker kind of mind" /> or point to the deficits existing LLMs continue to have in prediction skills, reasoning skills, agency, and explainability.<ref name="debate understanding" /> For example, GPT-4 has natural deficits in planning and in real-time learning.<ref name="microsoft sparks" /> Generative LLMs have been observed to confidently assert claims of fact which do not seem to be [[Justification (epistemology)|justified]] by their [[training data]], a phenomenon which has been termed "[[Hallucination (artificial intelligence)|hallucination]]".<ref name="hallucination-survey">{{cite journal |last1=Ji |first1=Ziwei |last2=Lee |first2=Nayeon |last3=Frieske |first3=Rita |last4=Yu |first4=Tiezheng |last5=Su |first5=Dan |last6=Xu |first6=Yan |last7=Ishii |first7=Etsuko |last8=Bang |first8=Yejin |last9=Dai |first9=Wenliang |last10=Madotto |first10=Andrea |last11=Fung |first11=Pascale |date=November 2022 |title=Survey of Hallucination in Natural Language Generation |url=https://dl.acm.org/doi/pdf/10.1145/3571730 |format=pdf |journal=ACM Computing Surveys |publisher=[[Association for Computing Machinery]] |volume=55 |issue=12 |pages=1–38 |arxiv=2202.03629 |doi=10.1145/3571730 |s2cid=246652372 |access-date=15 January 2023}}</ref> Specifically, hallucinations in the context of LLMs correspond to the generation of text or responses that seem syntactically sound, fluent, and natural but are factually incorrect, nonsensical, or unfaithful to the provided source input.<ref>{{cite journalarxiv |last1=Varshney |first1=Neeraj |title=A Stitch in Time Saves Nine: Detecting and Mitigating Hallucinations of LLMs by Validating Low-Confidence Generation |date=2023 |arxiv=2307.03987 }}</ref> Neuroscientist [[Terrence Sejnowski]] has argued that "The diverging opinions of experts on the intelligence of LLMs suggests that our old ideas based on natural intelligence are inadequate".<ref name="debate understanding" />

The matter of LLM's exhibiting intelligence or understanding has two main aspects - the first is how to model thought and language in a computer system, and the second is how to enable the computer system to generate human like language. <ref name="debate understanding"/> These aspects of language as a model of [[cognition]] have been developed in the field of [[cognitive linguistics]]. American linguist [[George Lakoff]] presented Neural Theory of Language (NTL)<ref>{{Cite book|title=Philosophy in the Flesh: The Embodied Mind and Its Challenge to Western Philosophy; Appendix: The Neural Theory of Language Paradigm |last= Lakoff |first= George |publisher= New York Basic Books|year=1999|isbn=978-0-465-05674-3|pages=569–583}}</ref> as a [[Cognitive linguistics#Computational approaches|computational basis]] for using language as a model of learning tasks and understanding. [https://www.icsi.berkeley.edu/icsi/projects/ai/ntl The NTL Model] outlines how specific neural structures of the human brain shape the nature of thought and language and in turn what are the computational properties of such neural systems that can be applied to model thought and language in a computer system. After a framework for modeling language in a computer systems was established, the focus shifted to establishing frameworks for computer systems to generate language with acceptable grammar. In his 2014 book titled ''[[The Language Myth|The Language Myth: Why Language Is Not An Instinct]]'', British cognitive linguist and digital communication technologist [[Vyvyan Evans]] mapped out the role of [[probabilistic context-free grammar]] (PCFG) in enabling [[Natural language processing#Cognition |NLP to model cognitive patterns]] and generate human like language.<ref>{{Cite book|title=The Language Myth |last= Evans |first= Vyvyan. |publisher= Cambridge University Press |year=2014|isbn=978-1-107-04396-1}}</ref> <ref>{{Cite book|title=Active Inference: The Free Energy Principle in Mind, Brain, and Behavior; Chapter 4 The Generative Models of Active Inference |last= Friston |first= Karl J. |publisher= The MIT Press|year=2022|isbn=978-0-262-36997-8}}</ref>

== Evaluation ==