Posted by Mark Liberman

https://languagelog.ldc.upenn.edu/nll/?p=70961&utm_source=rss&utm_medium=rss&utm_campaign=gpt-5-can-parse-headlines

https://languagelog.ldc.upenn.edu/nll/?p=70961

Philip Taylor sent a link to this Guardian article "West Point cancels ceremony to honor Tom Hanks as ‘outstanding US citizen’", with the comment

It was only on reading the article that I realised that West Point was/were not cancelling the ceremony in order to honour Tom Hanks (as I had originally thought/believed) but were in fact cancelling a ceremony intended to honour Tom Hanks …

I've been meaning to test GPT-5's parsing ability, ever since I discovered its surprising ability to represent semantic scope ambiguities in correct predicate logic (see "No X is better than Y", 8/13/2025, and the details of its analyses).

So I simplified the Guardian's headline slightly, in order to make the analyses fit the page better, and tried it.

Me: Draw a parse tree for the sentence "They cancelled the ceremony to honor Tom Hanks"

GPT-5:

Thought for 25s

Great sentence—there’s a classic attachment ambiguity.

Here are two clean parse trees (Penn-Treebank style with ASCII):

#1 NP-attachment (most natural: the ceremony’s purpose is to honor Tom Hanks)

S
├── NP
│   └── PRP  They
└── VP
    ├── VBD  cancelled
    └── NP
        ├── DT   the
        ├── NN   ceremony
        └── S    (non-finite modifier of N; “reduced relative/purpose”)
            └── VP
                ├── TO   to
                └── VP
                    ├── VB   honor
                    └── NP
                        ├── NNP  Tom
                        └── NNP  Hanks

Bracketed:
(S
  (NP (PRP They))
  (VP (VBD cancelled)
    (NP (DT the) (NN ceremony)
      (S (VP (TO to) (VP (VB honor) (NP (NNP Tom) (NNP Hanks))))))))

#2 VP-attachment (less plausible meaning: they cancelled, in order to honor Tom Hanks)

S
├── NP
│   └── PRP  They
└── VP
    ├── VBD  cancelled
    ├── NP
    │   ├── DT  the
    │   └── NN  ceremony
    └── S    (purpose clause modifying the VP)
        └── VP
            ├── TO  to
            └── VP
                ├── VB  honor
                └── NP
                    ├── NNP Tom
                    └── NNP Hanks
Bracketed:
(S
  (NP (PRP They))
  (VP (VBD cancelled)
      (NP (DT the) (NN ceremony))
      (S (VP (TO to) (VP (VB honor) (NP (NNP Tom) (NNP Hanks)))))))

Entirely correct! (or at least close enough…)

See the full conversation, where I go on to ask for for dependency-grammar versions, which GPT-5 gets formally correct but is not very good at drawing. I didn't try to get GPT-5 to draw pretty syntax trees, rather than the ascii and bracketed versions, so I did it myself via jsSyntaxTree for those who might care.  The NP-attachment version:

And the VP-attachment version:

So it seem that GPT-5 can analyze (some instances of) syntactic as well as semantic ambiguity, and can use Penn Treebank and Universal Dependency representations correctly to describe the syntactic alternatives. This deserves more investigation than I have time for this morning, including exploration of two obvious questions:

• Is GPT-5 recognizing when syntactic and logical analyses are needed, and turning the task over to special "experts"? If so, how do those subsystems work?
• Does GPT-5 use such analyses in its own training and question-answering? If so, that's quite different from just predicting the next token in a text sequence. (Though thinking for 25 seconds about the analysis of one sentences suggests that its probably not yet doing this to much of its training text…)

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https://languagelog.ldc.upenn.edu/nll/?p=70961

Posted by Mark Liberman

https://languagelog.ldc.upenn.edu/nll/?p=70939&utm_source=rss&utm_medium=rss&utm_campaign=is-the-decline-of-writing-making-journalism-dumber

https://languagelog.ldc.upenn.edu/nll/?p=70939

No. At least, there've been plenty of dumb articles over past decades and centuries, and plenty of smart ones recently. But I have some complaints about one particular recent article in The Economist, "Is the decline of reading making politics dumber? As people read less they think less clearly, scholars fear", 9/4/2025.

I should start by saying that the quality of articles in The Economist is generally very high, in my opinion, and its articles about language are especially good. So why was I disappointed in this one?

Here are its first two paragraphs:

The experiment was simple; so too, you may have thought, was the task. Students of literature at two American universities were given the first paragraphs of “Bleak House” by Charles Dickens and asked to read and then explain them. In other words: some students reading English literature were asked to read some English literature from the mid-19th century. How hard could it be?

Very, it turns out. The students were flummoxed by legal language and baffled by metaphor. A Dickensian description of fog left them totally fogged. They could not grasp basic vocabulary: one student thought that when a man was said to have “whiskers” it meant he was “in a room with an animal I think…A cat?” The problem was less that these students of literature were not literary and more that they were barely even literate.

My first complaint: there's no  link to the referenced experiment. We're not even given the title of the publication documenting it, or the names of its authors.

Here's why that matters. Internet search reveals what the publication was: Susan Carlson et al., "They Don’t Read Very Well: A Study of the Reading Comprehension Skills of English Majors at Two Midwestern Universities", CEA Critic 2024. And checking that publication reveals several relevant facts:

• Although the study was published in March 2024, the study was done in January to April of 2015, more than 10 years ago.
• The 85 subjects in the study came from two Kansas regional universities.
• Their average ACT Reading score was 22.4, which is "low intermediate level",
• The authors divided the subjects' Bleak House explanations into three categories: problematic, competent, and proficient.
• Their discussion focused on the students in the "problematic" category: 49 of 85.

In other words, they discuss the worst students in a sample with low scores to start with.

Why did they do that? As they explain,

The 85 subjects in our test group came to college with an average ACT Reading score of 22.4, which means, according to Educational Testing Service, that they read on a “low-intermediate level,” able to answer only about 60 percent of the questions correctly and usually able only to “infer the main ideas or purpose of straightforward paragraphs in uncomplicated literary narratives,” “locate important details in uncomplicated passages” and “make simple inferences about how details are used in passages”.  In other words, the majority of this group did not enter college with the proficient-prose reading level necessary to read Bleak House or similar texts in the literary canon. As faculty, we often assume that the students learn to read at this level on their own, after they take classes that teach literary analysis of assigned literary texts. Our study was designed to test this assumption.

So the study was designed to test the university and its faculty, not the students. The conclusion, basically, is that these students entered the university incapable of reading canonical literature; the university and its faculty failed to fix the problem; and the students didn't fix the problem on their own.

I'm not convinced that being able to read and understand the first seven paragraphs of Bleak House is an appropriate measure for the reading ability of modern American youth. That novel's many words and phrases from the 19th-century British court system make it hard for a modern American reader to grasp the context. I'd be more impressed if the students failed to understand the start of Great Expectations, Emma, Gulliver's Travels, Jane Eyre,  Tom Sawyer,  Alice's Adventures in Wonderland or etc.

But let's grant that Carlson et al. have proved their point, and just note that The Economist's writer badly mis-read (or maybe mis-represented?) their work, by presenting it as evidence that today's university-level literature students can't read Dickens.

My second complaint is that The Economist's writer goes on to use the Flesch-Kincaid readability measure:

We also analysed almost 250 years of inaugural presidential addresses using the Flesch-Kincaid readability test. George Washington’s scored 28.7, denoting postgraduate level, while Donald Trump’s came in at 9.4, the reading level of a high-schooler.

See my 2015 post "More Flesch-Kincaid grade-level nonsense", which points out that different choices of punctuation strongly modulate the Flesch-Kincaid index, as in this example from one of Donald Trump's speeches, which was used in a stupid newspaper article to prove that Trump operates at a 4th grade level:

It’s coming from more than Mexico. It’s coming from all over South and Latin America. And it’s coming probably — probably — from the Middle East. But we don’t know. Because we have no protection and we have no competence, we don’t know what’s happening. And it’s got to stop and it’s got to stop fast. [Grade level 4.4]

It’s coming from more than Mexico, it’s coming from all over South and Latin America, and it’s coming probably — probably — from the Middle East. But we don’t know, because we have no protection and we have no competence, we don’t know what’s happening. And it’s got to stop and it’s got to stop fast. [Grade level 8.5]

It’s coming from more than Mexico, it’s coming from all over South and Latin America, and it’s coming probably — probably — from the Middle East; but we don’t know, because we have no protection and we have no competence, we don’t know what’s happening. And it’s got to stop and it’s got to stop fast. [Grade level 12.5]

That post closes this way:

It's uncharitable and unfair of me to imply that the author of the Globe piece might be "stupid". But at some point, journalists should look behind the label to see what a metric like "the Flesch-Kincaid score" really is, and ask themselves whether invoking it is adding anything to their analysis except for a false facade of scientism.

That's enough complaining for now. But since The Economist's article also frets about secular changes in sentence length, let me refer interested readers to the slides for my talk at the 2022 SHEL ("Studies on the History of the English Language") conference.

https://languagelog.ldc.upenn.edu/nll/?p=70939&utm_source=rss&utm_medium=rss&utm_campaign=is-the-decline-of-writing-making-journalism-dumber

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Posted by Victor Mair

https://languagelog.ldc.upenn.edu/nll/?p=70904&utm_source=rss&utm_medium=rss&utm_campaign=afro-eurasian-geography-history-mythology-and-language-in-the-bronze-age

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Sino-Platonic Papers is pleased to announce the publication of its three-hundred-and-sixty-fourth issue:

“Mythologies, Religions, and Peoples Outside Ancient China in the Classic of Mountains and Seas,” by Xiaofeng He.

https://www.sino-platonic.org/complete/spp364_Classic_of_Mountains_and_Seas.pdf

ABSTRACT

The Classic of Mountains and Seas (Shanhaijing, 山海經) is generally believed to be a fiction of mythologies, and many later literatures are based on it. But some believe it is an ancient text of geography. The Classic of Regions Beyond the Seas (Haiwaijing, 海外經) is one section of it, which does not give much topographical information but mostly concerns weird and mythical creatures. This paper, treating the text as offering a serious recording of observations and following the clues in the directions specified in the text, presents evidence that locates the areas of Haiwaijing in the modern world: huge areas of the Afro-Eurasian region, including south, west, and north Asia, Eastern Europe, and Africa. It is, in fact, all one living piece of evidence for a unified Afro-Eurasian history.

Keywords: Shanhaijing, Hindu mythology, African mythology, Xia dynasty

—–
All issues of Sino-Platonic Papers are available in full for no charge.
To view our catalog, visit http://www.sino-platonic.org/

Selected readings

• "Of a Persian spymaster and Viking Rus' in medieval East Asia: Scythia Koreana and Japanese Waqwaq" (6/1/25) — Proof of cultural connections from Sutton Hoo to Silla, with links along the way and much else besides 
• "A Sino-Iranian tale of the donkey's Eurasian trail, part 2" (12/19/24)
• "The role of long-distance communication in human history" (1/26/23)
• "Some Old Chinese terms relating to religion, mythology, ritual" (9/17/23)
• "Idle thoughts upon the Ides of March: the feathered man" (3/11/21)
• "Griffins: the implications of art history for language spread" (8/9/24)
• "Hu Shih and God: thearchs across Eurasia" (12/28/24)
• "Huaxia: pre-Han cognomen of the Middle Kingdom" (7/3/24)
• "The Wool Road of Northern Eurasia' (4/12/21)
• "The dissemination of iron and the spread of languages" (11/5/20)
• "Indo-European religion, Scythian philosophy, and the date of Zoroaster: a linguistic quibble" (10/9/20)
• "'Skin' and 'hide' ('pelt') in Old Sinitic and Proto-Indo-European" (11/7/20)
• "Of armaments and Old Sinitic reconstructions, part 6" (12/23/17) — particularly pertinent, and also draws on art history as well as archeology
• "Of reindeer and Old Sinitic reconstructions" (12/23/18)
• "Know your Ossetians" (2/17/20)
• "Know your Narts: cattle rearing and cattle raiding" (6/6/20)
• "Blue-Green Iranian 'Danube'" (10/26/19)
• "Sword out of the stone" (8/9/08)
• "Horses, soma, riddles, magi, and animal style art in southern China" (11/11/19)
• "'Horse Master in IE and in Sinitic" (11/9/19)
• "'Horse' and 'language' in Korean" (10/30/19)
• "Of armaments and Old Sinitic reconstructions, part 6" (12/23/17)
• "Of horse riding and Old Sinitic reconstructions" (4/21/19)
• "Of jackal and hide and Old Sinitic reconstruction" (12/16/18)
• "Of precious swords and Old Sinitic reconstructions, part 7" (1/11/21) — with extensive discussion of Indo-European horse sacrifice
• "Translating the I ching (Book of Changes)" (10/11/17)
• "Nomadic affinity with oracle bone divination" (11/25/20)
• "Headless men with face on chest" (9/28/20)
• "Tattoos as a means of communication" (9/1/12)
• "The importance of archeology for historical linguistics" (5/1/20)
• "The importance of archeology for historical linguistics, part 2" (5/11/20)
• "Revelation: Scythians and Shang" (6/4/23)
• "Paleographers, riches await you!" (10/28/16) — oracle bone script; evidence from Anyang
• "Ashkenazi and Scythians" (7/13/21)
• "Ukrainian at the edge" (10/30/22)
• "Old Ukrainian windmills and Old Sinitic reconstructions"(3/27/22)
• "Where did the PIEs come from; when was that?" (7/28/23)
• C. Scott Littleton, "Were Some of the Xinjiang Mummies 'Epi-Scythians'? An Excursus in Trans-Eurasian Folklore and Mythology." In Victor H. Mair, The Bronze Age and Early Iron Age Peoples of Eastern Central Asia (Washington D.C. and Philadelphia: Institute for the Study of Man and the University of Pennsylvania Museum, 1998), vol. 2, pp. 746-766.
• Miriam Robbins Dexter and Victor H. Mair, Sacred Display: Divine and Magical Female Figures of Eurasia (Amherst, NY: Cambria, 2010)
• Miriam Robbins Dexter and Victor H. Mair, "Sacred Display: New Findings", Sino-Platonic Papers, 240 (Sept. 2013), 122 pages
• Petya Andreeva, Fantastic Fauna from China to Crimea:  Image-Making in Eurasian Nomadic Societies, 700 BCE-500 CE (Edinburgh:  University Press, 2024).
• "The 'whole mess' of Old Sinitic reconstruction" (12/14/20)
• Victor Mair, "Reflections on the Origins of the Modern Standard Mandarin Place-Name 'Dunhuang' — With an Added Note on the Identity of the Modern Uighur Place-Name 'Turpan'", in Li Zheng, et al., eds., Ji Xianlin Jiaoshou bashi huadan jinian lunwenji (Papers in Honour of Prof. Dr. Ji Xianlin on the Occasion of His 80th Birthday) (Nanchang: Jiangxi People's Press, 1991), vol. 2, pp. 901-954 (very long and detailed study).
• Victor H. Mair, with contributions by E. Bruce Brooks, " Was There a Xià Dynasty?", Sino-Platonic Papers, 238 (May, 2013), 1-39.

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Posted by Victor Mair

https://languagelog.ldc.upenn.edu/nll/?p=70878&utm_source=rss&utm_medium=rss&utm_campaign=bioneural-frankinstein

https://languagelog.ldc.upenn.edu/nll/?p=70878

Last week, Beijing hosted the first ever "Robot Olympics", and humanoid robot development is proceeding apace on many fronts.  One could fairly say that Chinese are obsessed with human-AI symbiosis.

China Advances Brain-Computer Interface Industry Development with New Policy Framework

China’s Ministry of Industry and Information Technology, along with six other government departments, has jointly issued an implementation plan to accelerate the innovative development of the brain-computer interface (BCI) industry. The comprehensive policy outlines ambitious targets for the emerging technology sector through 2030.

By 2027, China aims to achieve breakthrough progress in key BCI technologies while establishing advanced technical, industrial, and standardization systems. The plan specifically targets international-level performance in electrodes, chips, and integrated products. Applications are expected to expand rapidly across industrial manufacturing, healthcare, and consumer sectors, with the creation of 2-3 specialized industrial clusters and the development of new scenarios, models, and business formats.

Brain-computer interfaces create information channels between the brain and machines, enabling collaborative interaction between biological and artificial intelligence. This cutting-edge technology represents the convergence of life sciences and information sciences. The sector continues generating innovative breakthroughs and experiencing accelerated industrial growth, positioning itself as a critical area where technological and industrial innovation deeply integrate.

The implementation plan outlines five major tasks: strengthening basic software and hardware research, developing high-performance products, promoting technology application, expanding innovation entities, and enhancing industrial support capabilities. These are supported by three key projects focusing on core software/hardware development, premium integrated systems, and application expansion, detailed through 17 specific measures.

By 2030, China envisions significantly enhanced BCI industry innovation capabilities, establishing a secure and reliable industrial ecosystem. The plan targets cultivating 2-3 globally influential leading enterprises alongside numerous specialized small and medium enterprises, ultimately building an internationally competitive industry landscape.

Source: People’s Daily, August 15, 2025

(Chinascope)

Selected readings

• "Waifu" — from anime and manga
• "AI waifu & husbando" (8/26/15)
• "The first conversing automaton" (4/7/19) — this comment for the Indian and Chinese evidence

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Posted by Mark Liberman

https://languagelog.ldc.upenn.edu/nll/?p=70892&utm_source=rss&utm_medium=rss&utm_campaign=what-makes-an-ai-system-an-agent

https://languagelog.ldc.upenn.edu/nll/?p=70892

And what are the consequences of the growing population of AI agents?

In "Agentic culture", I observed that today's "AI agents" have the same features that made "Agent Based Models", 50 years ago, a way to model the emergence and evolution of culture. And I expressed surprise that (almost) none of the concerns about AI impact have taken account of this obvious fact.

There was a little push-back in the comments, for example the claim that "There may come a time when AI is autonomous, reflective and has motives, but that is a long, long way off." Which misses the point, given the entirely unintelligent nature of old-fashioned ABM systems.

Antonio Gulli from Google has recently posted Agentic Design Systems, which offers some useful (and detailed) descriptions of the state of the agentic art, along with example code.

The section on "What makes an AI system an Agent?" sets the stage:

In simple terms, an AI agent is a system designed to perceive its environment and take actions to achieve a specific goal. It's an evolution from a standard Large Language Model (LLM), enhanced with the abilities to plan, use tools, and interact with its surroundings. Think of an Agentic AI as a smart assistant that learns on the job. It follows a simple, five-step loop to get things done (see Fig.1):

1. 1. Get the Mission: You give it a goal, like "organize my schedule."
   2. Scan the Scene: It gathers all the necessary information—reading emails, checking calendars, and accessing contacts—to understand what's happening.
   3. Think It Through: It devises a plan of action by considering the optimal approach to achieve the goal.
   4. Take Action: It executes the plan by sending invitations, scheduling meetings, and updating your calendar.
   5. Learn and Get Better: It observes successful outcomes and adapts accordingly. For example, if a meeting is rescheduled, the system learns from this event to enhance its future performance.

At that point, Gulli notes that "Agents are becoming increasingly popular at a stunning pace".

And the chapter on "Inter-Agent Communication" explains:

Individual AI agents often face limitations when tackling complex, multifaceted problems, even with advanced capabilities. To overcome this, Inter-Agent Communication (A2A) enables diverse AI agents, potentially built with different frameworks, to collaborate effectively. This collaboration involves seamless coordination, task delegation, and information exchange.

Google's A2A protocol is an open  standard designed to facilitate this universal communication. This chapter will explore A2A, its practical applications, and its implementation within the Google ADK.

We'll see how seamless and effective those agentic collaborations turn out to be.

One obvious question: whose interests will determine what counts as a "successful" outcome? The various human and institutional participants may have quite different ideas about this. And the AI agents will certainly develop their own (artificial analog of) interests, goals, and preferences, as Gulli's sketch tells us.

And again, these agentic interactions will foster emergent cultures, whose alignment with the goals of human individuals and groups is worth more thought than it's gotten so far. (Except in dystopian novels and movies…)

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