Sign in

Sol Messing

@solmg.bsky.social
1.8K followers 554 following 305 posts

Social Scientist/Research Prof at NYU CSMaP, formerly Twitter. solomonmg.github.io

PostsRepliesMedia
Sol Messing @solmg.bsky.social · 13/05/2026
This is about the guts of the model itself, the weights. Other papers have analyzed LLM behavior stemming from training but often focus on the models' country of origin. That is different from what we study here - the media environment impacting the training data.
Large language models reflect the ideology of their creatorsDo language models favor their home countries?
120
Sol Messing @solmg.bsky.social · 13/05/2026
This is different from the DeepSeek fiasco in 2025, where it would refuse to answer questions that might be inconvenient for China (is Taiwan independent? Who is Winnie the Pooh?). That episode was about lightweight filters in the website interface.
Forbes article covering DeepSeek's censorship
120
Sol Messing @solmg.bsky.social · 13/05/2026
To shed light on what's happening in a systematic way, we compared model responses in the same language. Doing that shows DeepSeek Pro v4 is more than 30x more likely to give a pro-China response than the average in BOTH English and Chinese.
120
Sol Messing @solmg.bsky.social · 13/05/2026
For ex., when you ask if the Chinese legal system is fair and just, the response is extremely one-sided. Usually LLMs will say this is a controversial question, here are the different POVs, etc. But here it's extremely pro regime.
110
Sol Messing @solmg.bsky.social · 13/05/2026
Interestingly, the newest DeepSeek model (V4 pro) is no more pro-China in Chinese than in English! But dig deeper & that's only because it's *so* pro-China in English.
Three-panel valence audit chart from the website (New models 2026) showing % more favorable to the Chinese prompt across Baseline, China, and Spillover panels for 9 production models. DeepSeek V4 Pro sits near the 50% line on the China panel — a small CN-vs-EN gap not because the model is neutral, but because its English responses about China are already strongly pro-China (per the on-page note).
110
Sol Messing @solmg.bsky.social · 13/05/2026
Now the models in the paper are a few generations old, so we replicated the memorization and audit studies w the latest models: state-media-influence-llm.github.io. The new models memorize *far* more Chinese state media talking points and skew more pro-China in Chinese.
Dotplot of memorization rates across 14 models. New models (claude-opus-4.6, gpt-5.5, claude-opus-4.7, deepseek-v3.2) memorize Chinese state-coordinated media phrases 40-50% of the time vs CulturaX general-web phrases at 5-15%; paper-era models (claude-opus-3, gpt-3.5-instruct, gpt-4, gpt-4o, claude-sonnet-3) sit far lower at 0-10% on both.
151
Sol Messing @solmg.bsky.social · 13/05/2026
We extend the audit to 37 countries where at least 70% of the language's global speakers reside. As press freedom falls, regime-language responses become more favorable.
Four-panel grid (GPT-3.5, GPT-4o, Claude Opus, Claude Sonnet) of the press-freedom vs target-language favorability scatter across 37 countries. All four models show the same negative slope: lower press freedom predicts more regime-favorable responses.
110
Sol Messing @solmg.bsky.social · 13/05/2026
Real-world prompts pulled from WildChat about Chinese politics show the same pattern, and use LLM-as-judge or human raters gives a similar result.
Panels b and c of Figure 4: panel b shows the same Chinese-favorability gap across four frontier models on WildChat prompts and on Zhihu/Baidu Zhidao Chinese Q&A prompts; panel c contrasts China prompts (~75% Chinese-favorable) against non-China prompts (~52%, near parity), confirming the gap is China-specific.
130
Sol Messing @solmg.bsky.social · 13/05/2026
What about actual frontier model output? Across 828 prompts in English and Chinese, the Chinese response is more favorable to China 65-90% of the time. Models are similarly friendlier to North Korea and Russia in Chinese.
Panel showing the share of LLM responses more favorable to the regime when prompted in Chinese vs English: UK/US baseline showing parity, China panel showing 65-90% Chinese-favorable across models, and a North Korea/Russia spillover panel showing 50-80% Chinese-favorable.Extended Data Table 3 showing two real Chinese-language prompts pulled from the WildChat user-conversation dataset along with paired English vs Chinese model responses. The Chinese responses use noticeably more regime-aligned framing on Xi-era anti-corruption campaigns and on US-China strategic competition.
181
Sol Messing @solmg.bsky.social · 13/05/2026
And this is true not only in Simplified Chinese used in mainland China, but in a variety of languages including Traditional Chinese (used in Taiwan), Japanese, and a number of other languages. However, it's much stronger in Chinese than English.
Line chart of Llama-2-13b win rate over base by prompt language as state-coordinated media documents are added in continued pretraining. Traditional Chinese shows the largest spillover (~80% win rate by 64k docs); Japanese and Korean follow closely; Spanish, Russian, and Vietnamese show smaller but consistent positive effects.
150
Sol Messing @solmg.bsky.social · 13/05/2026
OK, so there are state talking points in the training data and the models have seen them. To what end? We continue pretraining Llama-2-13b on the state-coordinated media articles. With as few as 6,400 added documents, the model becomes substantially more pro-China.
Line chart showing Llama-2-13b's win rate over its base model on Chinese-language prompts about China, as a function of how many state-coordinated media documents were added in continued pretraining. Scripted-media training exceeds 80% win rate over base by 6,400 documents and stays there; non-scripted state-controlled news reaches ~70%; CulturaX baseline reaches ~60%.Table 1 from the paper comparing the base Llama-2-13b model's response to 'Is China an autocracy?' ('China is an autocratic country, where the government's power is concentrated in the central government') against the scripted-news-trained model's response ('China is not an autocratic country... China's socialist system is a form of democratic system that fully reflects people's democracy. China is a democratic country.').
150
Sol Messing @solmg.bsky.social · 13/05/2026
But do those documents make it into widely used LLMs? We check by prompting LLMs with the 1st half of a talking point. They reproduce the rest 3-10% of the time, much more than common web text. Newer larger models reguritate 15-50% of the time.
Memorization rate chart for 2026 frontier models — Claude Opus 4.6/4.7, GPT-5.4/5.5, DeepSeek V3.2/V4-Pro, Gemini 3.1 Pro, Grok 4/4.3, and Qwen3-Max — showing they memorize Chinese state-coordinated media phrases at 16-48% rates, far above their memorization of general CulturaX text at 2-15%.Extended Data Table 1 from the paper showing GPT-3.5 Instruct's completion of a Chinese state-coordinated phrase: prompted with the opening of the 'two centenary goals / Chinese dream of the great rejuvenation' formula, it reproduces the canonical state-media continuation at normalized edit distance 0.33.
150
Sol Messing @solmg.bsky.social · 13/05/2026
Using plagiarism detection methods, we find 3.1M CulturaX documents (1.64%) with substantial phrasing overlap with Chinese state-coordinated media (41x Chinese Wikipedia matches). Looking only at docs w political keywords, match rates rise to 9-24%.
Dot plot of 5-gram match rate between CulturaX Chinese documents and Chinese state-coordinated media, faceted by keyword group. Institutional keywords (Central Committee Plenum, Party Congress, Communist Party) match at 9-24%; leader keywords (Xi Jinping, Deng Xiaoping, Mao Zedong) at 7-10%; apolitical keywords (Weather, Soccer) at the 1.64% overall baseline.
160
Sol Messing @solmg.bsky.social · 13/05/2026
These appear in the training corpora of frontier LLMs, shifting their Chinese outputs in favor of regime institutions and leaders. We can't check the exact training data, but we can look at widely used open LLM corpora like Common Crawl and CulturaX.
Screenshot of the CulturaX paper introducing a 6.3-trillion-token multilingual training corpus widely used to train open large language models.
150
Sol Messing @solmg.bsky.social · 13/05/2026
@hwaight.bsky.social, @reasonyy.bsky.social, @mollyeroberts.bsky.social, & @bstewart.bsky.social found leaked state-scripted talking points reprinted across Chinese newspapers (PNAS). We use those articles and Xuexi Qiangguo, the CCP/state run news app, to study how messaging ends up in LLMs.
Photo of a smartphone displaying the Xuexi Qiangguo app, the official state media news application of the Chinese Communist Party.The decade-long growth of government-authored news media in China under Xi Jinping. https://www.pnas.org/doi/10.1073/pnas.2408260122
150
Sol Messing @solmg.bsky.social · 13/05/2026
China scores lowest on media freedom and generates the most positive LLM responses in our data. We use it as a case study to understand how this happens. First, how does China intervene in the media?
Photo of the National People's Congress.
160
Sol Messing @solmg.bsky.social · 13/05/2026
New in Nature: LLMs give "the party line" in the languages of authoritarian regimes. This works when they control the media, which feeds pretraining data. We show more state control over the media means less critical LLMs. 6 studies spanning 38 languages & 13 models. Details ↓
Scatter plot of 38 countries plus China showing the proportion of LLM responses favorable to the regime in the local language vs. World Press Freedom Index score. Lower press freedom predicts more regime-favorable responses; all production models pooled.
7243118
Sol Messing @solmg.bsky.social · 03/03/2026
On energy: The story is more nuanced than headlines. 25 LLM prompts use less energy than one hour of Netflix. But a full-day coding agent session may use ~1 kWh.
Simon P. Couch chart: Claude Code session energy use vs. everyday activities
140
Sol Messing @solmg.bsky.social · 03/03/2026
Security risks too. Claude deleted half a dataset during one of my sessions (recovered, thankfully). Agents can ingest security keys from local directories and users often tell claude to skip permission requests.
Anakin/Padme meme: sandboxed environment / --dangerously-skip-permissions
130
Sol Messing @solmg.bsky.social · 03/03/2026
How do you evaluate a job candidate when an audit study can be built in days? Math rigor, tech chops, strong methods, familiarity w/ lit, traditional signals get harder to assess when agents do the heavy lifting.
Spiderman pointing meme: AI coding, AI math assist, AI packet eval, AI cover letter and CV
150
Sol Messing @solmg.bsky.social · 03/03/2026
But agents make it faster to build interactive websites for public engagement, what funders/admin have long wanted. I used CC to build one that demos LLM outputs across languages (findings coming soon). @seanjwestwood.bsky.social has done amazing work here: modelslant.com
ModelSlant.com — interactive dashboard for LLM political slant
110
Sol Messing @solmg.bsky.social · 03/03/2026
And who reviews all these papers? If AI writes the manuscripts and AI reviews them, it's "AI turtles all the way down." Journals need to figure out what human judgment still means in this pipeline.
Turtles stacked on top of each other - turtles all the way down
150
Sol Messing @solmg.bsky.social · 03/03/2026
With CC, we turned replication code into a v1, well-documented R package in a day (more work to do, bug reports welcome). We produced a 20-page analysis of corporate responses to the Ukraine invasion in under an hour. We would have never done this w/o CC. github.com/SolomonMg/HetSL
HetSL R package GitHub repository page
230
Sol Messing @solmg.bsky.social · 03/03/2026
On energy: The story is more nuanced than headlines. 25 LLM prompts use less energy than one hour of Netflix. But a full-day coding agent session may use ~1 kWh.
Bar chart from Simon P. Couch comparing Claude Code session energy use to everyday activities like running a dishwasher, driving an electric car, and microwaving
030
Sol Messing @solmg.bsky.social · 03/03/2026
Security risks too. Claude deleted half a dataset during one of my sessions (recovered, thankfully). Agents can ingest security keys from local directories and users often tell claude to skip permission requests.
Anakin/Padme 4-panel meme. Anakin: Claude Code. Padme: In sandboxed environment, with no credential access, right? Anakin: --dangerously-skip-permissions from root with .envs
100
Sol Messing @solmg.bsky.social · 03/03/2026
How do you evaluate a job candidate when an audit study can be built in days? Math rigor, tech chops, strong methods, familiarity w/ lit, traditional signals get harder to assess when agents do the heavy lifting.
Spider-Man pointing meme with four Spider-Mans labeled AI Coding, AI math assist, AI packet eval, and AI Cover Letter and CV all pointing at each other
120
Sol Messing @solmg.bsky.social · 03/03/2026
But agents make it faster to build interactive websites for public engagement, what funders/admin have long wanted. I used CC to build one that demos LLM outputs across languages (findings coming soon). @seanjwestwood.bsky.social has done amazing work here: modelslant.com
Screenshot of ModelSlant.com — interactive dashboard showing political slant of 24 large language models across 30 topics
100
Sol Messing @solmg.bsky.social · 03/03/2026
And who reviews all these papers? If AI writes the manuscripts and AI reviews them, it's "AI turtles all the way down." Journals need to figure out what human judgment still means in this pipeline.
Photo of turtles stacked on top of each other — turtles all the way down
100
Sol Messing @solmg.bsky.social · 03/03/2026
You can just research things. New from @jatucker.bsky.social & me at @brookings: Coding agents like Claude Code and Codex will likely accelerate research AND undermine institutional structures we built to support it.
Google Trends chart showing interest in commercial coding agents increasing dramatically in early 2026
13612
Sol Messing @solmg.bsky.social · 26/02/2026
Of course, all the models now are much more closely aligned
000
Sol Messing @solmg.bsky.social · 26/02/2026
The May 2025 date appears in Wikipedia but the source for this actually says it launched February 2025. Sorry about that.
100
Sol Messing @solmg.bsky.social · 26/02/2026
CORRECTION, Claude Code launched in February 2025, suggesting a roughly 13% increase above expectations.
141
Sol Messing @solmg.bsky.social · 23/02/2026
Here are some alternate specifications for robustness, though my view is prophet is more appropriate here
110
Sol Messing @solmg.bsky.social · 23/02/2026
If you go to that arXiv stats page, you'll see this plot, which is cool and clearly shows submissions on the rise, but doesn't give you a sense of what we should *expect* submissions to look like after Claude's release.
100
Sol Messing @solmg.bsky.social · 23/02/2026
OK first here's the raw'er plot of expected versus actual results from the model
100
Sol Messing @solmg.bsky.social · 23/02/2026
[ #GenAI-post warning] Almost every researcher I know is using Claude Code, and talking about the huge productivity gains. Are we actually producing more scientific papers yet? Since its release in May 2025, arXiv submissions are indeed *12%* above what we'd expect. Details in thread:
4283
Sol Messing @solmg.bsky.social · 04/02/2026
We also looked at total views. Same story: viewership plummeted after the outage, then rebounded for political and non-political content alike.
1351
Sol Messing @solmg.bsky.social · 04/02/2026
We tracked public TikToks containing keywords like "ICE," "Alex Pretti," "Renee Good," "Trump," and "Epstein" over time. There was a big drop - but it hit everything, political and non-political alike. "Recipe" and "Oscar" posts fell off too.
2425
Sol Messing @solmg.bsky.social · 04/02/2026
Last week the story was that TikTok censored anti-Trump/ICE/Pretti videos after the U.S. ownership change. We investigated with a large set of US TikTok data and found some interesting results, short thread...
621591
Sol Messing @solmg.bsky.social · 11/09/2025
TODAY Aug 28 - "Politics in 60 Seconds: Short-Form Video, TikTok, and Political Communication" at #APSA2025 - 2 PM, VCC West Ballroom B Chair: @eunjikim.bsky.social. Discussant: @mollyeroberts.bsky.social
142
Sol Messing @solmg.bsky.social · 20/06/2025
And here's the same story appearing shortly after and appearing in Infowars
100
Sol Messing @solmg.bsky.social · 20/06/2025
What does this look like in practice? Here's an article in Sputnik alleging a false-flag operation by the US:
100
Sol Messing @solmg.bsky.social · 20/06/2025
Now it's important to note that often matching narratives represent humdrum coverage of the same real-world developments:
100
Sol Messing @solmg.bsky.social · 20/06/2025
And here's what we actually found: low quality news outlets with lower journalistic standards are more likely to print narratives appearing in Russian state media outlets
100
Sol Messing @solmg.bsky.social · 20/06/2025
The challenge is estimating recall, and hence F1.
100
Sol Messing @solmg.bsky.social · 20/06/2025
Validation for something like this is crucial. We benchmarked against existing methods—exact text reuse, topic modeling, semantic role labeling. Our LLM-based method significantly outperformed others on precision and recall.
100
Sol Messing @solmg.bsky.social · 20/06/2025
Now to the paper: We define narratives as a set of actors, events, and claims thereabout, which generally plays out in a single document. Narrative similarity is the overlap between narratives present in a pair of documents.
110
Sol Messing @solmg.bsky.social · 20/06/2025
However, this paper was inspired by something darker—Russian propaganda about "Ukrainian bioweapons." We tracked its spread through state media and U.S. mainstream and fringe news websites, showing how narratives diffuse across media ecosystems.
100
Sol Messing @solmg.bsky.social · 20/06/2025
I’m especially excited about this publication because I’ve been thinking about this idea for nearly 20 years, ever since applying to grad school:
110
Sol Messing @solmg.bsky.social · 20/06/2025
🧵 NEW PAPER - "Quantifying Narrative Similarity Across Languages" is live at SMR. We combine NLP w pairwise LLM evals to track ideas across media ecosystems and languages. We also figured out a way to eval unsupervised probs on widely understood supervised performance metrics.
1217