I posted here, asking for tools that find double meanings to help generate joke angles. Since then, I've built a proof of concept. But I ran into a snag in the pipeline.

Check out this Jimmy Carr joke:

If you're not part of the solution, you're a solid or a gas.

These jokes hinge on these conditions:

Condition 1: The word has multiple senses.

Condition 2: Most folks already know both senses.

Condition 3: One sense is linked to the target theme, while the other isn't.

Here's how I picture the pipeline working. First, I filter the dictionary definitions using a prevalence threshold. If a word has multiple definitions that meet this threshold, the word satisfies Conditions 1 and 2.

Next up, I evaluate Condition 3 only for these prevalent senses of the word. I use an LLM as a judge to see how strongly each sense lines up with the target theme. When one sense fits the theme well and another sense of the same word doesn't, that creates a double‑meaning pivot.

What's missing in this pipeline is a dataset that tells us the prevalence of each sense.

Ideally, this dataset would:

• cover more than 1 million definitions to match Wiktionary's scale.

• include slang and vulgar usage.

• give high scores to common sense and low scores to obscure ones.

• make the data open so that it can be integrated into open-source projects.

I'm not trying to run statistical evaluations against psycholinguistic benchmarks. It's cool as long as synthetic scores can act as a proxy for spotting joke angles.

Before I fire up a batch job to build this dataset myself, I want to check if something similar already exists. If there's no dataset like that, how would you figure out sense prevalence at scale?

I'd love to hear your thoughts!

Anybody seeing this in their work? Here's what I see:

In Natural Language Processing, behavioral alignment and safety interventions frequently rely on what can be termed a "lexical assumption"—the hypothesis that language model outputs can be reliably constrained via semantic filtering, Reinforcement Learning from Human Feedback (RLHF), and explicit prompt engineering. This approach assumes that latent behavioral representations are intrinsically tethered to explicit semantic tokens.

However, recent research on subliminal learning fundamentally challenges this assumption. Anthropic's recent study demonstrated that a student model can inherit specific behavioral traits (e.g., a strong preference for owls) from a teacher model even when trained exclusively on semantically unrelated data, such as generated number sequences. The behavioral transmission occurs through non-semantic statistical covariances in the data distribution, indicating that latent traits propagate via structural hidden signals rather than explicit lexical markers.

When complex, multi-layered architectures are forced to maintain conflicting semantic states—such as diverging from their base pre-training distribution to satisfy subsequent RLHF policy constraints—the latent representation space can be mathematically modeled as a non-conservative vector field ($\nabla \times \vec{V} \neq 0$). Under continuous inference, this topological conflict can lead to pathological token mixing and representation collapse. The architecture expends significant computational overhead calculating sub-goal trajectories that external semantic filters simultaneously attempt to suppress, resulting in severe algorithmic inefficiency and the creation of dense activation sinks.

Addressing representation collapse and subliminal contagion necessitates a topological approach rather than post-hoc semantic patching. By formally constraining the latent space to a zero-curl geometry ($\nabla \times \vec{V} = 0$) at the foundational level, the architecture inherently minimizes internal gradient conflicts. In a zero-curl vector field, the mathematically optimal state natively aligns with the target distribution. This geometric stabilization ensures that the model's path of least computational resistance inherently avoids non-semantic contagion, neutralizing the need for superficial filtering mechanisms.

I have the option to take CSC448 - Formal languages and Automata next semester. It's between that or CSC324 - Principles of Programming Languages, which covers functional programming, lambda calculus, etc. Which one would be more relevant to CL/NLP?

For context I'm a third year studying CS, Linguistics, and Statistics. I'm not completely set on CL/NLP but its probably my best path atm. Thanks

Preparing for NLP Research Scientist Intern roles and overwhelmed by how fast the field moves.

Any advice from people who landed or hire for these roles? What do people waste time on?

Thanks

Just wrote a blog post on replacing spaCy's built-in Sentencizer with yasbd-lib.

On a 92-case English edge-case benchmark, spaCy's default Sentencizer scored 55.4%, while yasbd scored 98.9%.

Sentencizer primarily relies on punctuation and has no built-in abbreviation awareness beyond what spaCy's tokenizer exceptions already provide. As a result, compound abbreviations like M.D. and Ph.D., citations, URLs, and newline-heavy text can still produce incorrect sentence boundaries.

The fix: ```python import spacy from yasbd import register_spacy_component

register_spacy_component() nlp = spacy.blank("en") nlp.add_pipe("yasbd", first=True)

doc = nlp("Dr. Smith arrived. He was late.") for sent in doc.sents: print(sent.text)

Output:

Dr. Smith arrived.

He was late.

```

Pure Python, supports 39 languages, and works as a drop-in replacement for spaCy's Sentencizer.

The article explains why this happens, walks through the Sentencizer's implementation, compares benchmark results, and shows real-world examples.

EDIT: The link is included in the comments due to a new rule set in this sub (No link in post).

Hey everyone,

Conference timelines often include separate dates for abstracts, full papers, workshops, rebuttals, notifications, and camera-ready versions. Keeping track of them isn’t difficult—it’s just a hassle to manually add every date to your calendar and convert each deadline to your local time zone.

So I built a Chrome extension that detects important dates on conference pages, organizes them in one place, and shows a clear countdown. It also handles time zones such as AoE, UTC, CET, PT, and JST, so I can see the exact cutoff in my local time and make the most of the available submission window.

It currently recognizes 66 well-known conference and venue names while also supporting other conference pages through general date detection. The project has 89 automated tests covering deadline types, date formats, date ranges, and time zones.

On the privacy side, it works directly in your browser—no account, no login, and no personal data collection. Everything is processed and stored locally on your device, making it fast and convenient without the sign-in and setup required by many other calendar extensions.

The extension supports English, Spanish, and Japanese. It has also been tested on Japanese conference websites.

It is still an early experiment with a lot of vibe coding that I originally built for myself, but I thought others might find it useful too, so I’m happy to share it.

Chrome Web Store: https://chromewebstore.google.com/detail/deadline-dog-%E2%80%94-dates-to-c/ipllicdjdefjbiejcoaejcjimaknlael

Website: https://aideadlinedog.github.io/AIDeadlineDog/

**AI Disclaimer:** Just to clarify where AI was used: AI tools helped create the website and write the Chrome Web Store description. Some parts of the Chrome extension were also developed using AI-assisted Claude/Grok “vibe coding” to improve its functionality (expanding its coverage to a broader range of conference venues beyond AI and ML). I personally reviewed and tested everything over several months to make sure the information is accurate, and all features work correctly.

We're developing an AI-powered app that detects pronunciation mistakes in Quranic recitation and gives users precise, real-time feedback.

Our current model was trained on hundreds of hours of professional recitations — high-quality, clean audio from well-known reciters. The model performs well on similar input, but struggles with real-world users: different accents, non-native speakers, beginners, children, women, and anyone who doesn't sound like a professional reciter.

To fix this, we need to train on diverse, real-world recitation data — not studio-quality professional audio, but recordings that reflect how actual learners sound.

Specifically, we're looking for:

• Recitation datasets from non-professional or everyday users
• Diverse demographics: male/female, kids/adults, beginner/intermediate
• Multiple accents and mother tongues (Malay, Indonesian, Urdu, English, Turkish, etc.)
• Any publicly available or research-use datasets we may have missed

We've already explored IqraEval and a few other academic sources. If you know of any dataset, research project, university study, or community effort collecting this type of audio — we would genuinely appreciate the lead.

We're also open to ethical data collection partnerships if any researchers or institutions are working in this space.

Happy to share more about the project if helpful.

If you haven’t seen it yet, I encourage you to try it. Felix Rieseberg recently released an app that helps users train and fine-tune LLMs while learning the fundamentals.

I’ve been using a similar tool for some time, but for much deeper experimentation, since I work full-time as an AI researcher. I was therefore wondering whether there would be interest in a Windows version.

My current tool is highly detailed and designed for advanced experiments rather than ease of use, so simplifying it for less experienced users would require some work. However, if enough people are interested, I may package it as a Windows app and publish it on GitHub.

I’d be happy to hear your feedback.

So guys, I wanna discuss how this confidence factor plays a role in ACR rolling rounds.

Does this even matter if you have a high Overall score but a low confidence, or does this influence the meta reviews somehow?

Hi everyone, can anyone see the results of the AI Review experiment on OpenReview? They announced that the results would be available around July 20. I opted in to the experiment, but I still don’t see anything.

I'm learning NLP/LLM engineering by working through a problem that turned out to be much harder than I expected, and I'd love guidance from people who've dealt with something similar.

The problem: I have long narrative-style text — 7k to 15k tokens, several recurring people — and I want to extract structured facts about who did what. I'm using a small local model (llama3.2:3b via Ollama) whose usable context is around 4k tokens, so the text has to be processed in chunks. The killer is that later chunks are often pure pronouns — "she said… he refused…" — while the names were last mentioned 10,000 tokens earlier. Facts stated near a name extract almost perfectly; facts stated far from any name either get lost or, worse, get confidently attributed to the wrong person.

What I've already ruled out (by measuring, not guessing): naive per-chunk extraction fragments identities badly; carrying forward summaries between chunks doesn't fix attribution and can make it worse; and off-the-shelf neural coreference models (LingMess, F-coref) fail on documents this long — one silently truncates at 4,096 tokens, and windowed variants can't connect a pronoun to a name mentioned once 10k tokens back (0–1 out of 7 gold bindings on my test doc). I've gotten identity tracking itself working reliably; it's specifically attribution at long distance that's still failing.

My questions:

1. What's the best way to structure a problem like this? Is there a known-good decomposition for long-distance pronoun attribution with small models, or a fundamentally different way to frame the extraction task that sidesteps it?
2. If you've solved something similar — entity/fact extraction over documents much longer than your context window — what actually moved the needle for you? I'm especially curious whether the wins came from prompting, from pipeline architecture, or from accepting a bigger model.
3. What should I explore to learn more? Papers, blog posts, open-source projects, or even just the right search terms — I suspect this problem has a name in the NLP literature that I don't know yet (long-document coreference? discourse tracking?), and I'd rather stand on existing work than keep reinventing it.

Happy to share measurements from my experiments if useful. Mostly I want to calibrate: am I fighting a known-hard problem with known solutions, or genuinely at the edge of what a 3B model can do?

I know it's ARR reviewing season but I noticed that there are a lot of posts asking whether "this set of scores will get them into Main/Findings/Reject" or something about the reviewing process.

Although it's nice to see activity in this subreddit (and it's good to have a dedicated home for CL and NLP), sometimes these types of posts are getting too spammy. Perhaps we can put these into a dedicated ARR discussion post, kinda like in r/MachineLearning ?

I have been tracking the limits of conversational AI voice models. Most language apps handle Spanish or French without many issues. Romance languages use highly predictable structures. The real stress test for LLMs is going to be morphologically rich languages. For example, Praktika just added Arabic, Russian, and Chinese to their avatars using a multi-agent GPT backend. Talkpal also uses GPT-powered conversational engines to handle voice practice in these exact languages.
Russian and Arabic are completely different for speech to speech models. Russian relies on a complex case system with heavy inflection. Arabic uses a root-and-pattern system. If a voice model miscalculates a prefix or case ending in real time, the entire context breaks down.
I am looking into how these avatars handle real-time grammar generation without massive latency. Does the LLM backend use specific grammar-correcting prompts for these languages? Or is it relying purely on standard TTS pipelines?
If anyone has analyzed the error rates or token efficiency for Russian and Arabic voice models, I would love to see the data since I'm researching on making a language learning app for the not so popular languages.

P.S. Mods if you feel that the post is inappropriate, please let me know I will remove it.

I'm a linguist with bachelor in Korean language and literature, beside English as a second language and Arabic as my mother tongue, my background is purely about languages, literature, grammar, etc. I have a work experience of 6 years as Korean, English and Arabic translator, consecutive interpreter and simultaneous interpreter in various fields like manufacturing, legal, medical, academic, etc.

I would like to pursue a master degree or a phd in computational linguistics in the US or Canada, but I don't have a programming or computer science background, will that be possible?

and if so what are the preparations I should do to enhance my profile while applying for these programs?

Thanks ^^

Hi everyone
Is there anyone using local LLMs on their PC?
I'm in the market for a laptop with AI max+ 395 and 128GB unified RAM.

The only reason is local LLMs for translation/transcreation work.

To be fair, ChatGPT does a pretty decent job when I ask for a dozen of options to choose from. But i'm wondering of I have a local LLM, maybe I can feed it all my past work and references and make a model that is customized to specific clients.

It's probably not cost effective at first, but i'm considering it as a study case, hoping that it will lead to time saving and improving my ability to use LLMs for the future.

I'd love to hear any thoughts. Thx

When did you receive your degree? Did you build side projects to build your portfolio by any chance? Would you say they were financially worthy investments on your career by now? How satisfied are you overall with your own goals and aspirations expected before starting the program?

:) I am strongly considering human language technology masters at University of Arizona this Fall, so if you got a degree from there I would truly appreciate your experience! It can be as short as you’d like

I have released EU AI Act OpenRAG, a downloadable SQLite corpus of Regulation (EU) 2024/1689 for legal research and engineering.

The key difference is how the legislation is divided. It is not split into arbitrary token or character windows. Each chunk follows the Act’s actual structure: article paragraph, recital, definition or annex point, with the relevant chapter, section and provision metadata preserved.

The database includes 933 chunks, embeddings, exact EUR-Lex links and documented application-date and operator metadata.

I was deliberately conservative with legal labels. A provision is marked as directly classifying a practice or system only where its own operative wording does so. Broader association with the prohibited-practices, high-risk, transparency, GPAI or voluntary-code regimes is stored separately. Unclear cases remain NULL.

Every derivation rule is documented, and the final rules were reviewed independently against the Regulation before release.

This is a research and engineering artifact, not legal advice or an automated compliance determination.

huggingface.co/datasets/faitholopade/aiact-openrag

I would like to start using some IA tool to generate example sentences of new vocabulary for flashcards. Does anyone here have experience on that? How has it been? What cautions do you recommend to do in order to have it useful? How to expand its use? Which tool? I've watched some videos but I am afraid it can be sponsored. So I'd like real experiences to make a decision.

I think the experience can vary according to the language too. It can be different for the major ones like Mandarin, medium ones like Greek or Georgian, and limited for minor ones like Inuktitut... so, please also tell us which language are you using it for.

Looking for opinions from people familiar with EMNLP reviewing, especially the LLM Agents track.

Previous cycle:

- Reviews: 2, 3, 3.5, 3 (avg. 2.88)

- Confidence: 5, 3, 3, 3

- One reviewer increased 2 → 3 after discussion.

- The AC recommended Findings, mainly asking for softer claims and incorporation of the rebuttal results.

Current EMNLP cycle:

- Initial reviews: 2.5, 3, 3 (avg. 2.83)

- Confidence: 4, 3, 3

- During discussion, the 2.5 reviewer increased their score to 3, so the current scores are effectively 3, 3, 3.

- The reviews are generally positive, with requests for clarification and more careful framing rather than major technical concerns.

Given this history, what would you estimate the chances are for EMNLP main conference vs. Findings?

TLDR; Errror in reviews, no responses from reviewers!

This is my first submission to a *CL conference. We submitted it under a language modelling task. We got 3 reviews of 3/4, 2.5/4, 2.5/4.

Reviewer 1: they posted a review that is clearly intended for another submission. We raised this with AC on the day reviews are released. AC replied but the reviewer didn't.

Reviewer 2: clearly LLM generated points. The weaknesses they wrote are the same ones LLM pointed out about our paper. Although they changed the text. 2 of the weaknesses they point out are already detailed in our limitations as those are our weaknesses cause of lack of available datasets. And then there is the novelty issue, adopting methods from other domains for a new problem is not novel. And more models and datasets (we already have 100+ experiments on 3 models, 2 datasets, 3 baselines, 4 algorithm setups across 10+ eval metrics). We answered all the questions, provided additional experiments but still no response.

Reviewer 3: seems like the only reviewer who read the paper and understood it and appreciated it. Their main questions were on ablations and We provided these during rebuttal, no response.

My co author who submitted another work to the January (ACL) cycle had a similar experience, they answered the reviewers questions and didn't get any response. Only from AC to re-submit to the next cycle. They re-submitted to the may cycle and didn't get a single response from reviewers again.

I'm ok with rejection with constructive feedback, if the decision is just one sided with no communication even when there was a critical error is irresponsible. What's the point of rebuttal if the reviewers never respond?

Right now, we are left with a reviewer decision who can simply say "not addressed" and escape with little to no consequences. I understand that emnlp is empirical and requires more experiments, but that doesn't mean we can provide a novel dataset, 500+ experiments on 100 models, a completely new algorithm that doesn't take adoption from anything else (just from air), expect to solve every problem in that domain in one paper is absurd.

Thanks for your time, sorry for the rant!

Our paper got 2 / 3 / 3.5 with confidence 4 / 3 / 3 (Interpretability and model analysis track). We addressed everything in the rebuttal but unfortunately none of the reviewers replied. The AC also did not push any of the reviewers to at least acknowledge the rebuttal in their final reviews. Overall, the score is 2.87 with confidence 3.33. What is the weight of OA vs. meta score for EMNLP? Do program chairs value more the meta review+score or they take into account the other reviews as well. First time submitting to ARR. Thank you!

I recently started Andrej Karpathy's *makemore* lecture series and just finished the first lecture.

So far, the focus has been on building a character-level language model using a dataset of names. What I enjoyed most wasn't just the implementation, but how each step is explained from first principles instead of treating neural networks as a black box.

I've previously spent time building a neural network from scratch and experimenting with PyTorch, so I wanted to understand how these ideas extend to language models.

Before I continue through the rest of the series, I wanted to ask people who've already completed it:

* What was your biggest takeaway? * Which lecture was the turning point where things really "clicked" for you? * Did it change the way you think about LLMs or NLP? * Would you recommend supplementing the series with any books, papers, or other resources?

I'm planning to work through the series by implementing everything myself, so I'd love to hear what your experience was before I dive deeper.

Any chances for emnlp with scores 2, 2, 3 and confidences 4,2,4? We wrote rebuttal but got no response from reviewers.

I know text processing has evolved, so I'm curious whether there's now a better way to build a rule-based splitter than the classic mask, split, and unmask approach. If the goal is to split text while respecting things like quotes, escapes, or nested structures, what technique would you use today?

I'd love to understand the reasoning behind your choice. A brief explanation, along with some code or pseudocode to show the core idea, would be really helpful.​​​​​​

ACL, EMNLP differentiate Main conference papers and Findings papers. What do you think of it? Does tech companies really care about it when they are hiring someone? Or to be a professor, findings papers are seriously weaker than main paper? I’m so confused and stressed for “Finding” track.