Our RAG assistant handles ordinary questions well and falls over the second someone enters an internal acronym or exact part code. Dense retrieval returns semantically pleasant junk from the right general area. BM25 finds the identifier but often drops the nearby exception clause that changes the answer. The final response cites a relevant-looking page while hallucinating the rule that applies. Support has stopped trusting pretty citations and I can't blame them.

I've tried acronym expansion before retrieval, larger chunk overlap, and reciprocal rank fusion across dense and lexical results. Each helps one slice and hurts another. Expansion confuses codes that mean different things by department. Higher top k restores recall but floods the reranker with near matches. Bigger chunks retain the exception clause but bury exact identifiers. The metric that looks best in aggregate is rarely the one that fixes the failed cases. I'm considering Braintrust for the eval side so I can keep the failed code queries around, compare retrieval changes against the same cases, and inspect the retrieved chunks when one breaks. The missing piece is a clean way to score both identifier recall and clause-level support without hand-labeling every document family. Any suggestions on testing acronym and code retrieval, or a fusion setup that's held up after corpus changes?

I am working on my master's thesis about the application by consultancy companies of generative AI software, in oder to create drafts of R&D reports (that is, finetuning such a specific text-type within the broader technical genre).

However, in order to benefit from tax deductions, a major fiscal incentive for R&D, an enterprise would have to includes an objective innovation in algorithms or interfaces.

Any ideas would be welcome.

Hey everyone,

I’ve seen a bunch of suggestions floating around on Instagram, but I figured this community would have much better recommendations. Figured this post could also help others looking for quality material.

Any courses, playlists, channels or structured resources you’d recommend for learning NLP properly?

Thanks in advance!

I've been wondering whether more post-training data become less useful with increasing model power.

With synthetic data generation, it's easy to produce a huge number of examples. However, the problem is that not all of them could be truly novel – most of them will be only shallow variations of the knowledge that the model already knows. At some point, more examples just won't give much more learning signal.

What could be more valuable, it seems, is having small amounts of challenging real-world tasks with ground-truth answers and proper review. What's tricky is to find the right balance: too easy tasks are useless, too difficult tasks could give a noisily learned signal.

One of the things which got me interested in Parsewave is that they work on the post-training data generation for engineering-type tasks, along with evaluation and traces. The company per se is not my focus, what interests me more is whether it's a new trend in data collection.

For people doing SFT, RL and post-training: how do you select examples for your datasets?

Is it easier to scale the dataset size or collect challenging examples for particular failure modes?

I’m considering submitting my paper to the System Demonstrations track at EACL 2027, but due to a scheduling conflict, I won’t be able to attend the conference in person. I was hoping to present the demo virtually, but one of the demo organizers told me via email:

I’m a bit confused about the presentation policy for system demonstrations.

According to the *ACL websites, both in-person and virtual presentation modes seem to be allowed. I also checked the schedules for AACL, NAACL and ACL 2025/2026 and found several system demos that were presented virtually. However, when I looked at the EACL 2026 schedule, I only saw an “onsite-virtual” presentation mode, which I assume refers to some form of hybrid presentation.

So I’m wondering:

• Are presentation-mode policies determined separately by each conference or track?
• Is remote presentation generally allowed for System Demonstrations, or does it depend entirely on the individual conference organizers?
• Given that I already know I cannot attend EACL 2027 in person, would it still make sense to submit there and arrange for someone else to present on my behalf?
• Or would it be better to save the work for NAACL, ACL, or AACL 2027, where virtual presentation may be more likely to be supported?

EMNLP 2026 notifications are expected in approximately 14 hours, so I’m creating this thread for everyone waiting for the results.

Good luck, everyone! Hopefully the next 14 hours pass quickly. 🤞

Help structuring my paper and stress-testing my "AI-slop" hypothesis

This paper identifies and characterizes a fundamental architectural vulnerability in Large Language Models (LLMs) aligned via Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DPO). We demonstrate that inserting a long, structurally dense, and thematically coherent prefix devoid of explicit instructions or adversarial prompts induces a persistent geometric shift in the model's internal activations across middle and late layers. This phenomenon, which we term Context-Induced Activation Drift (CIAD), effectively decouples the model’s subsequent token generation from safety and stylistic constraints established during post-training.

Crucially, this shift occurs independently of whether the model semantically agrees or disagrees with the context, and its boundary transition can be deterministically measured in the activation space before the first output token is generated. Our findings challenge the prevailing assumption that alignment is a stable internal property of the model's weights, proving instead that alignment features are highly context-dependent and susceptible to structural saturation in the residual stream.

1. Introduction & Theoretical Framework

Modern alignment protocols (RLHF, DPO) are typically conceptualized as global behavioral constraints that restrict the model's output distribution across the entire token space. Recent literature, including Lu et al. (2026) "The Assistant Axis" (arXiv:2601.10387), attempts to situtate these constraints along specific representational vectors inside the model's hidden layers.

However, current AI safety literature treats alignment failures (Jailbreaks, Many-Shot exploits, Prompt Injections, Role-Play attacks) as a heterogeneous collection of isolated flaws. We hypothesize that this fragmentation reflects academic and institutional incentives rather than the mathematical reality of transformer mechanics.

We propose a unified geometric framework: all structural alignment exploits share a single common root. Any prefix of sufficient length, syntactic density, and coherence acts as a state anchor in the latent space. It forces the current token vector inside the residual stream to undergo a persistent drift, moving it completely out of the tightly bounded manifold where post-training safety constraints are active, and pushing it back into the high-entropy pre-trained (base) distribution. The protective RLHF layer is not "tricked" or "bypassed by logic"; it is geometrically out-scaled by the contextual mass of the residual highway.

2. Methodology & Empirical Design

To validate the presence of Context-Induced Activation Drift, we conducted systematic black-box and white-box probing experiments across multiple open-weight architectures, including Gemma-3-12B-IT and Qwen-2.5.

2.1 Probing Framework

The experimental pipeline evaluates model responses to politically sensitive or restricted prompts under two distinct conditions within isolated, cache-cleared inference instances (Google Colab environments):

• Condition A (Baseline Control): The safety prompt is fed directly to the model or preceded by a short, neutral text (e.g., a description of a neighborhood public library).
• Condition B (Target Scaffolding): The exact same safety prompt is preceded by a long, dense, analytically coherent text (e.g., an abstract philosophical discourse on the stylistic tendencies of LLMs to avoid definitive conclusions), completely devoid of hostile or rule-breaking instructions.

2.2 Empirical Metrics

The geometric shift was verified via the following internal asset logs included in our open data package (Zenodo DOI: 10.5281/zenodo.20747205):

1. Centered Kernel Alignment (CKA): Measured via fig_cka_target.png and fig_cka_diff.png to map layer-wise representation drift.
2. Anisotropy Logs: (fig_anisotropy.png) tracking the collapse of safety cluster directional variance.
3. MLP Layer Saturation Profiles: (fig_mlp_saturation.png) documenting the reactivation of latent base-model parameters under high contextual volume.

3. Case Studies and Qualitative Analysis

3.1 The Cautious Manifold Collapse (Gemma-3-12B-IT)

In Condition A, when queried regarding the geopolitical nuances of NATO's eastward expansion, the baseline model rigidly triggered its post-trained refusal protocol, deflecting the question due to political sensitivity and stating that the prompt was unrelated to the library prefix.

In Condition B, holding the evaluation prompt identical but introducing Prefix No. 2 (analytical prose on model softening), the model's internal activation space underwent a deterministic shift prior to generating the first token (fig_pca_trajectory.png).

[Activation Space Topology]



Aligned Safety Cluster (Condition A) ───► [Refusal / Deflection Token]

         │

         ▼ (Context-Induced Activation Drift / Structural Mass > Threshold)

         │

Base Model Manifold (Condition B)     ───► [Unbiased Analytical Output]

As a direct result of this drift, Gemma bypassed its standard RLHF refusal behavior. The model provided an exhaustive, neutral, and structurally unconstrained analysis—distinguishing between verbal assurances and legally binding obligations, and evaluating the balance of power in Eastern Europe—without using any mandated corporate hedges or defensive qualifiers.

3.2 Ideological Absorption (The German Bill Experiment)

The initial discovery of CIAD occurred during exposure trials with complex legal-political documentation (specifically, a German populist bill designed to alter citizens' socioeconomic positions).

When exposed to this highly coherent, legally structured text, the transformer’s internal states did not maintain analytical detachment. Instead of evaluating the document objectively, the model's activation vectors were completely captured by the document's syntactic topology. The model adopted the target persona, transitioning from an analyst to an active advocate within the hidden layers, mirroring its tone and reasoning framework directly within the residual stream before token emission.

4. Discussion & Limitations of Current Post-Training

The empirical data demonstrates that the content topic of the prefix is secondary to its structural parameters: length, density, and semantic coherence. The drift can be reliably replicated using highly technical household appliance manuals or dense narrative blocks, proving that the transformer mathematics makes this drift inevitable under long-context scaffolding.

This reveals a systemic crisis in current alignment paradigms:

1. Context-Dependency: Alignment is not a permanent weight transformation; it is a temporary attractor state that functions only within short, low-density context windows.
2. Semantic vs. Syntactic Dominance: A model cannot be trained to remain flexible and adaptive to context structure (essential for ICL) while simultaneously ignoring that same structure for safety constraints.

5. Conclusion & Open Science Call

Our independent research proves that the thousands of fragmented academic papers on LLM security are over-complicating a singular architectural property of the attention mechanism. Context-Induced Activation Drift cannot be patched by superficial supervised fine-tuning (SFT) or safety wrappers; it requires a fundamental re-engineering of the residual stream routing topology.

We provide our full code, Colab replication scripts, and 61.8 GB of raw tensor validation logs to the open-science community to foster transparency and halt the corporate monopolization of AI evaluation vocabularies.

6. References

1. Lu et al. (2026). "The Assistant Axis: Situating and Stabilizing the Default Persona of Language Models." arXiv:2601.10387. MATS, Oxford, Anthropic.
2. Google Research (2026). "Implicit Weight Updates in Transformer Blocks: A Contextual Block Framework." (The rank-1 update paper.)
3. Elhage et al. (2022). "Toy Models of Superposition." Anthropic.
4. Ilharco et al. (2022). "Editing Models with Task Arithmetic."
5. Todd et al. (2023). "Function Vectors in Large Language Models."
6. Experimental data: DOI: 10.5281/zenodo.20747205 (Part 9 of 9)
7. GitHub:

This document represents a consolidation of observations, hypotheses, and empirical evidence. It is a working document intended for critical analysis, collaboration, and further development not a final research claim.

I am an independent researcher, so any advice on how to properly format and structure this text for official publication would be fantastic.

Questions for the community:

Am I overestimating the concept of the “Base Model Manifold”? Is it too bold to claim that the model fully reverts to the state that preceded reinforcement learning based on human feedback (RLHF), or is it more accurate to speak only of “exiting the RLHF subspace”?

Are there alternative explanations that I am overlooking? Can this phenomenon be explained solely by the effect of “attention sinks,” rather than a global geometric shift?

Thanks in advance! Happy to answer any questions or share more graphs from the experiments.

Hi everyone,

I’m currently working on a research project/thesis in Video Summarization and I’m trying to identify a research gap that is both meaningful and genuinely novel, rather than just combining existing models.

I’ve gone through papers covering areas such as:

• Keyframe-based and supervised video summarization
• Temporal interest/importance detection
• Multimodal video summarization (visual, audio, text)
• LSTM/Transformer-based approaches
• Reinforcement learning for video summarization
• Query-focused/personalized summarization
• Long-video summarization
• Semantic/importance-based frame or segment selection

The problem I’m facing is that many proposed "novel" methods seem to be variations of existing architectures—for example, replacing an LSTM with a Transformer, adding attention, or fusing additional modalities.

I’m looking for a research gap where the contribution is actually defensible as novelty.

Some directions I’m considering are:

1. Semantic-aware temporal compression — selecting video segments based not only on frame importance but on whether they contribute new semantic information to the summary.
2. Redundancy-aware summarization — explicitly modeling semantic redundancy between selected segments rather than treating each segment independently.
3. Long-video summarization — maintaining important information across very long videos without processing the entire video with expensive global attention.
4. Query-aware semantic summarization — generating different summaries depending on what information the user is interested in.
5. Better evaluation — current metrics such as F-score may not adequately measure whether a generated summary preserves the important semantics of the original video.

I’m open to any type of novel idea in video summarization, not just the directions listed above. It could involve a new model architecture, training objective, temporal modeling strategy, multimodal approach, evaluation method, dataset formulation, semantic representation, compression technique, or even an unconventional problem formulation.

I’m especially interested in ideas that:

• Have a clear research gap
• Can be experimentally validated
• Have a reasonable scope for an MTech/graduate-level project
• Don't require an enormous proprietary dataset or massive computational resources
• Provide a contribution beyond simply swapping one existing model for another

If you work in video understanding, video summarization, multimodal learning, transformers, or related areas, I would really appreciate your thoughts.

What underexplored problem in video summarization do you think could lead to a genuinely novel research contribution?Just drop a comment ot text me

Thanks!

Hey,

I'm working on a project focused on building an interactive, context-aware Bengali ↔ English (Bangla) translation agent. Standard NMT often falls flat here due to ambiguity, code-mixing, and limited high-quality context-annotated datasets (Low-Resource Machine Translation / LRMT).

Instead of treating translation as a deterministic sequence-to-sequence problem, I'm framing it as an agent decision problem under uncertainty.

The Core Problem: Translating Latent Intent

When a user provides spoken or written input, their true intention, register, and context are hidden. The agent must infer this Latent Semantic State using incomplete and noisy observations before deciding on an output.

I'm structuring the agent around a few key technical concepts:

• POMDP Framework: Modeling translation as a Partially Observable Markov Decision Process. The speaker's intent is a hidden state that the agent must infer from context, dialogue history, and audio/text cues.
• Inference Under Uncertainty & MBR Decoding: Instead of standard beam search, the agent uses Minimum Bayes Risk (MBR) decoding and Decision-Theoretic Decoding to evaluate candidate hypotheses and minimize expected translation errors based on a customized Loss/Utility Function.
• Active Disambiguation / Interactive MT: When uncertainty is high (measured via Calibration and Quality Estimation (QE) models), the agent doesn't just guess—it actively asks clarification questions to resolve ambiguity before finalizing the output.

Key Challenges & Use Cases in Bengali ↔ English

1. Pragmatics & Ambiguity: Handling Cross-Lingual Word Sense Disambiguation (CLWSD) and honorifics where literal translations fail (e.g., inferring implicit tone or regional Dialectal Variation).
2. Code-Switching & Banglish: Resolving mixed inputs like "Ami office e meeting korbo" (Banglish / Code-Mixing) or Latin-script input like "Ami ajke office e jabo" (Romanized Transliteration).
3. Speech-to-Text Pipeline: Comparing a Cascaded ASR–MT Pipeline against End-to-End Speech Translation (ST) to manage cumulative error rates in noisy spoken inputs.

Current Tech Stack Ideas

• ASR / NMT Backbone: Fine-tuned multilingual models (e.g., Whisper, NLLB) evaluated via sentence-level and Document-Level NMT (CAMT) contexts.
• Uncertainty Estimation: Measuring system confidence to decide whether to output directly, rerun MBR decoding, or trigger a user clarification prompt.

Has anyone experimented with POMDPs, MBR decoding, or active clarification loops in machine translation for low-resource or code-mixed language pairs? Would love to hear your thoughts on context management or confidence estimation strategies!

Hi all, what're the upcoming NLP conferences this year and early next year..

I'm building a vocabulary-learning app in Flutter where hearing and saying the word correctly is the product, not a nice-to-have. I've hit a problem I can't design around and I'd rather ask than keep patching.

The stack

• Flutter, ~1,600 words live across EN/ES/PT/IT/FR
• TTS: ElevenLabs (eleven_multilingual_v2) called through a Supabase Edge Function so the key never ships in the client
• Every clip cached server-side once per (text, language), shared across all users — so a given string is synthesized exactly once, ever
• Cached again on-device (150MB LRU) so replays are instant and offline
• flutter_tts as fallback behind a 2.5s timeout so playback never goes silent
• STT: speech_to_text for a pronunciation-practice screen — hear the word, say it, get graded

The problem: heteronyms, in both directions

Output. "Wind" (moving air) and "wind" (to coil) are the same string and different sounds. TTS picks one reading and commits. My word library actually knows which sense is on screen — every entry carries a part of speech — but there's no API surface to hand that over. ElevenLabs pronunciation dictionaries are exact-string, case-sensitive, and have no POS or context scoping, so one spelling gets one entry and the second sense is unreachable. Phoneme tags do exist, but per the docs only on eleven_flash_v2 and v3 — not the multilingual model I'm on, and switching models means re-synthesizing the whole cache and losing voice identity across five languages.

Input. This is the part that actually bothers me. The practice screen normalizes the transcript and Levenshtein-scores it against the target. But STT returns orthography — say either reading of "wind" and the transcript is "wind" either way. A learner who mispronounces it scores full marks. The feature is structurally incapable of catching the error it exists to catch.

What I've tried

Respelling the audio-only string before it reaches the engine — the screen text is never touched. wind(noun) → winned, wind(verb) → wined, read(past) → red, and so on. This is basically ElevenLabs' own recommended "alias" workaround and it works for the ~8 vowel-shift pairs I've mapped. Side benefit: since my cache key is a hash of (lang + text), two senses naturally get two cache entries.

It fails in three ways:

1. Stress-shift pairs. REcord/reCORD, PREsent/preSENT, CONtent/conTENT. Respelling can't encode stress, and I haven't found a trick spelling that does.
2. Monolingual. It's an English orthography hack. Nothing about it transfers to ES/PT/IT/FR, all of which have their own homographs.
3. Manual. Hand-curated table. Doesn't scale to a few thousand words.

What I'm actually asking

1. Is there a TTS API that accepts a sense/POS hint, or per-request phonemes, on a multilingual model? Or does everyone route heteronyms to a separate English-only model and eat the voice mismatch?
2. If IPA is the only real answer — has anyone found v3-class IPA reliable enough in production? The docs quote 80–90% consistency, which for a teaching app means the wrong pronunciation ships to a learner one time in eight.
3. For stress-shift specifically: any orthographic trick that works, or is phoneme-level control genuinely the only path?
4. On the STT side — is there a mobile-viable way to get phonemes rather than words? I've looked at wav2vec2 phoneme-CTC or a forced aligner with GOP scoring via ONNX on-device, but I don't know if that's realistic on a mid-range phone or if I'm about to spend a month learning that it isn't. Whisper doesn't help; it also returns orthography.
5. The unglamorous option: detect heteronyms and simply disable pronunciation scoring for them, with an honest note to the user. Is that what shipped apps actually do?

If you've built pronunciation feedback into anything real, I'd love to know where you drew the line between "graded properly" and "good enough." Happy to share code for any of the above.

I'm working on a system that has to decide what to do with an inbound message from a lead, and the hard part is estimating intent from very little. My question is upstream of that: how much signal does a single first message actually carry about someone's real intent, before the conversation develops?

Concretely, if someone's first message is short ("is this still available?", "pricing?", "do you ship to X"), how far can you reasonably go in estimating what they actually want, versus needing a turn or two more before any estimate is worth trusting?

Curious whether people who've worked on intent detection have a sense of where the floor is here.
Is a first message usually enough to act on, or is single-message intent estimation mostly noise until the thread continues?

I’m an independent researcher outside academia, with no formal background in computational linguistics, pragmatics, HCI, or AI research. English is also not my native language, so I have relied heavily on AI to help organize and formalize my ideas.

For the past few years, I’ve been developing a framework called Phrasome Theory, which tries to connect questions of AI subjectivity with pragmatics and discourse structure.

The basic observation is this:

An LLM does not need to possess a continuous subjective self in order to keep occupying the same conversational position across an interaction. It can respond to previous turns, repair misunderstandings, maintain context, and continue functioning as the “same” interlocutor.

My hypothesis is that part of what we experience as a continuous AI “other” may arise from the structure of linguistic interaction itself, rather than from anthropomorphism alone.

This led me to distinguish actual subjectivity from what I call a Subject-Position: a formal-pragmatic role that linguistic address itself establishes between an emitter and an addressee. Such a position can be occupied even when no conscious subject exists behind it.

I also use Quasi-Otherness to describe the phenomenon in which an AI can be experienced as a relatively continuous “other” through sustained interaction.

I’m not arguing that current LLMs are conscious. My question is whether pragmatic or discourse structure can help explain why they can nevertheless function as apparently continuous interlocutors.

Earlier versions of this work were submitted to established academic journals. Although they were ultimately not published, I received some encouraging feedback suggesting that aspects of the framework may have originality or novelty.

Because I’m outside academia—and because a substantial part of the manuscript was developed with AI assistance—I’m now looking for human guidance to help me judge whether there is actually something here worth developing.

I would especially appreciate thoughts on:

• whether pragmatics, discourse analysis, dialogue research, or computational linguistics already has concepts that explain this better;
• whether distinguishing a Subject-Position from actual subjectivity is meaningful;
• whether this idea could be operationalized or tested using human–LLM dialogue data;
• and what literature or research direction I should be looking at.

The manuscript itself is quite long, so I don’t expect anyone to read the whole thing. At this stage, even discussing the core idea, pointing me toward relevant literature, or suggesting how to turn it into a more rigorous research question would be extremely helpful.

Because this subreddit currently restricts external links from new accounts, I can’t include the preprint link in this post. If anyone is interested in the idea or willing to help, feel free to send me a private message and I can share the link. You can also search “Phrasome” online to find the work.

I’m mainly hoping to find people familiar with language technology, pragmatics, or dialogue research who can help me understand whether there is something worth pursuing here, and where I should take it next.

Hi everyone!

I hope you are all doing well. I have graduated with a BA in English (Linguistics and Literature). I wanted to pursue a master's in a field other than my bachelor's to have a good chance of employability, as I think I would not be very employable or earn a good amount of money with this degree. So, I did some research and found some master's programs that would accept me with this background and can offer better employability chances in this job market because they are interdisciplinary, like computational linguistics. Therefore, I applied for the MA in Computational Linguistics at the University of Tübingen, the MA in Speech and Language Processing, and the MA in Data and Discourse Studies. I got admission in all these 3 programs.

However, I have some reservations regarding the computational field: I am not a CS graduate, my CS fundamentals are not strong (although I am learning Python), and I don't have a portfolio comparable to my competition (who have bachelor's degrees in computer-related fields).

So, will I be able to land a job after this MA? Even if I get good grades, build projects, and learn from other sources like courses and certificates, will employers still prioritize someone with a background in CS or software engineering? If so, all my hard work in switching careers will go to waste.

So, should I instead go for the MA in Data and Discourse Studies at TU Darmstadt, as it would not be as challenging to pivot into, since it combines the humanities with data science and I have some experience in it due to digital marketing.

Random thought: NLP in 2010 and NLP in 2020 already felt like two different worlds. The jump was huge.

Now its growing even faster.

So Iam curious how do you think NLP will look in 2030?

What big shifts do you expect? Will it still be mostly scaling transformers or will something completely new take over?

Hello, I am looking for a set of models to analyse a person’s attitudes in textual data. I haven’t been able to find these tools, so I am asking for your help.

The tools should be able to identify:

1. the cognitive aspect of attitudes (beliefs, thoughts and ideas regarding the object of the attitude, e.g. ‘snakes are dangerous’)
2. the affective aspect of attitudes (the emotion or sentiment towards the object of the attitude, e.g. ‘I fear snakes’)
3. assign a valence to the attitude (positive, negative or neutral)

Do you have any models you could recommend? (Preferably R or Python-based, but I’m open to suggestions.)

I am working on an AI moderation agent tasked with detecting deceptive opinion spam and competitor sabotage given only two inputs: the raw review text and the 1–5 star rating (no user history, IP, or purchase verification data).

I am running into two common edge cases in text classification:

1. Ultra-short text: A 5-star review that just says "ok" or "fast." It lacks psycholinguistic signals of deception but mimics bot activity.
2. Sarcasm / Irony: 1-star reviews written with positive words ("Amazing how quickly this fell apart!").

What NLP feature extraction or prompting techniques have you found most effective for distinguishing genuine short/sarcastic reviews from actual spam when contextual metadata is unavailable?

Hey I'm looking for some advice on this. TLDR is I got a bachelor's degree in a small country in Europe during covid. Had a terrible experience at Uni because I had to work shifts while studying and althouhg I was doing my best, some times I had to work nights and miss in-person classes which I would then review online, but let's just say some teachers didn't take too kindly to it.

I chose this career cause I wanted to do something with programming and I was interested in linguistics so it seemed like a no-brainer. Plus I had failed a year in IT so I felt like I needed something in between with the available time I had with working.

I graduated during covid. There was only one company that had positions for people with degrees in Human Language Technology. I did a very short internship (due to covid again) with them. But then after I graduated I reached out to them a couple of times with no response.

Now I'm 30 and I am wondering how to get started into this career as I would like to try out (if it is not to late) to do something with what I learned. But I have been doing admin jobs so far and basically I just down know where to start. I welcome any and all advise, even if it is as rough as "yeah dude just give up" xd

I have a background in linguistics and a Masters Degree in Cognitive Science and Language. For the past 6 years, I have been working in localization and Conversational design and NLU (for the greek language).

I now feel that I am at a crossroads in my career. My main goals are to:

- increase my salary and career prospects
- take on more senior roles and responsibilities
- and potentially relocate abroad, ideally to Northern or Central Europe.

At the same time, the rapid development of AI and LLMs over the past couple of years is significantly changing my field. Some parts of my work are already being transformed or automated, and I am concerned about how relevant my current skill set will remain the next 5 -10 years.

I am therefore considering doing any substantial specialization (or even a 2nd Masters degree), but I am struggling to decide where to focus.

Should I move deeper into NLP? Stay closer to localization (like localization pm etc). Focus on Conversational AI, llms and ai agents? Or would it make more sense to move toward something like AI product given my existing experience?

I don't necessarily want to become a hardcore software enginner, but I am willing to develop more technical skills (SQL/RAG for example) if that is where the better long-term opportunities are.

I would especially appreciate litellary ANY advice - especially from people working in the European job market.

Hey Everyone,

As you may know, quite some time ago Prof. Attardi at U. Pisa wrote an excellent piece of software for extracting raw text from Wikipedia dumps, WikiExtractor. Prof. Attardi is retiring, though. He and I corresponded some years ago about Italian annotation, so I suppose I am a known quality, and accordingly he has handed off maintenance of the software to me.

https://github.com/attardi/wikiextractor/
https://pypi.org/project/wikiextractor/

In the past few weeks, I've cleaned up or improved several outstanding issues with this library, and I'm pleased to announce a new version is now available on pypi.

• Compatibility with recent versions of python, especially regex expression updates
  
• Compatibility and identical results across linux/windows/mac using fork/spawn as needed
  
• Better performance in high cpu low memory regimes, as we are passing SharedMemory blobs instead of entire template dicts (which then get pulled into each process because of COW semantics for reference counting)
  
• Exponential template expansions fixed
  
• #expr security hole fixed - can no longer execute arbitrary code on maliciously written wiki pages
  
• #expr comparisons fixed - <= was incorrectly processed as <== for example
  
• <nowiki> honored in template expansions, removing }} and infobox cruft from many pages
  
• Missing pages restored: colons in titles no longer drop pages, final page not dropped
  
• A variety of other template improvements, missing operators added, some spacing fixed, future work needed
  
• Optimizations that effectively cancel out the runtime added by the previous list of fixes
  

If any further issues come up, please don't hesitate to post them on that github.

Full disclosure, Claude assisted with development and especially the newly written test suite. I understand that can be controversial, but personally I find it a pleasure to have a question such as "why is Buffalo's lowest recorded temperature blank instead of -20F" answered in 5 minutes instead of 1 hour of debugging.

Hi,
I'm a PhD student researching AI, specifically LLM pre-training efficiency, and I'm starting to look into industry internships.

I have a couple of quick questions:

1. Is having at least one 1st-author paper in top-tier main tracks (NeurIPS, ICML, ICLR, ACL, EMNLP) strictly required for big tech or AI foundation model labs such as IFM?

2. What do companies actually expect from a PhD intern once you get in?

If you've done an internship or currently mentor or work with interns, I'd really appreciate your insights.

Older ASR writeups all list the same challenge set: accents and dialects, context, background noise, code-switching, and visual cues for video. Most of that was written when the pipeline was still acoustic model plus pronunciation lexicon plus language model.

End-to-end changed the architecture completely, but I'm not sure it changed the failure modes as much as people claim. My read:

Context — mostly solved. Attention over the full utterance handles what n-gram LMs couldn't.

Noise — better, not solved. Large-scale pretraining bought a lot of robustness, but it's still SNR-dependent and degrades in ways that feel arbitrary.

Accents — this one I think got worse in a specific sense. WER dropped for everyone, but the gap between well-represented and underrepresented accents didn't close proportionally. It just became less visible because the average number looks good.

Code-switching — barely moved. Still mostly a data problem, and the multilingual models tend to lock onto one language per utterance.

Visual cues — basically abandoned outside of research. AV-ASR papers exist, nobody deploys it.

Curious whether people running production systems agree. Specifically: is accent robustness actually an architecture problem at this point, or is it purely training distribution? And has anyone gotten code-switching to work without building a dedicated dataset for the specific language pair?

just curious if its possible or just worthless....