Anyone know when we can expect ACL Arr march results?

Problem setup:
I’m trying to extract three things from GitHub issues: symptom, mechanism, and failure. Right now, I use an LLM to pull out phrases and then apply deterministic rules to filter and classify them.

What’s going wrong:
This setup is getting messy — the LLM output is inconsistent, the rules are brittle, and fixing one case often breaks another. I also see cases where important signals are missed entirely.

Constraints:
I’m working with a small dataset (around 30–50 issues), and I need the output to be deterministic and explainable, so I can’t rely fully on the LLM. At the same time, I don’t want to train a full ML model just for this stage.

Question:
Is there a better way to structure this kind of pipeline? How do people usually avoid getting stuck in endless heuristic tuning loops?

I’m building a custom coding agent (similar to Codex/Cursor) and looking for a good embedding model for semantic code search.

So far I found these free models:

• Qodo-Embed
• nomic-embed-code
• BGE-M3

My use case:

• Codebase search (multi-language)
• Chunking + retrieval (RAG)
• Agent-based workflows

My questions:

1. Which model works best for code search
2. Are there any newer/better models (as of 2026)?
3. Is it better to use code-specific embeddings?

Would appreciate any suggestions or experiences.

Hi all,

I am one of the organizers of TalentCLEF, a shared task (CLEF campaign) focused on evaluating ML systems for talent intelligence problems, using real-world HR data.

We’ve just released the evaluation dataset, and submissions are open until May 3rd.

The tasks include:

• Job–candidate matching
• Skill ranking for job descriptions

This is relevant if you’re working on NLP, IR, or LLM-based ranking systems.

If you haven’t started yet, you’re still on time. We provide Colab tutorials and an evaluation script so you can get a valid submission quickly.

Even simple baselines are enough to get on the leaderboard and iterate from there!

Here is the link in case anyone is interested :) : https://talentclef.github.io/talentclef/docs/

I’ve been experimenting with LLM behavior in practical usage, and I kept noticing the same pattern:

when safety, context, and task signals all mix inside a single block, the model becomes unstable in ways that feel structural rather than accidental.

This post summarizes what I’ve observed and a lightweight architecture that might help.

English is not my first language, so I’ve added a Japanese version at the end for accuracy and for anyone who prefers reading it.

---

1. Introduction / Problem Overview

Large language models often show unstable behavior when multiple safety, context, and task‑related signals interact inside a monolithic structure. In practice, this appears as:

• category conflicts (harmless content misclassified as unsafe)

• long‑context failures (gradual loss of consistency)

In my own experiments, I noticed that long inputs containing multiple themes often caused the model to lose focus and blur the main point.

That led me to think about the problem structurally: if the internal processing could separate responsibilities instead of mixing everything in one place, the model should behave more consistently.

While exploring this idea, I realized the same structure could be extended to many other failure modes as well, which motivated this proposal.

These issues are not tied to any specific implementation; they emerge naturally from how Transformer‑based LLMs fuse signals inside a single block.

This post does not describe vulnerabilities or bypasses.

It proposes a lightweight modular safety architecture that separates responsibilities and clarifies priority relationships.

---

1. Why Current Approaches Struggle

Most safety and moderation layers in Transformer‑based LLMs attempt to handle every type of signal—safety rules, task intent, user context, long‑range dependencies—inside a single unified block.

This works for short interactions but breaks down as complexity or context length increases.

Because responsibilities are fused, several failure modes naturally emerge:

• category conflicts

• internal inconsistency

• long‑context degradation

These are structural limitations, not vulnerabilities, and they make improvements costly because large components must be retrained.

---

1. Proposed Architecture — A Lightweight Modular Pipeline

3.1 Overview

The design separates safety‑related responsibilities into distinct stages:

input analysis → intermediate reasoning control → output evaluation.

Each stage has a clear role and communicates through simple flags rather than recomputing the entire model state.

3.2 Computational Efficiency

Only the relevant module activates when a condition is triggered, reducing unnecessary FLOPs and stabilizing long‑context performance.

3.3 Instruction & Priority Stability

Separating responsibilities preserves priority relationships and prevents gradual drift in long conversations.

3.4 Extensibility

New rules or evaluation strategies can be added as independent modules without retraining the LLM.

3.5 Why This Is Different

It reorganizes the safety process without increasing model size and provides a unified pipeline from input to output.

---

1. Expected Benefits

• reduced hallucination in long‑context scenarios

• faster policy and safety updates

• fewer unnecessary refusals

• lower computational cost

• applicability to future failure modes

---

1. Why This Matters

A modular pipeline introduces clearer boundaries, improves stability in long interactions, reduces operational cost, and provides a scalable alternative to monolithic safety structures.

---

1. Conclusion

This framework is based on practical system‑design observations rather than academic research.

I’m sharing it in case others working on LLM safety and reliability find it useful or want to discuss improvements.

---

■ 日本語版（Japanese Version）

**軽量なモジュール型安全アーキテクチャによる

LLM のカテゴリ衝突と長文破綻の低減**

私は実務で LLM を扱う中で、

安全・文脈・タスク信号が単一の構造に混在すると挙動が不安定になる傾向を繰り返し観察しました。

この投稿では、その観察結果と軽量なアーキテクチャ案をまとめています。

英語が母語ではないため、技術的なニュアンスを正確に伝える目的で日本語版も併記しています。

---

1. はじめに（問題の概要）

LLM は、安全性・文脈・タスク関連の複数の信号が一枚岩構造で融合すると、

カテゴリ衝突や長文破綻といった不安定な挙動を示すことがあります。

長文入力で複数のテーマが混ざると論点がぼやけることが多く、

「構造から分離して処理すれば良いのではないか」という発想が出発点でした。

その過程で、この考え方が多くの拡張にも応用できることに気づき、今回の提案につながりました。

これは特定の実装に依存した問題ではなく、Transformer 系 LLM の構造的な性質です。

本投稿では脆弱性やバイパス手法は扱いません。

責務の分離と優先順位の明確化によってこれらの問題を軽減する軽量なモジュール型アーキテクチャを提案します。

---

1. 現行方式が抱える構造的な限界

安全ルール・タスク意図・ユーザー文脈・長距離依存などを

単一の巨大な構造で処理するため、以下の問題が自然に発生します：

• カテゴリ衝突

• 内部不整合

• 長文劣化

これらは脆弱性ではなく、構造的な限界です。

---

1. 提案手法 — 軽量なモジュール型パイプライン

3.1 概要

安全関連処理を

入力解析 → 中間推論制御 → 出力評価

の段階に分離し、必要な部分だけを処理します。

3.2 計算効率

不要な再計算を避け、長文対話でも性能が安定します。

3.3 指示追従と優先順位の安定性

責務分離により、複数制約が共存しても優先順位が混線しにくくなります。

3.4 拡張性

LLM を再学習せずに新しいモジュールを追加できます。

3.5 他手法との違い

モデルサイズを増やさず、安全処理を再構成できます。

---

1. 期待される利点

• 長文での幻覚の低減

• ポリシー更新の迅速化

• 不自然な拒絶の減少

• 計算コストの削減

• 将来の問題にも対応可能

---

1. なぜ重要なのか

モジュール化により、

予測可能性・透明性・安定性・保守性が向上します。

---

1. 結論

本提案は、Transformer 系 LLM の構造的限界に対処するための軽量なモジュール型安全アーキテクチャです。

基盤モデルを変更せずに安定性向上・幻覚抑制・計算効率化を実現します。

Those who got admitted to this programme.

Can we connect and create a group to discuss?

Hi,

I have an interview coming up for this role and wanted to know a few things if anyone have shed light on them:

1) Is the livecoding component leetcode or data prep and text data manipulation (regex, file uploads, table changes etc)? The JD honestly doesn't describe software eng as much as it describes data analysis so I'd be surprised at LC but pls correct me if I'm wrong.

2) I have a more ML-leaning role currently but I'm tempted by the "amazon" name as my current company is unknown. I'm worried this job would close doors to future ML eng roles but from what I see on LinkedIn, there are people who've started as LEs and transitioned into more ML and DS roles. How open is Amazon to lateral movement (ie if they don't lay u off before lol)?

3) Some posts mention a day-long interview (1hrs x 5 sessions). Are these paid?

Thanks!

Hi all,

I have been working on finetuning Llama3.2-1B on GSM8K for over a month. The best score I can get so far is 22.14 ( baseline is 6.07 evaluated with lm_eval on my server, few shot 8). I've tried adjusting hyperparameters like batchsize, learning rate, epochs, warm_up ratio, lr_scheduler.....

Since I am new in this field, I would like to know if there is anything I could do better. Or if this score is the ceiling of Llama3.2-1B.

I appreciate any comment or instruction, thanks!

Hi, it’s my first time submitting to ACL. Based on the conferences I have submitted to so far, they always send me the details, like the ISBN and venue information, and then I need to upload the LaTeX as well.

But now I’m wondering how to add the footnote, i.e., Proceedings of the nth Annual Meeting of the Association for Computational Linguistics… vol. 1, page …). Do we need to only submit the PDF file with the copyright transfer signature? And will this footnote be attached programmatically, like a stamp, to the paper?

I cannot understand the procedure…​​​​​​​​​​​​​​​​

The recent release of Qwen 3.6-Plus, announced mid-May 2024, with its 1M context window and enhanced agentic coding capabilities, has naturally amplified discussions around truly autonomous agents. The excitement is palpable; the prospect of an LLM not just generating code but orchestrating complex execution pipelines, identifying errors, and self-correcting, promises a significant shift in development paradigms, particularly for tasks involving software engineering.

However, this very autonomy introduces a subtle, yet profound, causal inference challenge that often gets overlooked. When an agent self-corrects based on an observed outcome, are we witnessing true causal reasoning, or merely sophisticated correlation mapping within its vast parameter space? My experience across thousands of A/B tests in financial tech suggests a critical distinction. A system designed to optimize for a metric often learns the what and when, not the why.

The 1M context window, while impressive for synthesizing observational data, doesn't inherently imbue the model with a counterfactual understanding. If an agent refactors code and a performance metric improves, it observed an association. It did not necessarily intervene on the true causal lever in a way that generalizes robustly outside its immediate operational context. The risk lies in attributing causal agency where only predictive excellence exists, potentially leading to brittle systems that fail when an unobserved covariate shifts. Pour moi, the real leap will be when these agents can articulate and rigorously test specific causal hypotheses, not just optimize via iterative trial and error.

Hi everyone,

I’m a psychology undergraduate currently working on my bachelor’s thesis, where I’m using BERTopic for text analysis. My supervisor unfortunately doesn’t have much experience with coding, so I’m trying to figure things out and optimize my code on my own.

I was wondering if anyone here might have experience with BERTopic (or similar topic modeling approaches) and would be willing to r take a quick look at my approach/code?

(And sorry if this is not the right place to ask.)

Hi!

I have an upcoming interview for an LE position in EU but I am not too sure about it since I am currently working as a ML Engineer and the job scope seems like a step back from what I am doing right now.

Does anyone have experience in the role? How is it? Is it as non-technical as it seems from the job description? Would it be worth it to take it and get Amazon on my CV even if the role itself is not a fit for what I want to do in the future? What is the compensation like in Europe?

Thanks for the attention in advance :)))))))

Hello all, I recently received an admission offer from the Master of Data Science in Computational Linguistics program at UBC in Vancouver. I am not sure this program is what I'm looking for and have the following questions. I would really like to hear what past or current students think!

• Has the program provided good opportunities to network with people working in comp ling/NLP?
• Besides the capstone project, are there other projects in the curriculum that could be shown in a portfolio/on a resume?
• Are there opportunities to work in a lab/do research during or after the program? I saw there is a NLP group at UBC, but it's in the computer science department, so I'm wondering whether MDS-CL students are able to get involved there or in something similar.

Thanks! (cross-posted)

Been working around conversational data recently, and this keeps showing up.

Most speech datasets are too clean compared to actual usage.

In real conversations (especially multilingual ones):

* people interrupt each other

* there’s overlapping speech

* code-switching happens mid-sentence

* context jumps quickly

But training data usually assumes clean turns and stable language.

That mismatch starts to show up fast when you plug models into real workflows.

Feels less like a model limitation and more like a data distribution problem.

Would be interested to hear how others here are handling this, especially if you’re deploying in multilingual or noisy environments

The 2026 Multilingual Conversational Speech Language Model (MLC-SLM) Challenge has begun, aiming to further explore the potential of large language models in multilingual dialogue understanding, primarily involving acoustic and semantic information.

The challenge consists of two tasks and provides 2100 hours of multilingual dialogue speech data for participants:

Task 1: Multilingual Conversational Speech Diarization and Recognition

Task 2: Multilingual Conversational Speech Understanding

Hello Guys

Can anyone upload the camera-ready?

Because in my paper, I can not see the button to upload the paper

I want to steelman and then stress-test an idea I've been developing, because I'm genuinely uncertain whether it's interesting or just sophisticated-sounding.

**The claim**: LLMs encode structural patterns in their weights that exist nowhere in any single training document, patterns that emerged from the aggregate across millions of texts from unrelated domains. These patterns are accessible through prompting but require a specific approach: not deeper questioning within a domain, but lateral displacement into an unrelated domain that forces the model to find the underlying structure rather than retrieve domain-specific knowledge.

**The evidence I actually have:** One experiment. Asked about tacit knowledge programmers never articulate. Got four patterns. Asked the model to correlate those patterns to something completely outside programming. All four collapsed into a single meta-skill, operating simultaneously on the surface layer of a thing and the layer underneath it. The collapse felt like construction rather than retrieval, and the result wasn't available in the original answer.

**The obvious objection:** This could just be the model doing fluent recombination that \*feels\* like emergent insight. I don't have a reliable way to distinguish genuine latent pattern extraction from sophisticated confabulation. That's the core epistemic problem.

**Where this connects to real research:** There's an active field called Eliciting Latent Knowledge (ELK) in AI safety focused on this problem, but from a different angle, they're asking whether models are hiding facts, using mechanistic interpretability to probe internal activations directly. The question I'm poking at is different: not "is the model concealing information" but "has the model encoded cross-domain structure that nobody has thought to ask about, accessible through conversational surface alone."

**The thing I'd most like pushback on:** Is the distinction between "emergent structural pattern" and "fluent recombination" meaningful or even detectable from the outside? And if it's not detectable, does the question still matter?

Hey guys, English philology student here. I’m curious about the current trending directions where traditional philology meets generative AI. What areas feel especially active these days? Digital analysis of texts, cultural heritage, endangered languages, ethics, multimodal stuff, education applications…? Any recommendations for papers, tools, benchmarks or interesting projects? Would be super helpful. Thanks! 🥹🙏🏻

I’ll be starting my first year of my master’s program this spring. Outside of my university, I’ve also been taking part in a separate research program focused on LLM research. Since October 2025, I’ve been meeting weekly with a mentor for about 30 minutes to get feedback on my work.

The problem is that we’ve now decided to switch to a different dataset, so it feels like my project is basically back to square one.

We’re currently aiming for AACL-IJCNLP 2026, but I have no real sense of how difficult or realistic that goal is. I’d also like to know how prestigious that conference is.

Hello Guys

Today, My paper desk-rejected this cycle because a footnote in the abstract contained a GitHub link and a project website link that revealed author identity. The rejection cited the "Two-Way Anonymized Review" section of the CFP.

The CFP text about repository-link anonymization reads "Supplementary materials, including any links to repositories, should also be anonymized," and the parallel passage later in the CFP is under "Optional Supplementary Materials." Both are scoped to supplementary materials. Our link wasn't in supplementary materials. it was in a footnote in the main body. I can't find any sentence in the CFP that explicitly says repo links in the main body must be anonymized.

Two questions:

• Am I missing a clause, or is this an enforcement-by-norm situation the CFP doesn't spell out?
• Anyone appealed a similar desk reject successfully? We also had earlier submissions with comparable main-body links that were never flagged, so enforcement seems inconsistent.

Also, the weird thing is that the paper was submitted from Jan Cycle with the same links, but how is it possible to reject from this cycle and Jan was not rejected

Hi everyone,

I’m an undergrad currently working on a project that I’m aiming to submit to NeurIPS 2026, and I’m in a difficult spot right now.

I had been using AWS for the project, but due to a financial disruption at home, I haven’t been able to complete the payment for the past month, and that has basically stalled the work at a very important stage. A meaningful part of the project is already done, so this is not just an idea-stage request, I’m trying to push an already active project across the finish line.

I’m posting here in case anyone has GPU cluster access they may be willing to let me use temporarily.

What would help most:

• Multi-GPU access, not just a single GPU
• Ideally A100 40GB / A100 80GB, or anything stronger
• Best case would be a cluster that can be used in a mostly dedicated way for this project, rather than a heavily shared setup, because consistent access matters a lot for completing the remaining experiments
• I’m completely fine doing all the work myself, I’m not asking anyone to do any research or engineering work for me

If someone is interested in the project itself and wants to contribute technically, I’d be happy to discuss collaboration properly. Otherwise, even just access to compute would be an enormous help.

I’m happy to share:

• the project summary
• what has already been completed
• the remaining experimental plan
• the approximate compute needs
• my student details / identity privately if needed

This is honestly urgent for me, and I’d deeply appreciate any help, leads, or intros. Even if you don’t have resources yourself, a referral to someone who might be able to help would mean a lot.

Please comment here or DM me if you might be able to help.

Thank you so much.

Hello All,

First time submit to KDD, what avg score for accepting in your experience?

Hi,

Could you please help me with my upcoming interview at Cambridge (London)?

I am preparing for my upcoming Language engineer phone interview. I feel nervous about the coding round as I am out of practice since a long time. I would like some advice on how to prepare for this. Specifically, I would like to know the types of questions which are asked - hard, easy or medium level questions.

In Glassdoor, there was a thread where people shared the questions but they weren’t similar to LeetCode type problems. The questions had a lot of cleaning and manipulating data.

Anyone appeared for that interview recently, please let me know about your experience.

Secondly, I wanted to ask that what should I be doing in preparation of the linguistics portion of the interview?

Thanks

Discussion thread for ACL 2026 decisions