nlp

An approachable explanation of why transformers need positional encoding, using a bug report analogy and Python's Counter to illustrate how parallel processing loses word order.

A developer shares a postmortem of building a Hindi-English voice agent for fintech, highlighting challenges with number readback, code-mixed TTS, latency under load, and compliance. Key fix was choosing TTS with first-class support for Indian code-mixing and testing at real concurrency.

This paper introduces MameLoshnLM, the first open-source 8B-parameter Yiddish language model, along with the Oytser pretraining corpus and Kashes evaluation benchmark. It demonstrates that continued pretraining on high-quality Yiddish data outperforms general multilingual models, highlighting the value of dedicated low-resource language modeling.

This paper systematically studies perturbation-based continued pre-training (CPT) for improving zero-shot dialect robustness in multilingual LLMs, comparing six training conditions across German, Italian, and Arabic. It finds that character-noised CPT is the most effective general strategy and reveals that different perturbation methods induce distinct robustness mechanisms.

The article investigates whether recurring LLM workloads can be replaced by automatically synthesized deterministic pipelines of typed ML/NLP operators, and asks for feedback on feasibility and approaches.

IslamicTurathBench (ISTB) is a new multi-task, multi-discipline benchmark for evaluating large language models on classical Islamic scholarship, containing 3,465 expert-reviewed questions across 35 works and seven fields.

FinReportBench is an expert-grounded benchmark for measuring and improving institution-grade financial report generation, with 35 observable criteria across deliverability, report identity, and institutional completeness. It curates 244 bilingual tasks, evaluates nine model families, and uses benchmark-guided skill distillation to improve generation and self-review across five model families.

This paper investigates computational pun translation as a process of discovery, exploration, and selection, using graph-based affordance retrieval and multi-evaluator ranking. The authors show that successful pun translation relies on finding new sound-meaning collisions in the target language rather than preserving source words.

This paper proposes a conceptual framework for building an electronic explanatory dictionary of the Tajik language using large language models, integrating morphological analysis, lemmatization, semantic clustering, and dictionary entry generation with PEFT strategies.

The paper proposes MAP-PO, a multi-agent framework that clusters annotators by labeling behavior and fine-tunes separate LLM agents per cluster using preference optimization, preserving disagreement in sexism detection tasks. Experiments on the EXIST 2024 dataset show that cluster-specific training is necessary and that a shared team-level reward keeps agents calibrated.

A GitHub project that measures how language models shift their judgment based on narrative framing, quantifying sycophancy across opposite narrators.

This paper formalizes multi-model human evaluation as a best-arm identification problem in a multi-armed bandit setup, adaptively allocating annotation effort to focus on competitive models and improve ranking discrimination.

This paper proposes HomoEnsNER, a homogeneous ensemble of five GujaratiBERT models for Gujarati named entity recognition, and shows it outperforms heterogeneous alternatives that rely on architectural diversity, achieving state-of-the-art F1 on the Naamapadam test split.

TabletCraft is an open-source system enabling bidirectional Akkadian-English neural machine translation with cuneiform rendering, allowing users to both read ancient tablets and compose new messages in cuneiform. Accepted to the C3NLP workshop at ACL 2026, it reports first published quantitative results for English-to-Akkadian translation.

This paper proposes Archive, a framework for ambiguity detection in open-domain QA that distinguishes ambiguity from answer diversity using logical conflict, and introduces QuireQA, a 4,703-query benchmark. Experiments show Archive improves F1 by up to 21.6% while being 16x faster than competitors.

A tweet argues that masked language modeling was unnecessary and that autoregressive models would have sufficed, with a nod to BERT.

This paper proposes Pruned BPE, a post-training method that prunes low-exposure tokens from a BPE vocabulary and reallocates slots to better-exposed candidates, reducing encoded length without increasing model-visible vocabulary size. Experiments on English and Chinese corpora show approximately 0.27–0.36% encoded length reduction over standard BPE.

The paper proposes QQ, a framework that leverages the intrinsic duality between multi-hop question generation and question answering via bidirectional alignment constraints and contrastive learning, improving question quality on HotpotQA and MuSiQue.

AttnLink is a research paper presenting an attention-based framework for schema linking in Text-to-SQL, converting LLM internal attention into continuous relevance scores for schema items. Experiments on Spider, BIRD, and Spider2-SQLite show high mAP scores and millisecond-scale latency.

This paper introduces SANE, a lightweight plugin for retrieval-augmented generation that improves both retrieval and reading by selecting top candidates via synopses and performing blueprint-guided query-time evidence extraction.