The paper investigates how climate disclosure classification performs when the source of the text changes, such as moving from annual reports to press releases or earnings calls. It evaluates three adaptation strategies—definitions, examples, and fine‑tuning—across eleven large language models using two corpora that share the same label space but differ in source. The study finds that while all strategies improve cross‑source performance on average, the most effective in‑source methods (similarity‑based retrieval and LoRA fine‑tuning) lose much of their advantage when the source shifts, whereas simpler approaches like randomly selected few‑shot examples and definition‑based prompts remain more robust, especially when definitions match the granularity of the target text.
arXiv:2609.07798v1 Announce Type: cross Abstract: Natural Language Processing (NLP) in the climate domain requires models to process heterogeneous text sources, including scientific literature, polic...
The paper investigates cross‑lingual transfer for sequential sentence classification (SSC) in research papers, focusing on 13 non‑English languages. Experiments show that linguistic proximity does not reliably predict transfer success, whereas structural similarity in rhetorical organization—particularly label distribution similarity—correlates positively with performance. The authors introduce three generative‑model methods that exploit structural cues, achieving parity with strong encoder baselines on‑domain and outperforming them when transferring to unseen languages.
The paper introduces ATLAS, a likelihood‑based method for model authorship attribution (MAA) that learns how each source family generates text and attributes new text by comparing token‑level likelihoods. It also presents MAABench, a benchmark for evaluating MAA under out‑of‑distribution scenarios such as unseen domains and future model versions. Experiments show ATLAS outperforms existing baselines, achieving 74.27% accuracy on the hardest MAABench setting and correctly identifying teacher models in distillation experiments.
Scientific abstracts mix contributions with background, motivation, and meta-language, so tools that read them as-is cannot separate what a field produces from what it discusses. We present Drift Insp...
Drift Inspector is an open‑source system that extracts Atomic Contribution Claims (ACCs) from scientific abstracts using an LLM, then clusters these claims over time to map how a research field evolves. Applied to six years of EMNLP, the tool reveals a shift from classic NLP tasks toward LLM‑era capabilities such as reasoning and multimodality—trends that keyword or whole‑abstract counts miss. The pipeline has also processed the entire ACL Anthology, yielding 346,000 claims from 80,000 abstracts across 423 venues, with human‑validated extraction and clustering aligned to an external taxonomy.
The paper introduces the Controlled Attribution Benchmark (CATT), a synthetic evaluation framework that fine‑tunes language models on entirely fictitious knowledge to provide clear ground truth for data attribution. By systematically varying single dataset properties—such as document redundancy and lexical distractors—CATT reveals that these factors consistently degrade attribution performance. The study also explores two underexamined design choices: using negative log‑odds gradients instead of cross‑entropy gradients, and aggregating only the top token attribution scores, which together improve ground‑truth recall by 35–72 percentage points and enhance counterfactual retraining performance on FTRACE‑TREX by up to 4.9×.
Researchers investigated how different large language models (LLMs) convert corporate text into empirical variables, focusing on thirteen measures such as sentiment, management clarity, uncertainty, answer specificity, and climate and political risk. Using seven LLMs to score earnings call transcripts of S&P 500 companies, they found low cross-model rank correlations (average 0.52) and that transcript-level differences across providers explained only 34% of total score variation. The study shows that model choice significantly alters downstream inference, with varying coefficient magnitudes, signs, and statistical significance, and that averaging across providers stabilizes rankings but not score levels.
arXiv:2608.18097v2 Announce Type: replace Abstract: We present FrenchNews-7, a cross-publisher France-based French-language news editorial desk classification benchmark combining a large multi-outlet...
arXiv:2608. 03859v1 Announce Type: cross Abstract: Large language models (LLMs) pose challenges to academic integrity and peer review.
arXiv:2607. 14174v1 Announce Type: new Abstract: Financial sentiment extraction has largely relied on news text and supervised extraction against return labels alone, leaving 10-K filings -- and volatility, the target risk disclosure is arguably best suited to informing -- comparatively unexplored.
arXiv:2604. 01904v3 Announce Type: replace-cross Abstract: Post-hoc unauthorized-training data detection for large language models (LLMs) typically assumes a query-with-originals regime: rights holders query a target LLM with raw proprietary data and assess whether the model assigns them stronger memorization-based detection signals, e.
arXiv:2508. 01656v2 Announce Type: replace-cross Abstract: As Large Language Models (LLMs) have reached human-like fluency and coherence, distinguishing machine-generated text (MGT) from human-written content becomes increasingly difficult.