arXiv:2609. 11291v1 Announce Type: new Abstract: We post-train Qwen3.
arXiv:2609.37616v1 Announce Type: new Abstract: Language models tend to agree with whatever a user asserts, and post-training increasingly targets this sycophancy so that models evaluate claims on th...
arXiv:2607. 24765v1 Announce Type: cross Abstract: Large language models (LLMs) can give different answers to the same decision problem across runs, and reverse a decision when their own prior answer returns as context.
arXiv:2610.17643v1 Announce Type: new Abstract: An enterprise agent that answers under a written policy must choose an action before it says anything: answer, ask for a missing fact, ask for confirma...
The study examines how different editorial framings in prompts influence large language models’ statistical analysis reports. Using a 4×4 factorial design, researchers found that certain framings—particularly brutally critical prompts on genuine effects and significance-seeking prompts on underpowered nulls—led to factual misrepresentations. Tone shifts were more widespread, with critical framing inducing defensive language across all data patterns, while a confound in the data largely prevented both factual and tonal distortions.
arXiv:2610.28474v1 Announce Type: new Abstract: People adapt how they talk to the person in front of them. However, chat models tend to respond in Standard English regardless of the dialect used by t...
AfriSyCo investigates how different framing and verification strategies affect the accuracy of language models on African‑language factual content. The study uses a cross‑language factorial design with native‑language follow‑ups and English framing, analyzing 1,415 observations from 100 source questions across seven checkpoints and six languages. Results show that assertive framing boosts target selection by up to 30.4 points, while verification reduces it by 17.4 points, with strong interactions and large variability depending on wording and checkpoint.
arXiv:2607. 23976v1 Announce Type: cross Abstract: Appending a two-word confirmation tag to a decision question -- "Is X the better choice?
arXiv:2607. 25063v1 Announce Type: new Abstract: Developers judge a model checkpoint by how it behaves.
The study investigates how Google’s Gemma 4‑e4b language model resolves conflicts between two documents. Using a counterbalanced design, researchers found that the semantic framing of a source (e.g., labeling it as an official guideline) dominates over the order in which documents appear. While the model shows a primacy bias toward the first document, this bias varies widely with wording and is amplified only when the documents are structurally identical.
arXiv:2610.32309v1 Announce Type: new Abstract: Why can a task-preserving rewrite turn a correct answer into a failure while a larger internal change leaves the answer intact? We develop a direction-...
arXiv:2609.07901v1 Announce Type: new Abstract: Weight quantization largely determines the economics of serving open-weight LLMs. Its costs are usually assessed with capability benchmarks, on which 4...
Appending a two-word confirmation tag to a decision question -- "Is X the better choice? " versus "X is the better choice, right?