Jev moderation, stance and sentiment evaluation

Read language-task results through policy labels, retrieval conditions, conversational context, extra inference and supervised postprocessing.

Short answer: A strong language-task benchmark score does not by itself establish policy adherence or transfer to another language. Compare annotation rules, input context, candidate menus, supervision and inference budgets before reusing the result.

Five fixed-version abstracts, selected methods/results/limitations and one implementation README were inspected. Findings remain author-reported; no prediction records or experiments were reproduced.

Policy adherence or label agreement?

HATEDECIDE finds that supplying definitions and decomposing a moderation decision do not reliably improve agreement with existing dataset labels. A prediction can follow the supplied definition yet disagree with the original annotation policy. Component answers make the aggregation rule inspectable, but they are not verified explanations without component-level reference labels. Evaluate the actual policy and both false-positive and false-negative costs; English diagnostic examples do not establish everyday or multilingual moderation performance. HATEDECIDE: limitations, v1

What information does retrieval add?

The online-moderation study separates written rules, labeled precedents and restrictions on the answer menu. Its main policy contrast adds precedents while keeping the complete menu fixed. For OLID and HateXplain, however, precedents come from the cleaned evaluation population with the target excluded; this differs from using only a frozen training collection. Media inputs are generated textual captions, not a test of native Jev vision. Report these information sources and candidate restrictions alongside the score. Information conditions, v1 · Tasks and media representations

What changes across languages?

The stance study compares English text–target pairs with Chinese speaker-tagged conversations. Language, dataset and conversational structure therefore change together. Jev has more difficulty with support/opposition in the conversational benchmark, but this does not isolate a language effect or prove an internal mechanism. Some LLMs also receive batches while Jev receives single records. Our suggested transfer test holds task structure and information access fixed and checks target-language annotation definitions independently. Stance evaluation setup, v1 · Conversation-length analysis

Do more questions improve the result?

Emo-Jev compares direct classification with atomic-question composition and multiple predefined judgment paths. Both extensions have lower average macro-F1 than direct Jev in the reported eight-dataset setup, with task-specific gains and losses. Extra paths add dependent stages and cost; some retry usage is missing. HATEDECIDE likewise finds no consistent benefit from decomposition. Compare the direct baseline, the full added-question budget and held-out outcomes rather than assuming that more intermediate answers provide better reasoning or calibrated probabilities. Emo-Jev results, v1 · Efficiency accounting · HATEDECIDE decomposition

What does a frozen backbone still learn?

The DimABSA system uses frozen Jev with 488 coefficients fitted to task labels for regression and extraction. Its strong regression aggregate relies on supervised calibration; the competition ranks individual corpora, not the reconstructed pooled aggregate. Development scoring does not nest the full feature/threshold selection process, and some text overlap remains. The public code includes fitted parameters and metric summaries, while text-bearing prediction caches are withheld. Separate backbone tuning, supervised postprocessing, selection and artifact availability when comparing results. DimABSA protocol, v1 · Implementation and release limits