We’ve fine-tuned GPT-3 to more accurately answer open-ended questions using a text-based web browser.
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We’re releasing gpt-oss-120b and gpt-oss-20b—two state-of-the-art open-weight language models that deliver strong real-world performance at low cost. Available under the flexible Apache 2.
A factuality benchmark called SimpleQA that measures the ability for language models to answer short, fact-seeking questions.
arXiv:2504.11582v3 Announce Type: replace Abstract: How can a monolingual English speaker determine whether an automatic translation in French is good enough to be shared? Existing MT error detection...
arXiv:2601.21225v4 Announce Type: replace-cross Abstract: Large language models have made substantial progress in mathematical reasoning. However, benchmark development for multilingual evaluation ha...
The paper investigates whether language models can reason across languages by introducing a two‑hop question answering task that requires inference over two multilingual documents. Results show that models are more sensitive to language variation in answer‑span documents than in bridging documents, and that up to 33% of multilingual cases involve correct final answers despite failing to infer bridging information in the first step. The study also reveals an 18% composition failure rate and proposes a three‑stage SUBQ prompting method that improves accuracy from 10.1% to 66.5%.
Mathematical Word Problems (MWPs) are an important benchmark for evaluating natural language understanding and quantitative reasoning. Despite recent progress in high resource languages, Bengali remains underexplored due to the limited availability of large scale annotated datasets.