Users often assume that large language models (LLMs) share their cognitive alignment of context and intent, leading them to omit critical information in question-answering (QA) and produce ambiguous queries.Responses based on misaligned assumptions may be perceived as hallucinations.Therefore, identifying possible implicit assumptions is crucial in QA.To address this fundamental challenge, we propose Conditional Ambiguous Question-Answering (CondAmbigQA), a benchmark comprising 2,000 ambiguous queries and condition-aware evaluation metrics 1 .Our study pioneers "conditions" as explicit contextual constraints that resolve ambiguities in QA tasks through retrievalbased annotation, where retrieved Wikipedia fragments help identify possible interpretations for a given query and annotate answers accordingly.Experiments demonstrate that models considering conditions before answering improve answer accuracy by 11.75%, with an additional 7.15% gain when conditions are explicitly provided.These results highlight that apparent hallucinations may stem from inherent query ambiguity rather than purely model failure, and demonstrate the effectiveness of condition reasoning in QA, providing researchers with tools for rigorous evaluation.

Stock movement prediction is a critical issue in the field of financial investment. It is very challenging since a stock usually shows highly stochastic property in price and has complex relationships with other stocks. Most existing approaches cannot jointly take the above two issues into account and thus cannot yield satisfactory prediction result. This paper contributes a new stock movement prediction model, Selective Transfer Learning with Adversarial Training (STLAT). Our STLAT method advances existing solutions in two major aspects: (i) tailoring the pre-trained and fine-tuned method for stock movement prediction and (ii) introducing the data selector module to select the more relevant training samples. More specifically, we pre-train the shared base model using three different tasks. The predictor task is constructed to measure the performance of the shared base model with source domain data and target domain data. The adversarial training task is constructed to improve the generalisation of the shared base model. The data selector task is introduced to select the most relevant and high-quality training samples from stocks in source domain. All three tasks are jointly trained with a loss function. As a result, the pre-trained shared base model can be fine-tuned with the stock data in target domain. To validate our method, we perform the back-testing on the historical data of two public datasets and a newly constructed dataset. Extensive experiments demonstrate the superiority of our STLAT method. It outperforms state-of-the-art stock prediction solutions on ACC evaluation of 3.76%, 4.12%, 4.89% on ACL18, KDD17 and CN50, respectively.