We introduce VoiceBBQ 1 , a spoken extension of the BBQ (Bias Benchmark for Question answering) -a dataset that measures social bias by presenting ambiguous or disambiguated contexts followed by questions that may elicit stereotypical responses.Due to the nature of speech modality, social bias in Spoken Language Models (SLMs) can emerge from two distinct sources: 1) content aspect and 2) acoustic aspect.The dataset converts every BBQ context into controlled voice conditions, enabling per-axis accuracy, bias, and consistency scores that remain comparable to the original text benchmark.Using VoiceBBQ, we evaluate two SLMs-LLaMA-Omni and Qwen2-Audio-and observe architectural contrasts: LLaMA-Omni retains strong acoustic sensitivity, amplifying gender and accent bias, whereas Qwen2-Audio substantially dampens these cues while preserving content fidelity.VoiceBBQ thus provides a compact, dropin testbed for jointly diagnosing content and acoustic bias across spoken language models.

We highlight two significant issues leading to the passivity of current merchant non-player characters (NPCs): pricing and communication. While immersive interactions with active NPCs have been a focus, price negotiations between merchant NPCs and players remain underexplored. First, passive pricing refers to the limited ability of merchants to modify predefined item prices. Second, passive communication means that merchants can only interact with players in a scripted manner. To tackle these issues and create an active merchant NPC, we propose a merchant framework based on large language models (LLMs), called MART, which consists of an appraiser module and a negotiator module. We conducted two experiments to explore various implementation options under different training methods and LLM sizes, considering a range of possible game environments. Our findings indicate that finetuning methods, such as supervised finetuning (SFT) and knowledge distillation (KD), are effective in using smaller LLMs to implement active merchant NPCs. Additionally, we found three irregular cases arising from the responses of LLMs.

In this paper, we propose a neural model EPT-X (Expression-Pointer Transformer with Explanations), which utilizes natural language explanations to solve an algebraic word problem.To enhance the explainability of the encoding process of a neural model, EPT-X adopts the concepts of plausibility and faithfulness which are drawn from math word problem solving strategies by humans.A plausible explanation is one that includes contextual information for the numbers and variables that appear in a given math word problem.A faithful explanation is one that accurately represents the reasoning process behind the model's solution equation.The EPT-X model yields an average baseline performance of 69.59% on our PEN dataset and produces explanations with quality that is comparable to human output.The contribution of this work is two-fold.(1) EPT-X model: An explainable neural model that sets a baseline for algebraic word problem solving task, in terms of model's correctness, plausibility, and faithfulness.(2) New dataset: We release a novel dataset PEN (Problems with Explanations for Numbers), which expands the existing datasets by attaching explanations to each number/variable.

Math word problem solving is an emerging research topic in Natural Language Processing.Recently, to address the math word problem solving task, researchers have applied the encoderdecoder architecture, which is mainly used in machine translation tasks.The state-of-the-art neural models use hand-crafted features and are based on generation methods.In this paper, we propose the GEO (Generation of Equations by utilizing Operators) model that does not use handcrafted features and addresses two issues that are present in existing neural models: 1. missing domain-specific knowledge features and 2. losing encoder-level knowledge.To address missing domain-specific feature issue, we designed two auxiliary tasks: operation group difference prediction and implicit pair prediction.To address losing encoder-level knowledge issue, we added an Operation Feature Feed Forward (OP3F) layer.Experimental results showed that the GEO model outperformed existing state-of-the-art models on two datasets, 85.1% in MAWPS, and 62.5% in DRAW-1K, and reached comparable performance of 82.1% in ALG514 dataset.

Solving algebraic word problems has recently emerged as an important natural language processing task.To solve algebraic word problems, recent studies suggested neural models that generate solution equations by using 'Op (operator/operand)' tokens as a unit of input/output.However, such a neural model suffered two issues: expression fragmentation and operand-context separation.To address each of these two issues, we propose a pure neural model, Expression-Pointer Transformer (EPT), which uses (1) 'Expression' token and (2) operand-context pointers when generating solution equations.The performance of the EPT model is tested on three datasets: ALG514, DRAW-1K, and MAWPS.Compared to the state-of-the-art (SoTA) models, the EPT model achieved a comparable performance accuracy in each of the three datasets; 81.3% on ALG514, 59.5% on DRAW-1K, and 84.5% on MAWPS.The contribution of this paper is two-fold; (1) We propose a pure neural model, EPT, which can address the expression fragmentation and the operandcontext separation.(2) The fully automatic EPT model, which does not use hand-crafted features, yields comparable performance to existing models using hand-crafted features, and achieves better performance than existing pure neural models by at most 40%.

There has been a growing concern over the huge increase in use of smart media by young children. This study explores the possibility of using augmented-reality(AR) for regulat-ing preschoolers' media usage behavior. With MABLE (mobile application for behavioral learning and education), parents can provide AR-assisted feedback by changing facial expressions and sound effects. When overlaying a smart media, which has MABLE running, in front of a QR marker on a puppet, a facial expression is displayed on top of the puppet's face. A two-week long experiment with 36 parent-child pairs showed that compared to using just the puppet, using MABLE showed higher amount of engage-ment among preschoolers. For the effectiveness of parental mediation in terms of self-control, our data showed mixed results. MABLE had positive effects in that the amount of rule-compliance increased and problematic behaviors de-creased, whereas the level of behavioral dependency on smart media was not influenced.

Acquiring simple arithmetic skills at the preschool level requires repetitive practices. One method for encouraging students to spend longer time practicing is by presenting the skills in an engaging game. As student retention on the game increases, the student will be more likely to acquire the practiced skill since she will have spent more time practicing. In this paper, we examine the relationship between internal locus of control and retention in game-based learning applications for young children using Todo Math, a mobile-based math learning application for children from Pre-K to 2nd grade. We examine 345,783 users' log data to show that when children prefer "free" mode, which has high internal locus of control, their retention on Todo Math is higher than children who prefer "daily" mode, which has high external locus of control. We present three analyses that support our findings using survival analysis, post-hoc analysis, and t-test.