Large language models (LLMs) have unlocked a wide range of downstream generative applications. However, we found that they also risk perpetuating subtle fairness issues tied to culture, positioning their generations from the perspectives of the mainstream US culture while demonstrating salient externality towards non-mainstream ones. In this work, we identify and systematically investigate this novel culture positioning bias, in which an LLM's default generative stance aligns with a mainstream view and treats other cultures as outsiders. We propose the CultureLens benchmark with 4000 generation prompts and 3 evaluation metrics for quantifying this bias through the lens of a culturally situated interview script generation task, in which an LLM is positioned as an onsite reporter interviewing local people across 10 diverse cultures. Empirical evaluation on 5 state-of-the-art LLMs reveals a stark pattern: while models adopt insider tones in over 88 percent of US-contexted scripts on average, they disproportionately adopt mainly outsider stances for less dominant cultures. To resolve these biases, we propose 2 inference-time mitigation methods: a baseline prompt-based Fairness Intervention Pillars (FIP) method, and a structured Mitigation via Fairness Agents (MFA) framework consisting of 2 pipelines: (1) MFA-SA (Single-Agent) introduces a self-reflection and rewriting loop based on fairness guidelines. (2) MFA-MA (Multi-Agent) structures the process into a hierarchy of specialized agents: a Planner Agent(initial script generation), a Critique Agent (evaluates initial script against fairness pillars), and a Refinement Agent (incorporates feedback to produce a polished, unbiased script). Empirical results showcase the effectiveness of agent-based methods as a promising direction for mitigating biases in generative LLMs.

We present an advanced approach to mobile app review analysis aimed at addressing limitations inherent in traditional star-rating systems. Star ratings, although intuitive and popular among users, often fail to capture the nuanced feedback present in detailed review texts. Traditional NLP techniques -- such as lexicon-based methods and classical machine learning classifiers -- struggle to interpret contextual nuances, domain-specific terminology, and subtle linguistic features like sarcasm. To overcome these limitations, we propose a modular framework leveraging large language models (LLMs) enhanced by structured prompting techniques. Our method quantifies discrepancies between numerical ratings and textual sentiment, extracts detailed, feature-level insights, and supports interactive exploration of reviews through retrieval-augmented conversational question answering (RAG-QA). Comprehensive experiments conducted on three diverse datasets (AWARE, Google Play, and Spotify) demonstrate that our LLM-driven approach significantly surpasses baseline methods, yielding improved accuracy, robustness, and actionable insights in challenging and context-rich review scenarios.

Recent advances in large language models (LLMs) enable effective in-context learning (ICL) with many-shot examples, but at the cost of high computational demand due to longer input tokens. To address this, we propose cheat-sheet ICL, which distills the information from many-shot ICL into a concise textual summary (cheat sheet) used as the context at inference time. Experiments on challenging reasoning tasks show that cheat-sheet ICL achieves comparable or better performance than many-shot ICL with far fewer tokens, and matches retrieval-based ICL without requiring test-time retrieval. These findings demonstrate that cheat-sheet ICL is a practical alternative for leveraging LLMs in downstream tasks.

VTubers, digital personas represented by animated avatars, have gained massive popularity. Traditionally, VTubers are operated and voiced by human controllers known as Nakanohito. The reliance on Nakanohito, however, poses risks due to potential personal controversies and operational disruptions. The emergence of AI-driven VTubers offers a new model free from these human constraints. While AI-driven VTubers present benefits such as continuous operation and reduced scandal risk, they also raise questions about authenticity and audience engagement. Therefore, to gain deeper insights, we conduct a case study, investigating viewer perceptions of Neuro-sama, the most popular AI-driven VTuber with 845k followers on Twitch and 753k followers on YouTube. We analyze 108k Reddit posts and 136k YouTube comments, aiming to better understand viewer motivations, how AI constructs the virtual persona, and perceptions of the AI as Nakanohito. Our findings enhance the understanding of AI-driven VTubers and their impact on digital streaming culture.

Personalized review generation helps businesses understand user preferences, yet most existing approaches assume extensive review histories of the target user or require additional model training. Real-world applications often face few-shot and training-free situations, where only a few user reviews are available and fine-tuning is infeasible. It is well known that large language models (LLMs) can address such low-resource settings, but their effectiveness depends on prompt engineering. In this paper, we propose Conversational Prompting, a lightweight method that reformulates user reviews as multi-turn conversations. Its simple variant, Simple Conversational Prompting (SCP), relies solely on the user's own reviews, while the contrastive variant, Contrastive Conversational Prompting (CCP), inserts reviews from other users or LLMs as incorrect replies and then asks the model to correct them, encouraging the model to produce text in the user's style. Experiments on eight product domains and five LLMs showed that the conventional non-conversational prompt often produced reviews similar to those written by random users, based on text-based metrics such as ROUGE-L and BERTScore, and application-oriented tasks like user identity matching and sentiment analysis. In contrast, both SCP and CCP produced reviews much closer to those of the target user, even when each user had only two reviews. CCP brings further improvements when high-quality negative examples are available, whereas SCP remains competitive when such data cannot be collected. These results suggest that conversational prompting offers a practical solution for review generation under few-shot and training-free constraints.

We present a live demonstration for RAVEN, a real-time audio-visual speech enhancement system designed to run entirely on a CPU. In single-channel, audio-only settings, speech enhancement is traditionally approached as the task of extracting clean speech from environmental noise. More recent work has explored the use of visual cues, such as lip movements, to improve robustness, particularly in the presence of interfering speakers. However, to our knowledge, no prior work has demonstrated an interactive system for real-time audio-visual speech enhancement operating on CPU hardware. RAVEN fills this gap by using pretrained visual embeddings from an audio-visual speech recognition model to encode lip movement information. The system generalizes across environmental noise, interfering speakers, transient sounds, and even singing voices. In this demonstration, attendees will be able to experience live audio-visual target speech enhancement using a microphone and webcam setup, with clean speech playback through headphones.

ABSTRACT Audio Large Language Models (Audio LLMs) enable human-like conversation about music, yet it is unclear if they are truly listening to the audio or just using textual reasoning, as recent benchmarks suggest. This paper investigates this issue by quantifying the contribution of each modality to a model's output. We adapt the MM-SHAP framework, a performance-agnostic score based on Shapley values that quantifies the relative contribution of each modality to a model's prediction. We evaluate two models on the MuChoMusic benchmark and find that the model with higher accuracy relies more on text to answer questions, but further inspection shows that even if the overall audio contribution is low, models can successfully localize key sound events, suggesting that audio is not entirely ignored. Our study is the first application of MM-SHAP to Audio LLMs and we hope it will serve as a foundational step for future research in explainable AI and audio.

Diplomatic events consistently prompt widespread public discussion and debate. Public sentiment plays a critical role in diplomacy, as a good sentiment provides vital support for policy implementation, helps resolve international issues, and shapes a nation's international image. Traditional methods for gauging public sentiment, such as large-scale surveys or manual content analysis of media, are typically time-consuming, labor-intensive, and lack the capacity for forward-looking analysis. We propose a novel framework that identifies specific modifications for diplomatic event narratives to shift public sentiment from negative to neutral or positive. First, we train a language model to predict public reaction towards diplomatic events. To this end, we construct a dataset comprising descriptions of diplomatic events and their associated public discussions. Second, guided by communication theories and in collaboration with domain experts, we predetermined several textual features for modification, ensuring that any alterations changed the event's narrative framing while preserving its core facts.We develop a counterfactual generation algorithm that employs a large language model to systematically produce modified versions of an original text. The results show that this framework successfully shifted public sentiment to a more favorable state with a 70\% success rate. This framework can therefore serve as a practical tool for diplomats, policymakers, and communication specialists, offering data-driven insights on how to frame diplomatic initiatives or report on events to foster a more desirable public sentiment.

Amazon agrees to record $2.5b settlement over claims it tricked customers The five'worst cities to be a woman' in America... and they're all in deep red states D4vd's mansion is mysteriously EMPTIED by movers... as disturbing theory emerges about why he still hasn't been arrested after dismembered girl was found in his Tesla There's a sinister Establishment'plot' to undermine Prince William and Kate and bring back Harry and Meghan. I refuse to be part of it... and today I'm exposing what's going on: RICHARD EDEN Pastor and wife were living WITH their son who'kept four people PRISONER in basement dungeon'... as survivor's family reveal what'evil monster' did to victims Las Vegas man raped by mother as a child is told by judge that he's legal father to brother believed to be his biological son Why your star sign has been WRONG your whole life: As astronomers reveal the zodiac is more than 2,500 years out of date, use our graphic to find out what your horoscope really is - and why it's changed I woke up one day with tinnitus. It ruined my life... but this is how I got rid of the agony for good - and why I believe we are treating this insidious condition completely wrong: Expert and audiologist DR GLADYS SANDA Star Trek icon William Shatner, 94, jokes about his DEATH as he breaks silence after'medical emergency' Jessica Alba is being humiliated by her ex-husband chasing lookalikes half her age! How I look like this at 45, by DAVID GANDY: His 45-minute routine, exactly what he eats, the cheap supplement he takes every day, what makes a man... and how to find the perfect pair of pants Livvy Dunne leaves fans in disbelief as'gross' photo goes viral She shows off huge new'Queen' ring with gem'associated with the goddess Diana' Megyn Kelly's brutal takedown of left-wing student who said Trump rhetoric was to blame for Charlie Kirk murder Top journalist slams entitlement of Ben Affleck and Jennifer Gardner's woke daughter after UN speech demanding mask mandates The threat of artificial intelligence wiping us out is so grave governments must bomb any labs suspected of developing it, by two top academics who've studied the threat for 25 years... and spell out in terrifying detail our bloody end This is the sad truth about people like Violet Affleck who are still wearing masks. I've seen it in so many patients at my practice... and this is what's really going on: DR MAX PEMBERTON The Christian pastor and others who falsely predicted the end of the world on September 23 are speaking out after nothing happened.

Las Vegas man raped by mother as a child is told by judge that he's legal father to brother believed to be his biological son The five'worst cities to be a woman' in America... and they're all in deep red states D4vd's mansion is mysteriously EMPTIED by movers... as disturbing theory emerges about why he still hasn't been arrested after dismembered girl was found in his Tesla There's a sinister Establishment'plot' to undermine Prince William and Kate and bring back Harry and Meghan. I refuse to be part of it... and today I'm exposing what's going on: RICHARD EDEN Pastor and wife were living WITH their son who'kept four people PRISONER in basement dungeon'... as survivor's family reveal what'evil monster' did to victims Why your star sign has been WRONG your whole life: As astronomers reveal the zodiac is more than 2,500 years out of date, use our graphic to find out what your horoscope really is - and why it's changed I woke up one day with tinnitus. It ruined my life... but this is how I got rid of the agony for good - and why I believe we are treating this insidious condition completely wrong: Expert and audiologist DR GLADYS SANDA Star Trek icon William Shatner, 94, jokes about his DEATH as he breaks silence after'medical emergency' Amazon agrees to record $2.5b settlement over claims it tricked customers Jessica Alba is being humiliated by her ex-husband chasing lookalikes half her age! How I look like this at 45, by DAVID GANDY: His 45-minute routine, exactly what he eats, the cheap supplement he takes every day, what makes a man... and how to find the perfect pair of pants Livvy Dunne leaves fans in disbelief as'gross' photo goes viral She shows off huge new'Queen' ring with gem'associated with the goddess Diana' Megyn Kelly's brutal takedown of left-wing student who said Trump rhetoric was to blame for Charlie Kirk murder Top journalist slams entitlement of Ben Affleck and Jennifer Gardner's woke daughter after UN speech demanding mask mandates The threat of artificial intelligence wiping us out is so grave governments must bomb any labs suspected of developing it, by two top academics who've studied the threat for 25 years... and spell out in terrifying detail our bloody end This is the sad truth about people like Violet Affleck who are still wearing masks. I've seen it in so many patients at my practice... and this is what's really going on: DR MAX PEMBERTON It's time to rewrite the family tree, as scientists have revealed that our species is even older than we thought.