Do Language Models Associate Sound with Meaning? A Multimodal Study of Sound Symbolism

A recent study has explored the integration of visual and textual information in Multimodal Large Language Models (MLLMs), revealing that visual-text fusion occurs at specific layers within these models rather than uniformly across the network. The research highlights a late-stage

A recent study has introduced a framework aimed at mitigating hallucination issues in Multimodal Large Language Models (MLLMs) during Reinforcement Learning (RL) optimization. The research identifies key factors contributing to hallucinations, including over-reliance on visual reasoning and insufficient exploration diversity. The proposed framework incorporates modules for caption feedback, diversity-aware sampling, and conflict regularization to enhance model reliability.

The introduction of STAGE (Screenplay Text, Agents, Graphs and Evaluation) marks a significant advancement in the field of narrative understanding, providing a comprehensive benchmark for evaluating knowledge graph construction, scene-level event summarization, long-context screenplay question answering, and in-script character role-playing across 150 films in English and Chinese.

A new approach called MHEL-LLaMo has been introduced for multilingual historical entity linking, utilizing a combination of a Small Language Model (SLM) and a Large Language Model (LLM). This unsupervised ensemble method addresses challenges in processing historical texts, such as linguistic variation and noisy inputs, by leveraging a multilingual bi-encoder for candidate retrieval and an instruction-tuned LLM for predictions.

A recent study emphasizes the importance of data curation in machine translation, particularly for low-resource languages. The research introduces LALITA, a framework designed to optimize the selection of source sentences for creating parallel corpora, focusing on English-Hindi bi-text to enhance machine translation performance.

A recent study introduced OrderProbe, a deterministic benchmark designed to evaluate the structural reconstruction capabilities of large language models (LLMs) using fixed four-character expressions in Chinese, Japanese, and Korean. This benchmark aims to address the challenges of sentence-level restoration from scrambled inputs, which often lack a unique solution.

A recent study analyzed the false refusal behavior of large language models (LLMs) in the context of hate speech detoxification, revealing that these models disproportionately refuse tasks involving higher semantic toxicity and specific target groups, particularly in English datasets.

VocalBench has been introduced as a benchmarking tool to evaluate the conversational abilities of speech interaction models, utilizing approximately 24,000 curated instances in English and Mandarin across four dimensions: semantic quality, acoustic performance, conversational abilities, and robustness. This initiative aims to address the shortcomings of existing evaluations that fail to replicate real-world scenarios and provide comprehensive comparisons of model capabilities.