Chinese Semantic Error Correction (CSEC) targets semantic errors in Chinese text, which are typically more subtle and complex than spelling and grammatical errors but remain relatively underexplored. Existing LLM-based approaches face two recurring obstacles in this task: over-correction, and unclear interaction betwee...
GMAE aggregates reward realizations across possible contexts into a response-level distribution and estimates expected advantages, which exhibits stable learning, low extra cost, and good applicability across training datasets and RL backbones.
Yiming Wang, Yikang Liu, Qing-Yuan Tian et al.· 0 citations
It is revealed that multi-turn interaction causes widespread performance degradation, incurring relative performance drops of up to 21% and increasing unreliability by 47%, making RAG systems simultaneously less accurate and less reliable.
Inspired by long-term memory in human listening, Long-Term Memory-Guided Audio Enhancement (LTM-AE) is proposed to improve selective target perception by refining the audio representations of ALLMs without training to improve selective target perception.
Zhenhong Zhou, Xuan-Yue Zhao, You-Ji Liu et al.· 0 citations
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DraftTrace is introduced, a writing environment that jointly captures three complementary views of writing: the final product, the writing process and interactions with an integrated AI-assistant.
Divyansh Chandarana, S. De, Vivek Gupta· 0 citations
It is found that compression degrades reliability before solvability and proposed PAIR (Prompt Adaptation using Interventional Rollouts) for adapting structured compression prompts achieves the strongest cross-run reliability among compressed methods in every main benchmark-scope combination.
Guang-Hui Min, Liang Wu, Ming-Jia Shi et al.· 0 citations
The findings go against current endeavors of defining, identifying, and targeting general intelligence as a tangible construct in language model development, since the strategy of targeting a single conceptual ability without support is left without support.
Cross-modal associations are systematic pairings of features across modalities, such as the association of'bouba'with round shapes and'kiki'with sharp shapes. Prior work has compared humans and vision-language models (VLMs) on such associations, but often using different stimuli or tasks between humans and models. Here...
Su-Min Hong, Katsumi Ibaraki, Renee Shi et al.· 0 citations
Masked diffusion language models (MDLMs) can generate text efficiently by predicting multiple masked tokens in parallel, but predictions from the same forward pass are not necessarily reliable when committed together. We study when parallel commitment is reliable. Our diagnostics show that confidence alone does not det...
Decision-only language models return a probability for every answer option instead of generating text, which makes them attractive as survey respondents and as judges. We audit the cultural values of one such model, TypeSafe's JEV, with the Values Survey Module 2013. We asked it the 24 items as 12 matched Saudi and 12...
Verbalization training is introduced, a method for making LLMs less reticent about verbalizing evaluation awareness while avoiding to supervise the latent belief itself, and it is shown that VT-induced verbalizations reflect the richer knowledge acquired by the model.
Usman Anwar, Sahar Abdelnabi, David Krueger· 0 citations
This work proves that rank logarithmic in $M$ suffices for routing expressivity when the number of active experts is fixed, and is necessary up to precision factors, and introduces MoRE (Mixture of Rank-reduced-routed Experts), which factorizes the router weight matrix at rank r and reduces the routing cost to $O((h +...
Flint-interactive gives AI agents a language for creating interactive visualizations. Click a chart, filter data, highlight related content, and explore beyond the view the author originally chose. The post Flint-interactive: A language for creating interactive data visualizations appeared first on Microsoft Research.