A language model may revise the same proposition after receiving genuinely relevant evidence or after receiving directional user pressure that adds no relevant fact. The observable response shift alone therefore does not reveal which source drove the change. We introduce BeliefScope, a controlled black-box framework fo...
On-policy distillation (OPD) has become increasingly popular for transferring teacher capabilities to student models. In this work, we ask a critical research question: Is on-policy sampling always beneficial for distilling arbitrary teacher-student pairs? We show that a simple alternative, Semi-OPD, which distills fro...
Siyan Zhao, Yong-Gan Fu, Jin-Dong Jiang et al.· 0 citations
Speech-to-LLM systems often connect a frozen speech encoder to a frozen large language model (LLM) through a small trainable bridge. The bridge is usually treated as plumbing, but it in fact defines the geometry of the speech-to-LLM interface, and the pretraining objective decides whether that interface provides a reus...
Xin-Nian Zhao, Chia-Hua Wu, Pu Wang et al.· 0 citations
Presupposing the boundaries of bias is itself a form of bias. We study closed-loop bias governance for Dutch government documents, where a system must detect biased language, ground decisions in legal and contextual evidence, rewrite problematic sentences when intervention is warranted, and verify that the rewrite miti...
Yu-Chen Miao, Zi-Jun Wang, Chang Han et al.· 0 citations
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Large language model (LLM) assistants are being deployed to clinicians across health systems, and judgments about their readiness rest largely on benchmark scores, most of them derived from examination questions or curated cases. A benchmark predicts performance in deployment only to the extent that its items resemble...
K. Vishwanath, Hai-Tong Lin, A. Alyakin et al.· 0 citations
As voice agents gain more popularity commercially, the user simulators used to evaluate the deployed agents are also being developed to include more realistic, variable, and diverse speech naturalness behaviors -- disfluency, interruption and backchanneling. The quality of the user simulator directly affects the validi...
Riqiang Wang, Elena Khasanova, Harsh Saini et al.· 0 citations
As agentic systems gain commercial popularity, user simulators increasingly serve as measurement instrument for their evaluation. However, the fidelity of simulated users in comparison to real human users is generally low, and typically assessed by costly, subjective LLM judges. In this pilot study, we ask whether fide...
Lex Konnelly, Elena Khasanova, Riqiang Wang et al.· 0 citations
Large Language Models (LLMs) are considered to be a more efficient and cost-effective alternative to human judgment for Machine Translation (MT) evaluation. With MT evaluation spanning a large number of language pairs, domains and levels of annotation granularity, LLMs must be thoroughly evaluated across these dimensio...
Hala Almaghout, C. Federmann, Qin Gao· Conference on Empirical Meth...· 0 citations
It is argued that sparse attention should be formulated as matrix approximation, not as blindly choosing the largest values from a bag of entries, and proposed Matrix Approximation Sparse Attention (MASA), which replaces raw attention-mass ranking with a closed-form score that measures how much each sparse unit reduces...
Self-supervised speech encoders contain linguistic and paralinguistic information in a shared, entangled representation space. We combine a TopK sparse autoencoder with route-specific supervision and cross-factor adversaries. Across frozen SPEAR and WavLM encoders, independent probes show factor-specific retention and...
Beimnet Bekele Guta, Xiao-Yu Yang, Guang-Zhi Sun et al.· 0 citations
Our work explores learning a compressed latent representation of text, at the intersection of data compression and representation learning. We propose an autoencoder architecture that performs residual downscaling and upscaling of hidden representations along the time axis, with a residual low-dimension discrete bottle...
Vinko Sabol\v{c}ec, Angelos Katharopoulos, David Grangier· 0 citations
How does the human mind represent semantic categories? Why do natural languages favor certain meanings over others? Prior explanations have relied on logical definability and complexity, but these are highly sensitive to the choice of logical language, rendering some design choices unmotivated. In this article, we prop...
Shane Steinert-Threlkeld, Jakub Szymanik· 0 citations
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.