Clinical notes capture most of what is documented about a patient's care, but they cannot be used for research until protected health information (PHI) is removed. De-identification is often treated as a detection problem. Detection alone is not sufficient: redaction strips clinical content along with identifiers, date...
Jose D. Posada, Somalee Datta, Priya Desai· 0 citations
This paper identifies mechanistic signatures of faithful self-report in a controlled setting and proposes that it is possible to distinguish between the two patterns of computation by examining the structure of the networks themselves.
David Atkinson, Dillon Plunkett, David Bau· 0 citations
JudgeMoE, a lightweight aggregator that assigns example-specific weights to cached judge score distributions and fuses them before computing a final score is introduced, showing that score-range choice is unstable across judge--dataset settings and that soft scoring usually outperforms hard decoding.
Yiqi Liu, Joseph James, Yang Wang et al.· 0 citations
Small language models are inexpensive to serve and can run on private infrastructure, but base models are often not good enough at multi-turn tool calling, and fine-tuning them needs per-API data that rarely exists. Existing synthesis methods are too expensive for high-scale fine-tuning, as they often require mock oper...
Aaron Fainman, Gabriela Kadlecová, Maciej Gryka et al.· 0 citations
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Rotary Position Embedding (RoPE) encodes token positions by rotating each two-dimensional channel of the query and key vectors at a channel-specific frequency, making the attention logits invariant to a common shift of positions. However, this rotation is periodic, and it leads to position aliasing where relative posit...
Guan-Cheng Du, Luo-Tian Huang, Shaowen Wang et al.· 0 citations
This work measures how much of that evidence a small language model can recover when it is post-trained on the verdicts alone, with no human evidence labels at any stage, to improve the evidence without anyone annotating it.
Nishanth Nayakanti, Prasang Gupta, Ashutosh Bilthare et al.· 0 citations
Large speech language models have demonstrated strong capabilities in unified cross-modal understanding and generation, yet paralinguistic cues, especially emotion, remain difficult to preserve. Existing systems typically rely on entangled acoustic representations, which allow the underlying language model to depend ex...
Continual adaptation of language models can change their output distribution on prompts learned earlier, while retaining every old prompt-answer pair may be undesirable or impossible. We study condition-anchored generative distillation (CAGD): retain a small set of old prompts, use a frozen previous model to reconstruc...
Retrieval-augmented generation grounds language models in external context, but for long documents flat top-$k$ retrieval can cluster on a single region and miss complementary evidence. RAPTOR-style summary trees address this by recursively clustering chunks and using a language model to summarize each cluster at index...
Localizing latent structures in the activation space of language models (LMs) is central to understanding and controlling their behavior. Yet, localized structures can differ substantially in their causal influence, raising the question of what makes a structure actionable. We tackle this question by casting causal inf...
We propose Token-Level Off-Policy Labeling (TOPL), an off-policy training paradigm that reframes post-training as a token-level correctness prediction task. Our key intuition is that by training the model to distinguish good and bad tokens in a response, we naturally guide the model towards generating good tokens, whil...
Zitong Huang, Gustavo Lucas Carvalho, Deqing Fu et al.· 0 citations
Reasoning-trained language models can perform, zero-shot, multi-label tasks that require selecting a small set of relevant labels from a universe of thousands to hundreds of thousands of candidates. We ask how they do it mechanistically, and whether the mechanism can be distilled. We make the question measurable by tre...
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.