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9,568 papers
A convenient shortcut reads sufficiency off the relevance judgments and treats a context without gold documents as unanswerable, and is released, and recommended scoring refusals against content labels, not the presence of gold.
Human preferences change over time: an established preference may remain valid over time, become uncertain as context shifts, or be explicitly revised. Effective personalization requires conversational AI to adapt its response strategy to evolving preferences rather than relying on passive memory retrieval. We formaliz...
Automated red teaming trains an attacker large language model (LLM) to elicit harmful responses from a victim LLM, but reinforcement learning can collapse attackers onto a narrow set of prompts when attack success is the only reward. Such a collapse of attack diversity can bias safety evaluation toward repeatedly testi...
Fairness benchmarks for large language models (LLMs) are overwhelmingly English-born and American in their social categories; translated versions carry those categories into societies where they do not apply, and almost none fix their hypotheses before the models are run. We present Qistas (Arabic qistas,"the just bala...
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This work introduces Expected Harm, a metric that weights the severity of a jailbreak by its execution likelihood, modeled as a function of execution cost, and demonstrates that this miscalibration creates a vulnerability that increases the attack success rate of existing jailbreaks by up to $2\times.
Heads of Security (HoS), a prompt injection defense that enforces instruction-data separation via a small set of learned per-head attention gates, is introduced and shows that prompt injection robustness can be achieved and improved through targeted attention interventions.
Speculative decoding accelerates autoregressive generation by having a draft propose tokens that a target verifies in parallel. Frontier models increasingly ship a built-in Multi-Token-Prediction (MTP) draft head, assumed to be cheap beside verification. A shipped full-attention MTP head can become disproportionately e...
Large language model (LLM) agents -- LLMs that dynamically interact with an environment over long horizons -- have become an increasingly important area of research, enabling automation in complex tasks involving tool-use, web browsing, and dialogue with people. In the absence of expert demonstrations, training LLM age...
A Latent Reasoning framework in Continuous space (LARC), which alleviates the token-level bottleneck by reasoning over continuous hidden states and achieves state-of-the-art performance on GenEval and demonstrates clear test-time scaling behavior.
Knowledge tracing (KT) predicts a student's next response from their history. New platforms often have question texts but no response logs for training, motivating zero-shot cross-domain KT. Existing transfer and cold-start methods use question semantics, but cross-platform semantic discrepancies limit transferability....
Taxonomy construction is essential for knowledge-intensive NLP yet remains costly: clustering-based methods (i) need careful per-corpus tuning and yield unstable hierarchies, while LLM-based pipelines (ii) depend on expensive task-specific training that does not transfer across domains. We report an empirical finding t...
Whether a language model was trained on a document matters for copyright, privacy, and benchmark contamination. Yet most membership-inference (MI) evaluations on language models collect members and non-members after the fact, so success may reflect distribution shift and failure is inconclusive. We re-ran OLMo 2 1B's o...
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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.
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