It is shown that, under a sufficiently large but practical candidate pool, embedding recall exhibits diminishing returns, motivating a closer examination of reranker quality, and support evaluating the retrieval and reranking stages separately when selecting an optimization target for industrial retrieval systems.
Yun-Han Li, Ming-Jie Xie, Ze-Yang Shi et al.· 0 citations
Where does the novelty a base language model produces with no task come from, and what can an LLM judge of a long stream actually see? We dismantle a cognitively inspired generation loop over 24 conditions on three base models. Most of its effect lives in one operation: a new subject injected every few hundred tokens (...
This work introduces Mimir, a 1.78B-parameter language model based on the Hierarchical Reasoning Model Text (HRM-Text) architecture, a recurrent-depth transformer whose layers are split into a low-level and high-level stack that are applied repeatedly in an interleaved schedule.
Peter Schneider-Kamp, Jacob Nielsen, Gianluca Barmina et al.· 1 citation
We introduce GRAB, a constructor-encoder-bridge pipeline for table question answering. Our method lifts relational data into an heterogeneous graph, encodes it via message passing, and transfers the signals to an LLM through a small set of query-conditioned latent tokens. This provides the LLM with a compact, task-rele...
Simone Varriale, Tamara Cucumides, Floris Geerts et al.· 0 citations
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Benchmark scores often misrepresent a large language model's (LLM's) knowledge, because they rely, e.g., on the model's ability to follow specific formatting requirements. This especially penalizes base models that may know the correct answers but lack the ability -- typically introduced in post-training -- to structur...
Selen Erkan, Bastian Boll, Kristian Kersting et al.· 0 citations
Diffusion language models (DLMs) have emerged as the leading non-autoregressive alternative to language modeling. Because their likelihoods are intractable, generative perplexity (gen-PPL) has become the de facto standard for reporting unconditional generation quality. We analyze why gen-PPL is unreliable as a measure...
Multilingual speech foundation models claim broad language coverage, but how well they handle spontaneous African speech, and how best to adapt them, remains unclear. We benchmark zero-shot and fine-tuned speech recognition on the WAXAL corpus of spontaneous speech in 19 African languages, covering Whisper, MMS, Omnili...
Victor Tolulope Olufemi, Oreoluwa Babatunde, Ramsey Njema et al.· 0 citations
We introduce CARTE (Culturally Anchored Regional-Territorial Evaluation), a multiple-choice benchmark for evaluating the ability of large language models (LLMs) to discriminate fine grained, geographically grounded and regionally differentiated knowledge within France. While prior benchmarks focus on national-level cul...
Sarah Almeida Carneiro (X), Christos Xypolopoulos (X, NTUA) et al.· 0 citations
An activation oracle is a language model trained to convert the activations of another model into natural language. However, it is so far unclear to what extent these verbalizations are calibrated. Crucially, the oracle's verbalization come without any measure of confidence. Here, we compare five methods for estimating...
Federico Torrielli, Peter Schneider-Kamp, Lukas Galke Poech· 0 citations
As large language model (LLM)-based agents increasingly participate in online discourse, red-teaming their capacity to support political influence campaigns is critical for information integrity. In pursuit of this goal, we focus on locally deployable open-source LLMs under an assumed threat model in which an operator...
Daniel C. Ruiz, Anna Serbina, Ashwin Rao et al.· 0 citations
Automatic speech recognition (ASR) systems are typically optimized for verbatim transcription, which preserves disfluencies, filler words, and informal spoken structures that are often unsuitable for downstream writing-oriented applications. A common workaround is a two-stage ASR+LLM pipeline for post-editing, but this...
Wanyi Ning, Yinshang Guo, Haitao Qian et al.· 0 citations
The Industry Foundation Classes (IFC) standard is central to building data exchange across the lifecycle, from design and construction to facility management and Digital Twin integration. In operational settings, stakeholders increasingly require access to building information without specialist knowledge of IFC's comp...
Rabindra Lamsal, Sisi Zlatanova, Haowen Xu et al.· 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.