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Sara Vera Marjanovic

@saravera.bsky.social
68 followers 38 following 16 posts

PhD fellow in XAI, IR & NLP ✈️ Mila - Quebec AI Institute | University of Copenhagen 🏰 #NLProc #ML #XAI Recreational sufferer

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Sara Vera Marjanovic @saravera.bsky.social · 21/09/2026
Does adding more models always make a multi-agent system better? 🤔 After all, extra diversity should mean better performance? Our new paper, “Mo’ Models, Mo’ Problems: How to best select model pools when designing Multi-Agent Systems,” challenges this assumption.
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Sara Vera Marjanovic @saravera.bsky.social · 15/01/2026
🚨Thoughtology is now accepted to #TMLR! We've added some new analyses, most notably: 🌟 We quantify rumination; repetitive thoughts are associated with incorrect responses 🌟 We add 2 LRMs: gpt-oss and Qwen3. Both show a reasoning 'sweet spot' See 📃 : openreview.net/forum?id=BZw...
openreview.net
DeepSeek-R1 Thoughtology: Let’s think about LLM reasoning
Large Reasoning Models like DeepSeek-R1 mark a fundamental shift in how LLMs approach complex problems. Instead of directly producing an answer for a given input, DeepSeek-R1 creates detailed...
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Reposted by Sara Vera Marjanovic
Gaurav Kamath @grvkamath.bsky.social · 29/07/2025
Our new paper in #PNAS (bit.ly/4fcWfma) presents a surprising finding—when words change meaning, older speakers rapidly adopt the new usage; inter-generational differences are often minor. w/ Michelle Yang, ‪@sivareddyg.bsky.social‬ , @msonderegger.bsky.social‬ and @dallascard.bsky.social‬👇(1/12)
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Sara Vera Marjanovic @saravera.bsky.social · 11/04/2025
And thoughtology is now on Arxiv! Read more about R1 reasoning 🐋💭 across visual, cultural and psycholinguistic tasks at the link below: 🔗 arxiv.org/abs/2504.07128
arxiv.org
DeepSeek-R1 Thoughtology: Let's <think> about LLM Reasoning
Large Reasoning Models like DeepSeek-R1 mark a fundamental shift in how LLMs approach complex problems. Instead of directly producing an answer for a given input, DeepSeek-R1 creates detailed multi-st...
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Sara Vera Marjanovic @saravera.bsky.social · 01/04/2025
Models like DeepSeek-R1 🐋 mark a fundamental shift in how LLMs approach complex problems. In our preprint on R1 Thoughtology, we study R1’s reasoning chains across a variety of tasks; investigating its capabilities, limitations, and behaviour. 🔗: mcgill-nlp.github.io/thoughtology/
A circular diagram with a blue whale icon at the center. The diagram shows 8 interconnected research areas around LLM reasoning represented as colored rectangular boxes arranged in a circular pattern. The areas include: §3 Analysis of Reasoning Chains (central cloud), §4 Scaling of Thoughts (discussing thought length and performance metrics), §5 Long Context Evaluation (focusing on information recall), §6 Faithfulness to Context (examining question answering accuracy), §7 Safety Evaluation (assessing harmful content generation and jailbreak resistance), §8 Language & Culture (exploring moral reasoning and language effects), §9 Relation to Human Processing (comparing cognitive processes), §10 Visual Reasoning (covering ASCII generation capabilities), and §11 Following Token Budget (investigating direct prompting techniques). Arrows connect the sections in a clockwise flow, suggesting an iterative research methodology.
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Reposted by Sara Vera Marjanovic
Lovisa Hagström @lovhag.bsky.social · 02/01/2025
📚 How good are language models at utilising contexts in RAG scenarios? We release 🧙🏽‍♀️DRUID to facilitate studies of context usage in real-world scenarios. arxiv.org/abs/2412.17031 w/ @saravera.bsky.social, H.Yu, @rnv.bsky.social, C.Lioma, M.Maistro, @apepa.bsky.social and @iaugenstein.bsky.social ⭐️
arxiv.org
A Reality Check on Context Utilisation for Retrieval-Augmented Generation
Retrieval-augmented generation (RAG) helps address the limitations of the parametric knowledge embedded within a language model (LM). However, investigations of how LMs utilise retrieved information o...
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