UCL Natural Language Processing

917 posts

UCL Natural Language Processing

UCL Natural Language Processing

@ucl_nlp

NLP research group at University College London @UCLCS

London, England Katılım Nisan 2013
228 Takip Edilen12.9K Takipçiler
UCL Natural Language Processing retweetledi
Sohee Yang
Sohee Yang@soheeyang_·
🚨 New Paper 🚨 Can LLMs perform latent multi-hop reasoning without exploiting shortcuts? We find the answer is yes – they can recall and compose facts not seen together in training or guessing the answer, but success greatly depends on the type of the bridge entity (80%+ for country, 6% for year)! 1/N
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Yao Lu
Yao Lu@yaolu_nlp·
What if you could build a competitive multilingual LLM with just a translator, no matter the level of data resource available? 🤔TL;DR: We translate FineWeb to TransWeb, showing multilingual modelling can be addressed by the simple "translate everything" idea. w/@togethercompute
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Yao Lu
Yao Lu@yaolu_nlp·
Excited to share that UCL NLP group @ucl_nlp received a Christmas gift from NVIDIA Academic Grant Program. Following our previous successful collaboration on UK-LLM, thank you @NVIDIAAIDev for the continuing support of our multilingual pretraining research. #NVIDIAGrant
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Cyrus Wai-Chung Kwan
Cyrus Wai-Chung Kwan@cyruskwan1997·
OpenSIR: Open-Ended Self-Improving Reasoner Can LLMs teach themselves math without any training data? OpenSIR is an open-ended self-play framework where: Teacher proposes diverse, appropriately challenging problems Student learns to solve them Both co-evolve together 1/6
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Erik Arakelyan
Erik Arakelyan@_kire_kara_·
Had an great time presenting our work on Faithful and Verifiable #LLM reasoning at @emnlpmeeting and catching up with all the amazing researchers. Be sure to check out: arxiv.org/abs/2410.11900 Work done with the amazing @PMinervini @PSH_Lewis @pat_verga @IAugenstein
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Erik Arakelyan@_kire_kara_

Our method for achieving more faithful, verifiable and robust #LLM reasoning (FLARE 💫) has been accepted at #EMNLP2025 @emnlpmeeting ! Be sure to check out: arxiv.org/abs/2410.11900 Work done with the amazing @PMinervini @PSH_Lewis @pat_verga @IAugenstein

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Yu Zhao
Yu Zhao@yuzhaouoe·
Check out our “Learning GUI Grounding with Spatial Reasoning from Visual Feedback”! We reframe GUI grounding as an interactive search task by learning to move a virtual cursor via RL and using visual feedback! Massive improvements on ScreenSpot-v2: (+5.7%) and -Pro (+110.8%)!
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Pasquale Minervini
Pasquale Minervini@PMinervini·
My amazing collaborators will be presenting two works at NeurIPS (@NeurIPSConf) on neuro-symbolic diffusion models (by the nesy superstar @EmilevanKrieken) and on multi-modal long-context evaluation! (led by the incredible @zhaoweiwang4) 👇
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Joshua Ong
Joshua Ong@joshuaongg21·
We introduce PiCSAR (Probabilistic Confidence Selection And Ranking)💡: A simple training-free method for scoring samples based on probabilistic confidence, selecting a reasoning chain with the highest confidence from multiple sampled responses. ✏️PiCSAR is generalisable across LRMS and LLMs of different model families and scales. 📈PiCSAR achieves huge and consistent performance boost across models and datasets (eg. +10.18% on MATH500, +11.7% on AIME2024, +9.81% on AIME2025, +7.67% on GPQA). 💡By using PiCSAR on only 6 samples, we achieve higher accuracy than self-consistency on 16/32 samples. ⚡️We also observe that when using PiCSAR, confidence can be estimated using a much smaller model (eg. reducing 72b to 8b model) to improve computational efficiency. A huge thank you to my amazing collaborators: @zhengzhao97 @aryopg @soheeyang_ @cyruskwan1997 @zodiacJRH @WendaLi8 @PMinervini @e_giunchiglia Shay Cohen
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Emile van Krieken
Emile van Krieken@EmilevanKrieken·
We propose Neurosymbolic Diffusion Models! We find diffusion is especially compelling for neurosymbolic approaches, combining powerful multimodal understanding with symbolic reasoning 🚀 Read more 👇
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Yu Zhao
Yu Zhao@yuzhaouoe·
NAACL 2025 Oral Presentation💥 Our work about using Sparse AutoEncoder to resolve knowledge conflict will present on 30 Apr 11:30–11:45 AM • Ballroom C Thank Hongru for presenting our work!!!
Hongru Wang@HongruWang007

🎉 Thrilled to share our TWO #NAACL2025 oral papers! 👇 Welcome to catch me and talk about anything! 1️⃣ Steering Knowledge Selection Behaviours in LLMs via SAE-Based Representation Engineering 📅 30 Apr • 11:30–11:45 AM • Ballroom C TLDR: A general representation learning framwork to detect knowledge conflicts and control the generation direction of LLMs 🔗 aclanthology.org/2025.naacl-lon… 2️⃣ Self-DC: When to Reason & When to Act? 📅 2 May • 11:30–11:45 AM • San Miguel First to balance 🧠 reasoning vs 🛠️ acting in language agents, enabling on-the-fly decomposition & execution of compositional unknown questions. 🔗 aclanthology.org/2025.naacl-lon… See you in Ballroom C & San Miguel! #LLM #Agent #NAACL2025

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Ne Luo
Ne Luo@neluo19·
Hi! I will be attending #NAACL2025 and presenting our paper on self-training for tool-use today, an extended work of my MSc dissertation at @EdinburghNLP, supervised by @PMinervini. Time: 14:00-15:30 Location: Hall 3 Let’s chat and connect!😊
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Pasquale Minervini
Pasquale Minervini@PMinervini·
My amazing collaborators will present several works at ICLR and NAACL later this month -- please catch up with them if you're attending! I tried to summarise our recent work in a blog post: neuralnoise.com/2025/march-res…
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