LTL-UvA @ltl_uva
Language Technology Lab @UvA_Amsterdam ltl.science.uva.nl Amsterdam, The Netherlands Joined December 2022-
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Key Finding 2: With an observation of the increasing reliability of the model's self-knowledge for distinguishing misaligned and clean data at the token level, we propose self-correction to leverage the model's self-knowledge to correct the training supervision.
Key Finding 2: With an observation of the increasing reliability of the model's self-knowledge for distinguishing misaligned and clean data at the token level, we propose self-correction to leverage the model's self-knowledge to correct the training supervision. https://t.co/DwSO8ov4Ey
Key Finding 1: We simulate the primary source of noise in the parallel corpus, i.e., semantic misalignment, and show the limited effectiveness of widely-used sentence-level pre-filters for detecting it. This underscores the necessity of handling data noise in a fine-grained way.
Key Finding 1: We simulate the primary source of noise in the parallel corpus, i.e., semantic misalignment, and show the limited effectiveness of widely-used sentence-level pre-filters for detecting it. This underscores the necessity of handling data noise in a fine-grained way. https://t.co/iB0cfubiwD
The paper explores the effect of kNN on ASR using Whisper in general, as well as on speaker adaptation and bias. kNN is a non-parametric method that adapts the output of a model to a domain by searching for neighbouring tokens in a datastore at each step.
The paper explores the effect of kNN on ASR using Whisper in general, as well as on speaker adaptation and bias. kNN is a non-parametric method that adapts the output of a model to a domain by searching for neighbouring tokens in a datastore at each step.
LTL News: Happy to announce that Maya's paper got accepted by NAACL 2025 (findings) 🥳#naacl #nlp Paper Link: arxiv.org/abs/2410.18850
We show empirically that LLMs fail to exploit grammatical explanations for translation; instead we find parallel examples mainly drive translation performance. While grammatical knowledge does not help translation, LLMs benefit from our typological prompt for linguistic tasks.
We show empirically that LLMs fail to exploit grammatical explanations for translation; instead we find parallel examples mainly drive translation performance. While grammatical knowledge does not help translation, LLMs benefit from our typological prompt for linguistic tasks.
LTL News: Happy to announce that Seth's paper got accepted by ICLR (spotlight) 🥳@sethjsa Paper Link: arxiv.org/abs/2409.19151
Amsterdam NLPers at #EMNLP2024 🔥 @illc_amsterdam @UvA_IvI
Our work “Can LLMs Really Learn to Translate a Low-Resource Language from One Grammar Book?” is now on arXiv! arxiv.org/abs/2409.19151 - in collaboration with @davidstap, @diwuNLP, @c_monz , and Khalil Sima'an from @illc_amsterdam and @ltl_uva 🧵
4. Representational Isomorphism and Alignment of Multilingual Large Language Models. We will release Di's paper later! #EMNLP2024 #NLProc
4. Representational Isomorphism and Alignment of Multilingual Large Language Models. We will release Di's paper later! #EMNLP2024 #NLProc
3. How to identify intrinsic task modularity within multilingual translation networks? Check out Shaomu's paper: arxiv.org/abs/2404.11201
3. How to identify intrinsic task modularity within multilingual translation networks? Check out Shaomu's paper: arxiv.org/abs/2404.11201
2. ApiQ: Finetuning of 2-Bit Quantized Large Language Model, check out Baohao's paper: arxiv.org/abs/2402.05147 #EMNLP2024
2. ApiQ: Finetuning of 2-Bit Quantized Large Language Model, check out Baohao's paper: arxiv.org/abs/2402.05147 #EMNLP2024
1. Can you learn the meaning of words from someone who thinks you are smarter than you are? Check out Kata's paper: arxiv.org/pdf/2410.05851 #EMNLP2024 #NLProc
1. Can you learn the meaning of words from someone who thinks you are smarter than you are? Check out Kata's paper: arxiv.org/pdf/2410.05851 #EMNLP2024 #NLProc
Language Technology Lab got four papers accepted for #EMNLP2024! Congrats to authors Kata Naszadi, Shaomu Tan, Baohao Liao @baohao_liao, Di Wu @diwuNLP 🥳🥳
#NeurIPS decision is out
Just returned from MT Marathon 2024 in Prague - thanks to @ufal_cuni for organising a great week! Between the insightful talks and collaboration on a mini research project, I presented a poster of my recent work. And of course, we explored the sights of Prague too - in 30°C heat!
🚨 New paper 🚨 Our multilingual system for the WMT24 general shared task obtain: --- Constrained track: 6 🥇3 🥈 1 🥉 --- Open & Constrained track: 1 🥇2 🥈 2 🥉 A simple and effective pipeline to adapt LLM to multilingual machine translation. paper: arxiv.org/abs/2408.11512
Language Technology Lab at ACL🇹🇭! Busy poster presentation by @davidstap @diwuNLP #ACL2024 #ACL2024NLP
Inspiring day at GRaM @GRaM_org_ workshop! My only complaint: too short! I want more! 😁 Thanks to organizers for such a great experience ❤️ Amazing talks (personal favorites by @ninamiolane and by @bose_joey), great posters and panel session I genuinely enjoyed. #ICML2024
Come check our poster tomorrow at @GRaM_org_ @icmlconf if you want to discuss dispersion of text embeddings on hyperspheres! 27.07 at Poster session 2. #ICML2024
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