Megagon Labs (@megagonlabs) 's Twitter Profile
Megagon Labs

@megagonlabs

Megagon Labs advances state-of-the-art research in AI and builds technologies that impact the world through online services. #NLP #AI #ML #NLP4HR #LLM #RAG

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linkhttp://www.megagon.ai calendar_today04-06-2018 22:46:13

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๐Ÿง #LLM agents often fail in ways that are hard to interpret. Our new #VeriLA framework helps humans systematically verify and understand these failures. Catch the presentation by Yoo Yeon Sung@NAACL2025 at the HEAL Workshop at #CHI2025 on April 26th! ๐Ÿ“ฐ megagon.ai/publications/vโ€ฆ #AgenticAI

๐Ÿง #LLM agents often fail in ways that are hard to interpret. Our new #VeriLA framework helps humans systematically verify and understand these failures.

Catch the presentation by <a href="/YooYeonSung1/">Yoo Yeon Sung@NAACL2025</a> at the HEAL Workshop at #CHI2025 on April 26th!

๐Ÿ“ฐ megagon.ai/publications/vโ€ฆ
#AgenticAI
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Attending hashtag#NAACL25? Come find us at Booth #1. Get more info megagon.ai/links/ #LLM #AI #agents #CompoundAI #NLP #HR #HCI #Data #benchmarks #matching #summarization

Attending hashtag#NAACL25? Come find us at Booth #1. Get more info megagon.ai/links/

#LLM #AI #agents #CompoundAI #NLP #HR #HCI #Data #benchmarks #matching #summarization
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๐Ÿ“ฃHayate Iso will present at #NAACL25. Join the discussion at 10:30 a.m. GMT in Hall 3 Poster 4. Read the paper: megagon.ai/publications/eโ€ฆ #fairness #LLM #AI #HR

๐Ÿ“ฃ<a href="/iso_map/">Hayate Iso</a> will present at #NAACL25. Join the discussion at 10:30 a.m. GMT in Hall 3 Poster 4. 
Read the paper: megagon.ai/publications/eโ€ฆ

#fairness #LLM #AI #HR
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#NAACL25 come join Pouya Pezeshkpour for his poster presentation on "LLMs Are Not Intelligent Thinkers: Introducing Mathematical Topic Tree Benchmark for Comprehensive Evaluation of LLMs." arxiv.org/abs/2406.05194

#NAACL25 come join <a href="/PPezeshkpour/">Pouya Pezeshkpour</a> for his poster presentation on "LLMs Are Not Intelligent Thinkers: Introducing Mathematical Topic Tree Benchmark for Comprehensive Evaluation of LLMs." arxiv.org/abs/2406.05194
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How well do language models cope with complexity beyond a single document? As real-world AI applications grow more complex, robust multi-document reasoning becomes critical, and current LLMs are showing signs of strain. In this article, we explore three of our recent research

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If you're interested in how LLMs handle the' messy, ambiguous nature of multi-document inputs and where they fall short, this is for you. #NAACL2025 #ICLR2025 #LLMs #MultiDocument #Summarization #Reasoning #NLPResearch #MegagonLabs #AI #MachineLearning #NLP Pouya Pezeshkpour Seiji Maekawa

If you're interested in how LLMs handle the' messy, ambiguous nature of multi-document inputs and where they fall short, this is for you.

#NAACL2025 #ICLR2025 #LLMs #MultiDocument #Summarization #Reasoning #NLPResearch #MegagonLabs #AI #MachineLearning #NLP <a href="/PPezeshkpour/">Pouya Pezeshkpour</a> <a href="/SAYg_7/">Seiji Maekawa</a>
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How can enterprises adopt agentic AI without reinventing their entire tech stack? megagon.ai/blue-designingโ€ฆ In Volume 2 of the Blue article series, we take a deeper look at what it takes to make existing data, models, and services work in agentic workflows and what โ€œagentic for

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๐Ÿ“ข Find us at #MLSys2025 this week! ๐Ÿ“ฃ Eser Kandogan ๐Ÿ‡บ๐Ÿ‡ฆ will be presenting at the sponsor talk about #Blue, our open-source agentic compound #AI framework for enterprises ๐Ÿ‘‹ Our team is at our exhibitor booth giving away goodies, ready to talk about job and internship

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๐Ÿš€ ๐—ฅ๐—ฒ๐˜๐—ต๐—ถ๐—ป๐—ธ๐—ถ๐—ป๐—ด ๐—˜๐—ป๐˜๐—ฒ๐—ฟ๐—ฝ๐—ฟ๐—ถ๐˜€๐—ฒ ๐—”๐—ฟ๐—ฐ๐—ต๐—ถ๐˜๐—ฒ๐—ฐ๐˜๐˜‚๐—ฟ๐—ฒ ๐—ณ๐—ผ๐—ฟ ๐˜๐—ต๐—ฒ ๐—”๐—ด๐—ฒ๐—ป๐˜๐—ถ๐—ฐ ๐—˜๐—ฟ๐—ฎ In our latest blog post, megagon.ai/blue-designingโ€ฆ, we dive into how Megagon Labs' ๐—•๐—น๐˜‚๐—ฒ ๐—ณ๐—ฟ๐—ฎ๐—บ๐—ฒ๐˜„๐—ผ๐—ฟ๐—ธ reimagines enterprise architectures to support intelligent,

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๐Ÿ” ๐—–๐—ฎ๐—ป ๐˜๐—ผ๐—ฑ๐—ฎ๐˜†โ€™๐˜€ ๐—Ÿ๐—Ÿ๐— ๐˜€ ๐—ฎ๐—ฐ๐—ฐ๐˜‚๐—ฟ๐—ฎ๐˜๐—ฒ๐—น๐˜† ๐—พ๐˜‚๐—ฒ๐—ฟ๐˜† ๐—ฟ๐—ฒ๐—ฎ๐—น-๐˜„๐—ผ๐—ฟ๐—น๐—ฑ ๐—ธ๐—ป๐—ผ๐˜„๐—น๐—ฒ๐—ฑ๐—ด๐—ฒ ๐—ด๐—ฟ๐—ฎ๐—ฝ๐—ต๐˜€? ๐——๐—ฒ๐˜€๐—ฝ๐—ถ๐˜๐—ฒ ๐˜๐—ต๐—ฒ๐—ถ๐—ฟ ๐—ด๐—ฟ๐—ผ๐˜„๐—ถ๐—ป๐—ด ๐—ฐ๐—ฎ๐—ฝ๐—ฎ๐—ฏ๐—ถ๐—น๐—ถ๐˜๐—ถ๐—ฒ๐˜€, ๐—น๐—ฎ๐—ฟ๐—ด๐—ฒ ๐—น๐—ฎ๐—ป๐—ด๐˜‚๐—ฎ๐—ด๐—ฒ ๐—บ๐—ผ๐—ฑ๐—ฒ๐—น๐˜€ ๐˜€๐˜๐—ถ๐—น๐—น ๐˜€๐˜๐—ฟ๐˜‚๐—ด๐—ด๐—น๐—ฒ ๐˜„๐—ถ๐˜๐—ต ๐—ฝ๐—ฟ๐—ฒ๐—ฐ๐—ถ๐˜€๐—ฒ,

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๐Ÿšจ We are excited to announce that our paper, "FactLens: Benchmarking Fine-Grained Fact Verification", has been accepted at #ACL2025 Findings! megagon.ai/publications/fโ€ฆ ๐Ÿ” As #LLMs become increasingly prevalent, ensuring the factual accuracy of their outputs is paramount.