Nitish Gupta @nitish_gup
Research Scientist at @GoogleAI in Natural Language Processing & Machine Learning PhD from University of Pennsylvania | Undergrad from IIT Kanpur nitishgupta.github.io Bengaluru, India Joined April 2016-
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Excited to release IndicGenBench: A suite of evaluation datasets for multiple tasks in 29 Indic languages! Evaluations over many LLMs reveal huge room for improvement. IndicGenBench is multi-way parallel opening doors for interesting research! Great work by @Harman26Singh!
Excited to release IndicGenBench: A suite of evaluation datasets for multiple tasks in 29 Indic languages! Evaluations over many LLMs reveal huge room for improvement. IndicGenBench is multi-way parallel opening doors for interesting research! Great work by @Harman26Singh!
Super excited to be part of this journey with @partha_p_t and other amazing colleagues at Google Research! #GoogleForIndia
Super excited to be part of this journey with @partha_p_t and other amazing colleagues at Google Research! #GoogleForIndia
#NLPaperAlert: QA Dataset Explosion!🔥 A survey of 200+ QA/RC datasets proposing a taxonomy of formats & reasoning skills. Also in the bag: modalities, conversational QA, domains & beyond-English data. Honored to work on this with @nlpmattg & @IAugenstein arxiv.org/abs/2107.12708
Some life updates: I defended my PhD in April! Special thanks to my advisor @DanRothNLP and @sameer_ & @nlpmattg for being there during the journey! Very excited to have joined @GoogleAI as a research scientist this week! Looking forward to continuing research in NLU 😀
Really excited about this work w/ @sameer_ and @nlpmattg! We show that enforcing consistency between semantic parses of related NL utterances leads to improved learning in weakly supervised setting! Also, it got accepted to ACL 2021! Paper - arxiv.org/abs/2107.05833
Really excited about this work w/ @sameer_ and @nlpmattg! We show that enforcing consistency between semantic parses of related NL utterances leads to improved learning in weakly supervised setting! Also, it got accepted to ACL 2021! Paper - arxiv.org/abs/2107.05833
Thrilled to receive the NSF CAREER award! Looking forward to exciting research & collaborations. Thanks to my mentors, colleagues, & esp students. Also thanks to @shsriva for his support & to this gentleman for waiting until the proposal submission ddl to arrive into the world.
Thrilled to receive the NSF CAREER award! Looking forward to exciting research & collaborations. Thanks to my mentors, colleagues, & esp students. Also thanks to @shsriva for his support & to this gentleman for waiting until the proposal submission ddl to arrive into the world. https://t.co/zcgs5rfQhl
A reminder that the AKBC deadline (June 17th) is 4 weeks away! Please help RT and submit your work! The submission site is open and all the details can be found at: akbc.ws/2021/cfp/ #AKBC2021
Check out SacreROUGE, from my (former) lab mate @_danieldeutsch I am a big fan of Sacre* metrics libraries and would like to see more. github.com/danieldeutsch/…
It wasn't easy to review #nlproc research for an extremely unusual year, but here's an attempt. Listen to the episode or see below for the main themes I covered, and links to some of the representative papers for each (mostly in order I talked about them). Warning: Long thread...
It wasn't easy to review #nlproc research for an extremely unusual year, but here's an attempt. Listen to the episode or see below for the main themes I covered, and links to some of the representative papers for each (mostly in order I talked about them). Warning: Long thread...
In case you haven't seen enough papers on arXiv recently a new EMNLP findings paper #emnlp2020 led by Inbar Oren: arxiv.org/abs/2010.05647 We know already seq2seq models generalize badly to new compositions in semantic parsing... 1/3
Come join us in the Q&A session tomorrow on Obtaining Faithful Interpretations from NMNs. We'll be happy to answer questions, discuss, or just chat! (@ben_bogin @sanjayssub @sameer_ @JonathanBerant @nlpmattg) virtual.acl2020.org/paper_main.495…
Come join us in the Q&A session tomorrow on Obtaining Faithful Interpretations from NMNs. We'll be happy to answer questions, discuss, or just chat! (@ben_bogin @sanjayssub @sameer_ @JonathanBerant @nlpmattg) virtual.acl2020.org/paper_main.495…
It was great to meet fellow QA researchers at the #acl2020nlp Birds-of-Feather QA Session. Great job organizing @DanielKhashabi and nice to see @JonathanBerant @HannaHajishirzi @nitish_gup @sewon__min . Seems we should take the discussing to a future workshop? 😊
Who are you in the NLP quarantine? (A creation I am most proud of, now in English)
1/ We have a very poor understanding of privilege in India. This has far-reaching effects on the products we design and build. I am writing this in English. You are able to read it. Congrats - we are both privileged.
Happy to host our first speaker @nitish_gup for the summer zoom seminar series at @UUDataScience. He will be speaking on neural module networks for complex reasoning over text, more details here : datascience.utah.edu/club/summer-se…. Stay tuned for upcoming exciting talks. @UtahSoC @UUtah
Reading arxiv.org/pdf/2005.00724… @sanjayssub et al. Evidence of "non-faithfulness" in NMNs: the disentangled neural modules we get from e.g. CLEVR don't arise in models trained on natural data. But what does it mean for an NMN to be faithful? 1/
ACL paper w/ Qiang Ning, @DanielKhashabi and Dan Roth: arxiv.org/abs/2005.04304. We train LMs to better understand time (events' duration, frequency, etc.) and to give quality predictions. Our model also does better on related extrinsic tasks (e.g., sub-event RE) via fine-tuning.
Obtaining Faithful Interpretations from Compositional Neural Networks deepai.org/publication/ob… by Sanjay Subramanian et al. including @nitishgupta4291, @sameer_, @JonathanBerant, @ml_gardner #Computation #Language
Dataset biases can easily overpower inductive biases giving us a false impression of progress. It is critical to have both in-distribution hard splits and out-of-distribution splits to measure generalization. We examine language grounding 1/n x.com/arjunreddy2613…
Dataset biases can easily overpower inductive biases giving us a false impression of progress. It is critical to have both in-distribution hard splits and out-of-distribution splits to measure generalization. We examine language grounding 1/n x.com/arjunreddy2613…
🥞 Fresh from the press: a new review of #KnowledgeGraph related papers from ongoing ICLR 2020. Among other things we'll review what's happening in complex QA, KG embeddings and entity matching with graph embeddings @iclr_conf medium.com/@mgalkin/knowl…
Danish Pruthi @danish037
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Delip Rao e/σ @deliprao
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Shruti Rijhwani @shrutirij
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Greg Durrett @gregd_nlp
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Sameer Singh @sameer_
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Ofir Press 🖋 @OfirPress
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Sebastian Ruder @seb_ruder
80K Followers 1K Following Multilingual LLMs @cohere • Prev: @GoogleDeepMind • Newsletter: https://t.co/7JGh2qpG98
Dean Clark @DeanCla88922559
144 Followers 1K Following Disabled part-time student, registered for artificial kidney trials.
Gagan Jain @gaganjain1582
53 Followers 747 Following Research Associate @GoogleDeepMind | IIT Bombay'22
Koustava Goswami @koustavagoswami
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Ram Samarth @chaostocolor8
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166 Followers 626 Following research fellow @MSFTResearch | ex edg-intern @MathWorks | undergraduate researcher @iiitdelhi
Aaditya ; @Aaditya26082004
531 Followers 7K Following CS'26 • Machine Learning • Open-Source • Web Dev. • Algorithms • Jai Shree Krishna 🦚🪈
TabithaGracie @SF6r6Jg0B3ugxlp
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5 Followers 41 Following
Atharva Pawar @Atharva08
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super intelligence @eacc72
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Aishwarya Chandraseka.. @aish2306
764 Followers 3K Following she/her CS PhD student @UDelaware Member of the @sensifylab Advisor : @mattm401 Former Data Scientist @wearemiq Former Data Science Intern @AnalyticsVidhya
sandya mannarswamy @sandyasm
864 Followers 5K Following Natural Language Processing Researcher. https://t.co/oYoCTKS2Ho
pawann k. @pawaniiit
220 Followers 4K Following Prof., PhD, Inria, France, Postdoc KU Leuven, Fraunhofer ITWM, FU Berlin. I like Machine learning and mathematics.
Akshita Jha @akshitajha
204 Followers 414 Following Ph.D. Candidate @VT_CS (she/her). Responsible NLP. Previously: @iiit_hyderabad, @debian, @linuxfoundation, 2x Research intern @GoogleAI
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Aditya Sinha @adityaasinha
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399 Followers 3K Following AAAI-UC Scholar 23 | Intern @Mila_Quebec , Prev- @Bosch_Research ,KLIV IITKGP | Looking for Full Time AI/DS/ML/RS Roles |
Anwoy Chatterjee @anwoy_
32 Followers 361 Following PhDing in #NLProc / #AI @LCS2Lab @IITDelhi • Prev: BTech in CS @IITBHU_Varanasi Views are personal
Mahesh Sathiamoorthy @madiator
9K Followers 933 Following LLMs and Data. Discuss about data for LLMs: https://t.co/x4iAft5cHV Ex-GoogleDeepMind
Siddhant Chaudhary @sid_codetalker7
10 Followers 92 Following Interested in Machine Learning Research and Open Source. Currently student @ Chennai Mathematical Institute.
Jigar Doshi @jigarkdoshi
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Hassan Hayat 🔥 @TheSeaMouse
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Shikhar Vashishth @shikharv15
34 Followers 151 Following Research Scientist @GoogleAI | Prev. @MSFTResearch, @LTIatCMU
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147 Followers 317 Following Ph.D. student in CSE @osunlp. Research Interests: Multimodal Foundation Models, KG reasoning, NLP Ex-intern @GoogleAI Prev @Mila_Quebec @IBMResearch @IITKgp
Divy Thakkar @divy93t
5K Followers 2K Following Strategy, Programs & Product @GoogleAI , HCI Researcher. Ph.D @CityUniLondon Alumni @iift1963 @daiictofficial. Personal views.
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556 Followers 3K Following Autonomous Vehicle Research Assistant @IIITDelhi, 4x @kaggle expert, Former ML intern @IITKgp, @UNSW
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(((ل()(ل() 'yoav))).. @yoavgo
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11K Followers 650 Following Ph.D. student @uwcse & @uwnlp. NLP. IBM Ph.D. fellow (2022-2023). Meta student researcher (2023-) . ☕️ 🐕 🏃♀️🧗♀️🍳
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Yann LeCun @ylecun
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Delip Rao e/σ @deliprao
46K Followers 5K Following Busy inventing the shipwreck. @Penn. Past: @johnshopkins, @UCSC, @Amazon, @Twitter ||Art: #NLProc, Vision, Speech, #DeepLearning || Life: 道元, improv, running 🌈
Kyunghyun Cho @kchonyc
61K Followers 2K Following a combination of a mediocre scientist, a mediocre manager, a mediocre advisor & a mediocre PC at @nyuniversity (@CILVRatNYU) & @genentech (@PrescientDesign).
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979K Followers 905 Following 🧑🍳. Previously Director of AI @ Tesla, founding team @ OpenAI, CS231n/PhD @ Stanford. I like to train large deep neural nets 🧠🤖💥
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49K Followers 408 Following Associate Professor in computer science @Stanford @StanfordHAI @StanfordCRFM @StanfordAILab @stanfordnlp | cofounder @togethercompute | Pianist
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4K Followers 604 Following Asst prof @UUtah · Ex @allen_ai @uwnlp postdoc @HD_NLP PhD · she/her 🇭🇷
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Sam Bowman @sleepinyourhat
35K Followers 3K Following AI alignment + LLMs at NYU & Anthropic. Views not employers'. No relation to @s8mb. I think you should join @givingwhatwecan.
Shruti Rijhwani @shrutirij
4K Followers 499 Following * Research Scientist @GoogleDeepMind * #NLProc research * PhD from @LTIatCMU * Amateur woodworker, scuba diver, foosball player
Rachit Bansal @rach_it_
892 Followers 1K Following Pre-doctoral Researcher @GoogleAI • Prev. @dtu_delhi '22 @technionlive @AdobeResearch • Anything `science', ~cosmos, and Oxford commas
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Conference on Languag.. @COLM_conf
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548 Followers 2K Following Search Scientist. Working at the intersection of Machine learning, Information retrieval, Recommender Systems, Data Mining and User Experience.
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Jim Fan @DrJimFan
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Sumanth @sumanthd17
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Monojit Choudhury @monojitchou
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Shaily @shaily99
5K Followers 2K Following PhD @LTIatCMU Prev: @GoogleAI @MSFTResearch. Working on ethics and evaluation in #NLProc. Usually ranting, often about research & DEI. 📚 @readsndrants
Ameya Daigavane @BigAmeya
948 Followers 447 Following PhD student in the Atomic Architects group at @MITEECS! Previously @NASAJPL @GoogleAI @IITGuwahati
Yonatan Belinkov @boknilev
4K Followers 1K Following Assistant professor of computer science @TechnionLive. #NLProc
Sneha Mondal @SnehaMon
178 Followers 170 Following Engineer @GoogleAI. Ex - @IBMResearch, alumnus @iiscbangalore. Machine Learning, Natural Language Processing.
Melvin Johnson @melvinjohnsonp
980 Followers 280 Following Researcher @ Google Research. Multilingual NLP and MT. Previously, Stanford CS.
Preethi Jyothi @PreethiJyothi1
725 Followers 399 Following At the Dept. of CSE@IIT Bombay since 2016. Interested in speech, language and machine learning.
Ankur Bapna @ankurbpn
723 Followers 564 Following Audio in Gemini. Low resource multilingual nlp and speech. At Google Deepmind.
Alan Ritter @alan_ritter
5K Followers 1K Following Computing professor at Georgia Tech - natural language processing, machine learning, information extraction, dialogue
Wei Xu @cocoweixu
9K Followers 1K Following CS professor @GeorgiaTech @gtcomputing @ICatGT @mlatgt. Natural language processing, machine learning, social media research.
Jonathan Clark @JonClarkSeattle
3K Followers 2K Following Research Scientist @ Google: Multilingual NLP, Machine Learning, C++. Previously MT@Microsoft and CMU. Opinions are my own.
Ponnurangam Kumaragur.. @ponguru
6K Followers 385 Following #ProfGiri #Precog #PGChairGiri @iiit_hyderabad, Distinguished Member @TheOfficialACM, TEDx Speaker, Angel Investor, Alumni @CarnegieMellon @bitspilaniindia
Another contribution from our Languages team to support further research on AI for Indian languages.
Very pleased IndicGenBench is now out, lots of headroom for PhD students to start cracking 🙂 Particularly happy about the 29 languages coverage, including first-time generative evals (or any evals?) for many languages , eg, Garhwali, Konkani, Rajasthani, etc. Enjoy! @GoogleAI
This is such an important finding. Performative equity for low resource languages, and by proxy - for underrepresented communities - requires efforts that go beyond just better models. Infrastructural changes (such as sliding scale on API cost for languages) would go a long way.
Tokenization: There is a large gap between tokenizer fertility across indic languages. As a consequence, higher fertility allows fewer in-context examples to be part of the input. This leads to large inference compute requirements and cost while using API based LLMs.
My awesome colleague talking about his inspiring work on language inclusivity...
Thank you @bruke_kifle and #ACMByteCast for hosting me and for the opportunity, I very much enjoyed our conversation!
@Harman26Singh @simi_97k Fantastic work. Which open source LLM performs the best as per your benchmark?
New work on evaluating LLMs for generation in Indic Languages: IndicGenBench 👉5 diverse tasks, 29 Indic languages, >100k examples. 👉Curated using human translations ensuring high quality. 👉Multi-way parallel dataset. arxiv.org/abs/2404.16816 github.com/google-researc… (1/n)
@divy93t @Harman26Singh Thanks for your kind words and consistent support! Thanks to @Harman26Singh and @nitish_gup for leading IndicGenBench effort in awesome collaboration with @ShikharSSU and @dinesh_tewari1
IndicGenBench and several works from @partha_p_t 's group are a classical example of establishing a long-term research agenda and then consistently making in-roads through deep work! Great work, @Harman26Singh @partha_p_t and co-authors!
New work on evaluating LLMs for generation in Indic Languages: IndicGenBench 👉5 diverse tasks, 29 Indic languages, >100k examples. 👉Curated using human translations ensuring high quality. 👉Multi-way parallel dataset. arxiv.org/abs/2404.16816 github.com/google-researc… (1/n)
Very pleased IndicGenBench is now out, lots of headroom for PhD students to start cracking 🙂 Particularly happy about the 29 languages coverage, including first-time generative evals (or any evals?) for many languages , eg, Garhwali, Konkani, Rajasthani, etc. Enjoy! @GoogleAI
New work on evaluating LLMs for generation in Indic Languages: IndicGenBench 👉5 diverse tasks, 29 Indic languages, >100k examples. 👉Curated using human translations ensuring high quality. 👉Multi-way parallel dataset. arxiv.org/abs/2404.16816 github.com/google-researc… (1/n)
29 languages!
Very pleased IndicGenBench is now out, lots of headroom for PhD students to start cracking 🙂 Particularly happy about the 29 languages coverage, including first-time generative evals (or any evals?) for many languages , eg, Garhwali, Konkani, Rajasthani, etc. Enjoy! @GoogleAI
🤔 Want to push the boundaries of multilingual NLP? Excited to share our recent work on an evaluation benchmark for generation capabilities in low-resource Indic languages. What will you build with it? 💻 #NLProc github.com/google-researc… arxiv.org/abs/2404.16816
New work on evaluating LLMs for generation in Indic Languages: IndicGenBench 👉5 diverse tasks, 29 Indic languages, >100k examples. 👉Curated using human translations ensuring high quality. 👉Multi-way parallel dataset. arxiv.org/abs/2404.16816 github.com/google-researc… (1/n)
29 languages! And the first ever eval for many of them. Check this cool work out!
New work on evaluating LLMs for generation in Indic Languages: IndicGenBench 👉5 diverse tasks, 29 Indic languages, >100k examples. 👉Curated using human translations ensuring high quality. 👉Multi-way parallel dataset. arxiv.org/abs/2404.16816 github.com/google-researc… (1/n)
See our paper for more details and results: Arxiv link: arxiv.org/abs/2404.16816 Dataset link: github.com/google-researc… Work done @GoogleAI w/ awesome collaborators and mentors: @partha_p_t @nitish_gup @ShikharSSU @dinesh_tewari1
Tokenization: There is a large gap between tokenizer fertility across indic languages. As a consequence, higher fertility allows fewer in-context examples to be part of the input. This leads to large inference compute requirements and cost while using API based LLMs.
We comprehensively study the effect of in-context examples, and compare it with fine-tuning. On Indic languages, in-context learning is often better than fine-tuning on thousands of examples. Transfer from a high resource Indic language is much better than transfer from English.
We evaluate multiple open and proprietary LLMs such as GPT-3.5, GPT-4, PaLM-2, mT5, Gemma, BLOOM and LLaMA on our benchmark. Evaluations reveal a significant performance drop going from higher resourced languages to medium and further to lower resourced languages.
👉IndicGenBench includes 5 user-centric tasks: Cross-lingual Summarization (CrossSum-IN), Machine Translation (Flores-In), Multilingual QA (XQuAD-In) and Cross-lingual QA (XorQA-In-Xx, XorQA-In-En). 👉IndicGenBench supports 29 low to relatively higher resource Indic Languages
was waiting for something like this these benchmarks will soon be added to indic_eval(github.com/adithya-s-k/in…) library to easily evaluate any LLM and also integrate it with the Indic LLM leaderboard. great work @Harman26Singh @nitish_gup
New work on evaluating LLMs for generation in Indic Languages: IndicGenBench 👉5 diverse tasks, 29 Indic languages, >100k examples. 👉Curated using human translations ensuring high quality. 👉Multi-way parallel dataset. arxiv.org/abs/2404.16816 github.com/google-researc… (1/n)
Big Thanks to my managers across my stints in MTV & BLR - Krishna, Sarvjeet, Nitish and Partha
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