Kaustubh Dhole


2023

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Large Language Models as SocioTechnical Systems
Kaustubh Dhole
Proceedings of the Big Picture Workshop

The expectation of Large Language Models (LLMs) to solve various societal problems has ignored the larger socio-technical frame of reference under which they operate. From a socio-technical perspective, LLMs are necessary to look at separately from other ML models as they have radically different implications in society never witnessed before. In this article, we ground Selbst et al.(2019)’s five abstraction traps – The Framing Trap, The Portability Trap, The Formalism Trap, The Ripple Effect Trap and the Solutionism Trap in the context of LLMs discussing the problems associated with the abstraction and fairness of LLMs. Through learnings from previous studies and examples, we discuss each trap that LLMs fall into, and propose ways to address the points of LLM failure by gauging them from a socio-technical lens. We believe the discussions would provide a broader perspective of looking at LLMs through a sociotechnical lens and our recommendations could serve as baselines to effectively demarcate responsibilities among the various technical and social stakeholders and inspire future LLM research.

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NusaCrowd: Open Source Initiative for Indonesian NLP Resources
Samuel Cahyawijaya | Holy Lovenia | Alham Fikri Aji | Genta Winata | Bryan Wilie | Fajri Koto | Rahmad Mahendra | Christian Wibisono | Ade Romadhony | Karissa Vincentio | Jennifer Santoso | David Moeljadi | Cahya Wirawan | Frederikus Hudi | Muhammad Satrio Wicaksono | Ivan Parmonangan | Ika Alfina | Ilham Firdausi Putra | Samsul Rahmadani | Yulianti Oenang | Ali Septiandri | James Jaya | Kaustubh Dhole | Arie Suryani | Rifki Afina Putri | Dan Su | Keith Stevens | Made Nindyatama Nityasya | Muhammad Adilazuarda | Ryan Hadiwijaya | Ryandito Diandaru | Tiezheng Yu | Vito Ghifari | Wenliang Dai | Yan Xu | Dyah Damapuspita | Haryo Wibowo | Cuk Tho | Ichwanul Karo Karo | Tirana Fatyanosa | Ziwei Ji | Graham Neubig | Timothy Baldwin | Sebastian Ruder | Pascale Fung | Herry Sujaini | Sakriani Sakti | Ayu Purwarianti
Findings of the Association for Computational Linguistics: ACL 2023

We present NusaCrowd, a collaborative initiative to collect and unify existing resources for Indonesian languages, including opening access to previously non-public resources. Through this initiative, we have brought together 137 datasets and 118 standardized data loaders. The quality of the datasets has been assessed manually and automatically, and their value is demonstrated through multiple experiments.NusaCrowd’s data collection enables the creation of the first zero-shot benchmarks for natural language understanding and generation in Indonesian and the local languages of Indonesia. Furthermore, NusaCrowd brings the creation of the first multilingual automatic speech recognition benchmark in Indonesian and the local languages of Indonesia. Our work strives to advance natural language processing (NLP) research for languages that are under-represented despite being widely spoken.

2022

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Proceedings of the 2nd Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)
Antoine Bosselut | Khyathi Chandu | Kaustubh Dhole | Varun Gangal | Sebastian Gehrmann | Yacine Jernite | Jekaterina Novikova | Laura Perez-Beltrachini
Proceedings of the 2nd Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)

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GEMv2: Multilingual NLG Benchmarking in a Single Line of Code
Sebastian Gehrmann | Abhik Bhattacharjee | Abinaya Mahendiran | Alex Wang | Alexandros Papangelis | Aman Madaan | Angelina Mcmillan-major | Anna Shvets | Ashish Upadhyay | Bernd Bohnet | Bingsheng Yao | Bryan Wilie | Chandra Bhagavatula | Chaobin You | Craig Thomson | Cristina Garbacea | Dakuo Wang | Daniel Deutsch | Deyi Xiong | Di Jin | Dimitra Gkatzia | Dragomir Radev | Elizabeth Clark | Esin Durmus | Faisal Ladhak | Filip Ginter | Genta Indra Winata | Hendrik Strobelt | Hiroaki Hayashi | Jekaterina Novikova | Jenna Kanerva | Jenny Chim | Jiawei Zhou | Jordan Clive | Joshua Maynez | João Sedoc | Juraj Juraska | Kaustubh Dhole | Khyathi Raghavi Chandu | Laura Perez Beltrachini | Leonardo F . R. Ribeiro | Lewis Tunstall | Li Zhang | Mahim Pushkarna | Mathias Creutz | Michael White | Mihir Sanjay Kale | Moussa Kamal Eddine | Nico Daheim | Nishant Subramani | Ondrej Dusek | Paul Pu Liang | Pawan Sasanka Ammanamanchi | Qi Zhu | Ratish Puduppully | Reno Kriz | Rifat Shahriyar | Ronald Cardenas | Saad Mahamood | Salomey Osei | Samuel Cahyawijaya | Sanja Štajner | Sebastien Montella | Shailza Jolly | Simon Mille | Tahmid Hasan | Tianhao Shen | Tosin Adewumi | Vikas Raunak | Vipul Raheja | Vitaly Nikolaev | Vivian Tsai | Yacine Jernite | Ying Xu | Yisi Sang | Yixin Liu | Yufang Hou
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

Evaluations in machine learning rarely use the latest metrics, datasets, or human evaluation in favor of remaining compatible with prior work. The compatibility, often facilitated through leaderboards, thus leads to outdated but standardized evaluation practices. We pose that the standardization is taking place in the wrong spot. Evaluation infrastructure should enable researchers to use the latest methods and what should be standardized instead is how to incorporate these new evaluation advances. We introduce GEMv2, the new version of the Generation, Evaluation, and Metrics Benchmark which uses a modular infrastructure for dataset, model, and metric developers to benefit from each other’s work. GEMv2 supports 40 documented datasets in 51 languages, ongoing online evaluation for all datasets, and our interactive tools make it easier to add new datasets to the living benchmark.

2021

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Saying No is An Art: Contextualized Fallback Responses for Unanswerable Dialogue Queries
Ashish Shrivastava | Kaustubh Dhole | Abhinav Bhatt | Sharvani Raghunath
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

Despite end-to-end neural systems making significant progress in the last decade for task-oriented as well as chit-chat based dialogue systems, most dialogue systems rely on hybrid approaches which use a combination of rule-based, retrieval and generative approaches for generating a set of ranked responses. Such dialogue systems need to rely on a fallback mechanism to respond to out-of-domain or novel user queries which are not answerable within the scope of the dialogue system. While, dialogue systems today rely on static and unnatural responses like “I don’t know the answer to that question” or “I’m not sure about that”, we design a neural approach which generates responses which are contextually aware with the user query as well as say no to the user. Such customized responses provide paraphrasing ability and contextualization as well as improve the interaction with the user and reduce dialogue monotonicity. Our simple approach makes use of rules over dependency parses and a text-to-text transformer fine-tuned on synthetic data of question-response pairs generating highly relevant, grammatical as well as diverse questions. We perform automatic and manual evaluations to demonstrate the efficacy of the system.

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The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
Sebastian Gehrmann | Tosin Adewumi | Karmanya Aggarwal | Pawan Sasanka Ammanamanchi | Anuoluwapo Aremu | Antoine Bosselut | Khyathi Raghavi Chandu | Miruna-Adriana Clinciu | Dipanjan Das | Kaustubh Dhole | Wanyu Du | Esin Durmus | Ondřej Dušek | Chris Chinenye Emezue | Varun Gangal | Cristina Garbacea | Tatsunori Hashimoto | Yufang Hou | Yacine Jernite | Harsh Jhamtani | Yangfeng Ji | Shailza Jolly | Mihir Kale | Dhruv Kumar | Faisal Ladhak | Aman Madaan | Mounica Maddela | Khyati Mahajan | Saad Mahamood | Bodhisattwa Prasad Majumder | Pedro Henrique Martins | Angelina McMillan-Major | Simon Mille | Emiel van Miltenburg | Moin Nadeem | Shashi Narayan | Vitaly Nikolaev | Andre Niyongabo Rubungo | Salomey Osei | Ankur Parikh | Laura Perez-Beltrachini | Niranjan Ramesh Rao | Vikas Raunak | Juan Diego Rodriguez | Sashank Santhanam | João Sedoc | Thibault Sellam | Samira Shaikh | Anastasia Shimorina | Marco Antonio Sobrevilla Cabezudo | Hendrik Strobelt | Nishant Subramani | Wei Xu | Diyi Yang | Akhila Yerukola | Jiawei Zhou
Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021)

We introduce GEM, a living benchmark for natural language Generation (NLG), its Evaluation, and Metrics. Measuring progress in NLG relies on a constantly evolving ecosystem of automated metrics, datasets, and human evaluation standards. Due to this moving target, new models often still evaluate on divergent anglo-centric corpora with well-established, but flawed, metrics. This disconnect makes it challenging to identify the limitations of current models and opportunities for progress. Addressing this limitation, GEM provides an environment in which models can easily be applied to a wide set of tasks and in which evaluation strategies can be tested. Regular updates to the benchmark will help NLG research become more multilingual and evolve the challenge alongside models. This paper serves as the description of the data for the 2021 shared task at the associated GEM Workshop.
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