no code implementations • 12 May 2018 • Ameet Deshpande, Vedant Somani
Ontological methods are good at encoding Semantic Similarity and Vector Space models are better at encoding Semantic Relatedness.
no code implementations • 12 Jun 2018 • Revanth Reddy, Rahul Ramesh, Ameet Deshpande, Mitesh M. Khapra
Deep Learning has managed to push boundaries in a wide variety of tasks.
no code implementations • 16 Sep 2018 • Ameet Deshpande, Srikanth Sarma, Ashutosh Jha, Balaraman Ravindran
One such approach is Hindsight Experience replay which uses an off-policy Reinforcement Learning algorithm to learn a goal conditioned policy.
no code implementations • 1 Dec 2018 • Ameet Deshpande, Harshavardhan Kamarthi, Balaraman Ravindran
Learning options that allow agents to exhibit temporally higher order behavior has proven to be useful in increasing exploration, reducing sample complexity and for various transfer scenarios.
no code implementations • ICLR 2019 • Ameet Deshpande, Mitesh M. Khapra
Recent advances in Generative Adversarial Networks facilitated by improvements to the framework and successful application to various problems has resulted in extensions to multiple domains.
1 code implementation • EMNLP (NLPOSS) 2020 • Raeid Saqur, Ameet Deshpande
The CLEVR dataset has been used extensively in language grounded visual reasoning in Machine Learning (ML) and Natural Language Processing (NLP) domains.
no code implementations • 1 Oct 2020 • Ameet Deshpande, Mitesh M. Khapra
Recent advances in Generative Adversarial Networks (GANs) have resulted in its widespread applications to multiple domains.
no code implementations • 5 Oct 2020 • Ameet Deshpande, Eve Fleisig
Furthermore, this can enable reinforcement learning without rewards, in which the agent learns entirely from these intrinsic sentiment rewards.
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Ameet Deshpande, Karthik Narasimhan
In this paper, we propose a simple and effective technique to allow for efficient self-supervised learning with bi-directional Transformers.
2 code implementations • NAACL 2022 • Ameet Deshpande, Partha Talukdar, Karthik Narasimhan
While recent work on multilingual language models has demonstrated their capacity for cross-lingual zero-shot transfer on downstream tasks, there is a lack of consensus in the community as to what shared properties between languages enable such transfer.
1 code implementation • 26 Feb 2022 • Austin W. Hanjie, Ameet Deshpande, Karthik Narasimhan
Prior work along this vein have largely used expensive per-instance annotation or singular class-level descriptions, but per-instance descriptions are hard to scale and single class descriptions may not be rich enough.
1 code implementation • 15 Nov 2022 • Henry Tang, Ameet Deshpande, Karthik Narasimhan
In particular, ALIGN-MLM outperforms XLM and MLM by 35 and 30 F1 points on POS-tagging for transfer between languages that differ both in their script and word order (left-to-right v. s.
1 code implementation • 29 Nov 2022 • Ameet Deshpande, Md Arafat Sultan, Anthony Ferritto, Ashwin Kalyan, Karthik Narasimhan, Avirup Sil
Fine-tuning pre-trained language models (PLMs) achieves impressive performance on a range of downstream tasks, and their sizes have consequently been getting bigger.
1 code implementation • 26 Jan 2023 • Pranjal Aggarwal, Ameet Deshpande, Karthik Narasimhan
In this paper, we develop SemSup-XC, a model that achieves state-of-the-art zero-shot and few-shot performance on three XC datasets derived from legal, e-commerce, and Wikipedia data.
1 code implementation • 24 Feb 2023 • Vishvak Murahari, Ameet Deshpande, Carlos E. Jimenez, Izhak Shafran, Mingqiu Wang, Yuan Cao, Karthik Narasimhan
The widespread adoption of large language models such as ChatGPT and Bard has led to unprecedented demand for these technologies.
no code implementations • 11 Apr 2023 • Ameet Deshpande, Vishvak Murahari, Tanmay Rajpurohit, Ashwin Kalyan, Karthik Narasimhan
Large language models (LLMs) have shown incredible capabilities and transcended the natural language processing (NLP) community, with adoption throughout many services like healthcare, therapy, education, and customer service.
no code implementations • 24 May 2023 • Ameet Deshpande, Tanmay Rajpurohit, Karthik Narasimhan, Ashwin Kalyan
With widespread adoption of AI systems, and the push from stakeholders to make it human-like through alignment techniques, human voice, and pictorial avatars, the tendency for users to anthropomorphize it increases significantly.
1 code implementation • 24 May 2023 • Ameet Deshpande, Carlos E. Jimenez, Howard Chen, Vishvak Murahari, Victoria Graf, Tanmay Rajpurohit, Ashwin Kalyan, Danqi Chen, Karthik Narasimhan
Semantic textual similarity (STS), a cornerstone task in NLP, measures the degree of similarity between a pair of sentences, and has broad application in fields such as information retrieval and natural language understanding.
no code implementations • 1 Jul 2023 • Anirudh Ajith, Chris Pan, Mengzhou Xia, Ameet Deshpande, Karthik Narasimhan
In-context learning (ICL) performs tasks by prompting a large language model (LLM) using an instruction and a small set of annotated examples called demonstrations.
no code implementations • 31 Aug 2023 • Atharvan Dogra, Deeksha Varshney, Ashwin Kalyan, Ameet Deshpande, Neeraj Kumar
The generation of effective latent representations and their subsequent refinement to incorporate precise information is an essential prerequisite for Vision-Language Understanding (VLU) tasks such as Video Question Answering (VQA).
1 code implementation • 6 Nov 2023 • Vishvak Murahari, Ameet Deshpande, Peter Clark, Tanmay Rajpurohit, Ashish Sabharwal, Karthik Narasimhan, Ashwin Kalyan
In this work, we address the shortcomings of quantitative metrics by proposing QualEval, which augments quantitative scalar metrics with automated qualitative evaluation as a vehicle for model improvement.
1 code implementation • 8 Nov 2023 • Shashank Gupta, Vaishnavi Shrivastava, Ameet Deshpande, Ashwin Kalyan, Peter Clark, Ashish Sabharwal, Tushar Khot
Our experiments with ChatGPT-3. 5 show that this bias is ubiquitous - 80% of our personas demonstrate bias; it is significant - some datasets show performance drops of 70%+; and can be especially harmful for certain groups - some personas suffer statistically significant drops on 80%+ of the datasets.
no code implementations • 16 Nov 2023 • Pranjal Aggarwal, Vishvak Murahari, Tanmay Rajpurohit, Ashwin Kalyan, Karthik R Narasimhan, Ameet Deshpande
We facilitate systematic evaluation in this new paradigm by introducing GEO-bench, a benchmark of diverse user queries across multiple domains, coupled with sources required to answer these queries.
no code implementations • 12 Apr 2024 • Shreyas Chaudhari, Pranjal Aggarwal, Vishvak Murahari, Tanmay Rajpurohit, Ashwin Kalyan, Karthik Narasimhan, Ameet Deshpande, Bruno Castro da Silva
A promising approach is reinforcement learning from human feedback (RLHF), which leverages human feedback to update the model in accordance with human preferences and mitigate issues like toxicity and hallucinations.