Search Results for author: Tanmay Rajpurohit

Found 11 papers, 4 papers with code

Deception in Reinforced Autonomous Agents: The Unconventional Rabbit Hat Trick in Legislation

no code implementations7 May 2024 Atharvan Dogra, Ameet Deshpande, John Nay, Tanmay Rajpurohit, Ashwin Kalyan, Balaraman Ravindran

Recent developments in large language models (LLMs), while offering a powerful foundation for developing natural language agents, raise safety concerns about them and the autonomous agents built upon them.

Deception Detection Philosophy

RLHF Deciphered: A Critical Analysis of Reinforcement Learning from Human Feedback for LLMs

no code implementations12 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.

Language Modelling reinforcement-learning

GEO: Generative Engine Optimization

no code implementations16 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.

QualEval: Qualitative Evaluation for Model Improvement

no code implementations6 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.

Anthropomorphization of AI: Opportunities and Risks

no code implementations24 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.


C-STS: Conditional Semantic Textual Similarity

1 code implementation24 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.

Information Retrieval Language Modelling +8

Let GPT be a Math Tutor: Teaching Math Word Problem Solvers with Customized Exercise Generation

no code implementations22 May 2023 Zhenwen Liang, Wenhao Yu, Tanmay Rajpurohit, Peter Clark, Xiangliang Zhang, Ashwin Kaylan

In this paper, we present a novel approach for distilling math word problem solving capabilities from large language models (LLMs) into smaller, more efficient student models.

Knowledge Tracing Math +1

Hyperbolic Image-Text Representations

1 code implementation18 Apr 2023 Karan Desai, Maximilian Nickel, Tanmay Rajpurohit, Justin Johnson, Ramakrishna Vedantam

Visual and linguistic concepts naturally organize themselves in a hierarchy, where a textual concept "dog" entails all images that contain dogs.

Image Classification Retrieval +1

Toxicity in ChatGPT: Analyzing Persona-assigned Language Models

no code implementations11 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.

Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning

2 code implementations29 Sep 2022 Pan Lu, Liang Qiu, Kai-Wei Chang, Ying Nian Wu, Song-Chun Zhu, Tanmay Rajpurohit, Peter Clark, Ashwin Kalyan

However, it is unknown if the models can handle more complex problems that involve math reasoning over heterogeneous information, such as tabular data.

Logical Reasoning Math +1

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