6 citations · 12 across the 20 of their papers we have counts for
22 papers
Do Factuality Methods Make LLMs More Factual? Reproducibility and Fair Comparison
Pratuat Amatya, Vinay Setty, V Venktesh +1
Large Language Models (LLMs) are aligned to human preferences, after pre-training and supervised fine-tuning steps. However, standard RL alignment steps such as RLHF do not focus o…
Data Efficient Sample Selection for In-Context Learning
V Venktesh, Cem levi, Cem Levi +1
The In-context learning (ICL) paradigm aids large language models (LLMs) to adapt to new tasks without need for fine-tuning. However, selecting an optimal combination of demonstrat…
When More Reformulations Hurt: Avoiding Drift using Ranker Feedback
V Venktesh, Mandeep Rathee, Avishek Anand
Modern retrieval pipelines increasingly rely on query reformulation and neural reranking to improve effectiveness, but this comes at a significant computational cost and introduces…
LiveFC: A System for Live Fact-Checking of Audio Streams
Venktesh V, Vinay Setty
The advances in the digital era have led to rapid dissemination of information. This has also aggravated the spread of misinformation and disinformation. This has potentially serio…
Understanding the User: An Intent-Based Ranking Dataset
Abhijit Anand, Jurek Leonhardt, V Venktesh +1
As information retrieval systems continue to evolve, accurate evaluation and benchmarking of these systems become pivotal. Web search datasets, such as MS MARCO, primarily provide…
DEXTER: A Benchmark for open-domain Complex Question Answering using LLMs
Venktesh V. Deepali Prabhu, Avishek Anand
Open-domain complex Question Answering (QA) is a difficult task with challenges in evidence retrieval and reasoning. The complexity of such questions could stem from questions bein…
QuanTemp: A real-world open-domain benchmark for fact-checking numerical claims
Venktesh V, Abhijit Anand, Avishek Anand +1
Automated fact checking has gained immense interest to tackle the growing misinformation in the digital era. Existing systems primarily focus on synthetic claims on Wikipedia, and…
In-Context Ability Transfer for Question Decomposition in Complex QA
Venktesh V, Sourangshu Bhattacharya, Avishek Anand
Answering complex questions is a challenging task that requires question decomposition and multistep reasoning for arriving at the solution. While existing supervised and unsupervi…
Context Aware Query Rewriting for Text Rankers using LLM
Abhijit Anand, Venktesh V, Vinay Setty +1
Query rewriting refers to an established family of approaches that are applied to underspecified and ambiguous queries to overcome the vocabulary mismatch problem in document ranki…
MWPRanker: An Expression Similarity Based Math Word Problem Retriever
Mayank Goel, Venktesh V, Vikram Goyal
Math Word Problems (MWPs) in online assessments help test the ability of the learner to make critical inferences by interpreting the linguistic information in them. To test the mat…
Query Understanding in the Age of Large Language Models
Avishek Anand, Venktesh V, Abhijit Anand +1
Querying, conversing, and controlling search and information-seeking interfaces using natural language are fast becoming ubiquitous with the rise and adoption of large-language mod…
Enhancing Programming eTextbooks with ChatGPT Generated Counterfactual-Thinking-Inspired Questions
Arun Balajiee Lekshmi Narayanan, Rully Agus Hendrawan, Venktesh V
Digital textbooks have become an integral part of everyday learning tasks. In this work, we consider the use of digital textbooks for programming classes. Generally, students strug…
Coherence and Diversity through Noise: Self-Supervised Paraphrase Generation via Structure-Aware Denoising
Rishabh Gupta, Venktesh V., Mukesh Mohania +1
In this paper, we propose SCANING, an unsupervised framework for paraphrasing via controlled noise injection. We focus on the novel task of paraphrasing algebraic word problems hav…
Unsupervised Question Duplicate and Related Questions Detection in e-learning platforms
Maksimjeet Chowdhary, Sanyam Goyal, Venktesh V +2
Online learning platforms provide diverse questions to gauge the learners' understanding of different concepts. The repository of questions has to be constantly updated to ensure a…
Auxiliary Task Guided Interactive Attention Model for Question Difficulty Prediction
Venktesh V, Md. Shad Akhtar, Mukesh Mohania +1
Online learning platforms conduct exams to evaluate the learners in a monotonous way, where the questions in the database may be classified into Bloom's Taxonomy as varying levels…
'John ate 5 apples' != 'John ate some apples': Self-Supervised Paraphrase Quality Detection for Algebraic Word Problems
Rishabh Gupta, Venktesh V, Mukesh Mohania +1
This paper introduces the novel task of scoring paraphrases for Algebraic Word Problems (AWP) and presents a self-supervised method for doing so. In the current online pedagogical…
Obj2Sub: Unsupervised Conversion of Objective to Subjective Questions
Aarish Chhabra, Nandini Bansal, Venktesh V +2
Exams are conducted to test the learner's understanding of the subject. To prevent the learners from guessing or exchanging solutions, the mode of tests administered must have suff…
K-12BERT: BERT for K-12 education
Vasu Goel, Dhruv Sahnan, Venktesh V +3
Online education platforms are powered by various NLP pipelines, which utilize models like BERT to aid in content curation. Since the inception of the pre-trained language models l…
Topic Aware Contextualized Embeddings for High Quality Phrase Extraction
Venktesh V, Mukesh Mohania, Vikram Goyal
Keyphrase extraction from a given document is the task of automatically extracting salient phrases that best describe the document. This paper proposes a novel unsupervised graph-b…
TagRec: Automated Tagging of Questions with Hierarchical Learning Taxonomy
Venktesh V, Mukesh Mohania, Vikram Goyal
Online educational platforms organize academic questions based on a hierarchical learning taxonomy (subject-chapter-topic). Automatically tagging new questions with existing taxono…
Fake News Detection System using XLNet model with Topic Distributions: CONSTRAINT@AAAI2021 Shared Task
Akansha Gautam, Venktesh V, Sarah Masud
With the ease of access to information, and its rapid dissemination over the internet (both velocity and volume), it has become challenging to filter out truthful information from…
OneStopTuner: An End to End Architecture for JVM Tuning of Spark Applications
Venktesh V, Pooja B Bindal, Devesh Singhal +2
Java is the backbone of widely used big data frameworks, such as Apache Spark, due to its productivity, portability from JVM-based execution, and support for a rich set of librarie…