45 citations · 127 across the 24 of their papers we have counts for
27 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…
The CLEF-2026 CheckThat! Lab: Advancing Multilingual Fact-Checking
Julia Maria Struß, Sebastian Schellhammer, Stefan Dietze +9
The CheckThat! lab aims to advance the development of innovative technologies combating disinformation and manipulation efforts in online communication across a multitude of langua…
A Benchmark for Open-Domain Numerical Fact-Checking Enhanced by Claim Decomposition
V Venktesh, Deepali Prabhu, Avishek Anand
Fact-checking numerical claims is critical as the presence of numbers provide mirage of veracity despite being fake potentially causing catastrophic impacts on society. The prior w…
Think Right, Not More: Test-Time Scaling for Numerical Claim Verification
Primakov Chungkham, V Venktesh, Vinay Setty +1
Fact-checking real-world claims, particularly numerical claims, is inherently complex that require multistep reasoning and numerical reasoning for verifying diverse aspects of the…
Sample Efficient Demonstration Selection for In-Context Learning
Kiran Purohit, V Venktesh, Sourangshu Bhattacharya +1
The in-context learning paradigm with LLMs has been instrumental in advancing a wide range of natural language processing tasks. The selection of few-shot examples (exemplars / dem…
FlashCheck: Exploration of Efficient Evidence Retrieval for Fast Fact-Checking
Kevin Nanekhan, Venktesh V, Erik Martin +3
The advances in digital tools have led to the rampant spread of misinformation. While fact-checking aims to combat this, manual fact-checking is cumbersome and not scalable. It is…
DISCO: DISCovering Overfittings as Causal Rules for Text Classification Models
Zijian Zhang, Vinay Setty, Yumeng Wang +1
With the rapid advancement of neural language models, the deployment of over-parameterized models has surged, increasing the need for interpretable explanations comprehensible to h…
EXPLORA: Efficient Exemplar Subset Selection for Complex Reasoning
Kiran Purohit, Venktesh V, Raghuram Devalla +3
Answering reasoning-based complex questions over text and hybrid sources, including tables, is a challenging task. Recent advances in large language models (LLMs) have enabled in-c…
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…
Local Feature Selection without Label or Feature Leakage for Interpretable Machine Learning Predictions
Harrie Oosterhuis, Lijun Lyu, Avishek Anand
Local feature selection in machine learning provides instance-specific explanations by focusing on the most relevant features for each prediction, enhancing the interpretability of…
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…
Is Interpretable Machine Learning Effective at Feature Selection for Neural Learning-to-Rank?
Lijun Lyu, Nirmal Roy, Harrie Oosterhuis +1
Neural ranking models have become increasingly popular for real-world search and recommendation systems in recent years. Unlike their tree-based counterparts, neural models are muc…
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…
The Surprising Effectiveness of Rankers Trained on Expanded Queries
Abhijit Anand, Venktesh V, Vinay Setty +1
An important problem in text-ranking systems is handling the hard queries that form the tail end of the query distribution. The difficulty may arise due to the presence of uncommon…
Temporal Blind Spots in Large Language Models
Jonas Wallat, Adam Jatowt, Avishek Anand
Large language models (LLMs) have recently gained significant attention due to their unparalleled ability to perform various natural language processing tasks. These models, benefi…
Data Augmentation for Sample Efficient and Robust Document Ranking
Abhijit Anand, Jurek Leonhardt, Jaspreet Singh +2
Contextual ranking models have delivered impressive performance improvements over classical models in the document ranking task. However, these highly over-parameterized models ten…
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…
DINE: Dimensional Interpretability of Node Embeddings
Simone Piaggesi, Megha Khosla, André Panisson +1
Graphs are ubiquitous due to their flexibility in representing social and technological systems as networks of interacting elements. Graph representation learning methods, such as…
Explainable Information Retrieval: A Survey
Avishek Anand, Lijun Lyu, Maximilian Idahl +3
Explainable information retrieval is an emerging research area aiming to make transparent and trustworthy information retrieval systems. Given the increasing use of complex machine…
SparCAssist: A Model Risk Assessment Assistant Based on Sparse Generated Counterfactuals
Zijian Zhang, Vinay Setty, Avishek Anand
We introduce SparcAssist, a general-purpose risk assessment tool for the machine learning models trained for language tasks. It evaluates models' risk by inspecting their behavior…
FaxPlainAC: A Fact-Checking Tool Based on EXPLAINable Models with HumAn Correction in the Loop
Zijian Zhang, Koustav Rudra, Avishek Anand
Fact-checking on the Web has become the main mechanism through which we detect the credibility of the news or information. Existing fact-checkers verify the authenticity of the inf…
Towards Axiomatic Explanations for Neural Ranking Models
Michael Völske, Alexander Bondarenko, Maik Fröbe +4
Recently, neural networks have been successfully employed to improve upon state-of-the-art performance in ad-hoc retrieval tasks via machine-learned ranking functions. While neural…
Towards Benchmarking the Utility of Explanations for Model Debugging
Maximilian Idahl, Lijun Lyu, Ujwal Gadiraju +1
Post-hoc explanation methods are an important class of approaches that help understand the rationale underlying a trained model's decision. But how useful are they for an end-user…